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Vakili Ojarood M, Farzan R, Mohsenizadeh SM, Torabi H, Yaghoubi T. Deep Learning during burn prehospital care: An evolving perspective. Burns 2024; 50:1349-1351. [PMID: 38582694 DOI: 10.1016/j.burns.2024.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 03/08/2024] [Indexed: 04/08/2024]
Affiliation(s)
| | - Ramyar Farzan
- Department of Plastic & Reconstructive Surgery, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran
| | - Seyed Mostafa Mohsenizadeh
- Department of Nursing, Qaen School of Nursing and Midwifery, Birjand University of Medical Sciences, Birjand, Iran
| | - Hossein Torabi
- Department of General Surgery, Poursina Medical and Educational Center, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran.
| | - Tahereh Yaghoubi
- Traditional and Complementary Medicine Research Center, Addiction Institute, Mazandaran University of Medical Sciences, Sari, Iran.
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2
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Qin Y, Qin X, Zhang J, Guo X. Artificial intelligence: The future for multimodality imaging of right ventricle. Int J Cardiol 2024; 404:131970. [PMID: 38490268 DOI: 10.1016/j.ijcard.2024.131970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 03/05/2024] [Accepted: 03/12/2024] [Indexed: 03/17/2024]
Abstract
The crucial pathophysiological and prognostic roles of the right ventricle in various diseases have been well-established. Nonetheless, conventional cardiovascular imaging modalities are frequently associated with intrinsic limitations when evaluating right ventricular (RV) morphology and function. The integration of artificial intelligence (AI) in multimodality imaging presents a promising avenue to circumvent these obstacles, paving the way for future fully automated imaging paradigms. This review aimed to address the current challenges faced by clinicians and researchers in integrating RV imaging and AI technology, to provide a comprehensive overview of the current applications of AI in RV imaging, and to offer insights into future directions, opportunities, and potential challenges in this rapidly advancing field.
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Affiliation(s)
- Yuhan Qin
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Xiaohan Qin
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Jing Zhang
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Xiaoxiao Guo
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
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Seetharam K, Thyagaturu H, Ferreira GL, Patel A, Patel C, Elahi A, Pachulski R, Shah J, Mir P, Thodimela A, Pala M, Thet Z, Hamirani Y. Broadening Perspectives of Artificial Intelligence in Echocardiography. Cardiol Ther 2024; 13:267-279. [PMID: 38703292 PMCID: PMC11093957 DOI: 10.1007/s40119-024-00368-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 04/11/2024] [Indexed: 05/06/2024] Open
Abstract
Echocardiography frequently serves as the first-line treatment of diagnostic imaging for several pathological entities in cardiology. Artificial intelligence (AI) has been growing substantially in information technology and various commercial industries. Machine learning (ML), a branch of AI, has been shown to expand the capabilities and potential of echocardiography. ML algorithms expand the field of echocardiography by automated assessment of the ejection fraction and left ventricular function, integrating novel approaches such as speckle tracking or tissue Doppler echocardiography or vector flow mapping, improved phenotyping, distinguishing between cardiac conditions, and incorporating information from mobile health and genomics. In this review article, we assess the impact of AI and ML in echocardiography.
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Affiliation(s)
- Karthik Seetharam
- Division of Cardiovascular Disease, West Virgina University, Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA.
- Wyckoff Heights Medical Center, Brooklyn, NY, USA.
| | - Harshith Thyagaturu
- Division of Cardiovascular Disease, West Virgina University, Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA
| | | | - Aditya Patel
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Chinmay Patel
- University of Pittsburg Medical Center, Harrisburg, PA, USA
| | - Asim Elahi
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Roman Pachulski
- St. John's Episcopal Hospital - South Shore, New York, NY, USA
| | - Jilan Shah
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Parvez Mir
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | | | - Manya Pala
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Zeyar Thet
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Yasmin Hamirani
- Robert Woods Johnson University Hospital/Rutgers University, New Brusnwick, NJ, USA
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4
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Parsa S, Somani S, Dudum R, Jain SS, Rodriguez F. Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time? Curr Atheroscler Rep 2024:10.1007/s11883-024-01210-w. [PMID: 38780665 DOI: 10.1007/s11883-024-01210-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/08/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE OF REVIEW This review evaluates how Artificial Intelligence (AI) enhances atherosclerotic cardiovascular disease (ASCVD) risk assessment, allows for opportunistic screening, and improves adherence to guidelines through the analysis of unstructured clinical data and patient-generated data. Additionally, it discusses strategies for integrating AI into clinical practice in preventive cardiology. RECENT FINDINGS AI models have shown superior performance in personalized ASCVD risk evaluations compared to traditional risk scores. These models now support automated detection of ASCVD risk markers, including coronary artery calcium (CAC), across various imaging modalities such as dedicated ECG-gated CT scans, chest X-rays, mammograms, coronary angiography, and non-gated chest CT scans. Moreover, large language model (LLM) pipelines are effective in identifying and addressing gaps and disparities in ASCVD preventive care, and can also enhance patient education. AI applications are proving invaluable in preventing and managing ASCVD and are primed for clinical use, provided they are implemented within well-regulated, iterative clinical pathways.
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Affiliation(s)
- Shyon Parsa
- Department of Medicine, Stanford University, Stanford, California, USA
| | - Sulaiman Somani
- Department of Medicine, Stanford University, Stanford, California, USA
| | - Ramzi Dudum
- Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Sneha S Jain
- Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Fatima Rodriguez
- Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA.
- Center for Digital Health, Stanford University, Stanford, California, USA.
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5
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Temsah MH, Alhuzaimi AN, Almansour M, Aljamaan F, Alhasan K, Batarfi MA, Altamimi I, Alharbi A, Alsuhaibani AA, Alwakeel L, Alzahrani AA, Alsulaim KB, Jamal A, Khayat A, Alghamdi MH, Halwani R, Khan MK, Al-Eyadhy A, Nazer R. Art or Artifact: Evaluating the Accuracy, Appeal, and Educational Value of AI-Generated Imagery in DALL·E 3 for Illustrating Congenital Heart Diseases. J Med Syst 2024; 48:54. [PMID: 38780839 DOI: 10.1007/s10916-024-02072-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 04/30/2024] [Indexed: 05/25/2024]
Abstract
Artificial Intelligence (AI), particularly AI-Generated Imagery, has the potential to impact medical and patient education. This research explores the use of AI-generated imagery, from text-to-images, in medical education, focusing on congenital heart diseases (CHD). Utilizing ChatGPT's DALL·E 3, the research aims to assess the accuracy and educational value of AI-created images for 20 common CHDs. In this study, we utilized DALL·E 3 to generate a comprehensive set of 110 images, comprising ten images depicting the normal human heart and five images for each of the 20 common CHDs. The generated images were evaluated by a diverse group of 33 healthcare professionals. This cohort included cardiology experts, pediatricians, non-pediatric faculty members, trainees (medical students, interns, pediatric residents), and pediatric nurses. Utilizing a structured framework, these professionals assessed each image for anatomical accuracy, the usefulness of in-picture text, its appeal to medical professionals, and the image's potential applicability in medical presentations. Each item was assessed on a Likert scale of three. The assessments produced a total of 3630 images' assessments. Most AI-generated cardiac images were rated poorly as follows: 80.8% of images were rated as anatomically incorrect or fabricated, 85.2% rated to have incorrect text labels, 78.1% rated as not usable for medical education. The nurses and medical interns were found to have a more positive perception about the AI-generated cardiac images compared to the faculty members, pediatricians, and cardiology experts. Complex congenital anomalies were found to be significantly more predicted to anatomical fabrication compared to simple cardiac anomalies. There were significant challenges identified in image generation. Based on our findings, we recommend a vigilant approach towards the use of AI-generated imagery in medical education at present, underscoring the imperative for thorough validation and the importance of collaboration across disciplines. While we advise against its immediate integration until further validations are conducted, the study advocates for future AI-models to be fine-tuned with accurate medical data, enhancing their reliability and educational utility.
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Affiliation(s)
- Mohamad-Hani Temsah
- College of Medicine, King Saud University, Riyadh, Saudi Arabia.
- Pediatric Department, King Saud University Medical City, King Saud University, Riyadh, Saudi Arabia.
- Evidence-Based Health Care & Knowledge Translation Research Chair, Family & Community Medicine Department, College of Medicine, King Saud University, 11362, Riyadh, Saudi Arabia.
| | - Abdullah N Alhuzaimi
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Division of Pediatric Cardiology, Cardiac Science Department, College of Medicine, King Saud University Medical City, 11362, Riyadh, Saudi Arabia
| | - Mohammed Almansour
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Department of Medical Education, College of Medicine, King Saud University, Riyadh, Saudi Arabia
| | - Fadi Aljamaan
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Critical Care Department, King Saud University Medical City, Riyadh, Saudi Arabia
| | - Khalid Alhasan
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Pediatric Department, King Saud University Medical City, King Saud University, Riyadh, Saudi Arabia
- Kidney & Pancreas Health Center, Organ Transplant Center of Excellence, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
| | - Munirah A Batarfi
- Basic Medical Sciences, College of Medicine King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
| | | | - Amani Alharbi
- Pediatric Department, King Saud University Medical City, King Saud University, Riyadh, Saudi Arabia
| | | | - Leena Alwakeel
- Pediatric Department, King Saud University Medical City, King Saud University, Riyadh, Saudi Arabia
| | | | | | - Amr Jamal
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Evidence-Based Health Care & Knowledge Translation Research Chair, Family & Community Medicine Department, College of Medicine, King Saud University, 11362, Riyadh, Saudi Arabia
- Department of Family and Community Medicine, King Saud University Medical City, 11362, Riyadh, Saudi Arabia
| | - Afnan Khayat
- Health Information Management Department, Prince Sultan Military College of Health Sciences, Al Dhahran, Saudi Arabia
| | - Mohammed Hussien Alghamdi
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Division of Pediatric Cardiology, Cardiac Science Department, College of Medicine, King Saud University Medical City, 11362, Riyadh, Saudi Arabia
- Department of Medical Education, College of Medicine, King Saud University, Riyadh, Saudi Arabia
| | - Rabih Halwani
- Department of Clinical Sciences, College of Medicine, University of Sharjah, 27272, Sharjah, United Arab Emirates
- Research Institute for Medical and Health Sciences, University of Sharjah, 27272, Sharjah, United Arab Emirates
| | - Muhammad Khurram Khan
- Center of Excellence in Information Assurance, King Saud University, 11653, Riyadh, Saudi Arabia
| | - Ayman Al-Eyadhy
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Pediatric Department, King Saud University Medical City, King Saud University, Riyadh, Saudi Arabia
| | - Rakan Nazer
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Department of Cardiac Science, King Fahad Cardiac Center, College of Medicine, King Saud University, Riyadh, Saudi Arabia
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Iorga RE, Costin D, Munteanu-Dănulescu RS, Rezuș E, Moraru AD. Non-Invasive Retinal Vessel Analysis as a Predictor for Cardiovascular Disease. J Pers Med 2024; 14:501. [PMID: 38793083 PMCID: PMC11122007 DOI: 10.3390/jpm14050501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 05/06/2024] [Accepted: 05/08/2024] [Indexed: 05/26/2024] Open
Abstract
Cardiovascular disease (CVD) is the most frequent cause of death worldwide. The alterations in the microcirculation may predict the cardiovascular mortality. The retinal vasculature can be used as a model to study vascular alterations associated with cardiovascular disease. In order to quantify microvascular changes in a non-invasive way, fundus images can be taken and analysed. The central retinal arteriolar (CRAE), the venular (CRVE) diameter and the arteriolar-to-venular diameter ratio (AVR) can be used as biomarkers to predict the cardiovascular mortality. A narrower CRAE, wider CRVE and a lower AVR have been associated with increased cardiovascular events. Dynamic retinal vessel analysis (DRVA) allows the quantification of retinal changes using digital image sequences in response to visual stimulation with flicker light. This article is not just a review of the current literature, it also aims to discuss the methodological benefits and to identify research gaps. It highlights the potential use of microvascular biomarkers for screening and treatment monitoring of cardiovascular disease. Artificial intelligence (AI), such as Quantitative Analysis of Retinal vessel Topology and size (QUARTZ), and SIVA-deep learning system (SIVA-DLS), seems efficient in extracting information from fundus photographs and has the advantage of increasing diagnosis accuracy and improving patient care by complementing the role of physicians. Retinal vascular imaging using AI may help identify the cardiovascular risk, and is an important tool in primary cardiovascular disease prevention. Further research should explore the potential clinical application of retinal microvascular biomarkers, in order to assess systemic vascular health status, and to predict cardiovascular events.
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Affiliation(s)
- Raluca Eugenia Iorga
- Department of Surgery II, Discipline of Ophthalmology, “Grigore T. Popa” University of Medicine and Pharmacy, Strada Universitatii No. 16, 700115 Iași, Romania; (R.E.I.); (A.D.M.)
| | - Damiana Costin
- Doctoral School, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iași, Romania
| | | | - Elena Rezuș
- Department of Internal Medicine II, Discipline of Reumathology, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iași, Romania;
| | - Andreea Dana Moraru
- Department of Surgery II, Discipline of Ophthalmology, “Grigore T. Popa” University of Medicine and Pharmacy, Strada Universitatii No. 16, 700115 Iași, Romania; (R.E.I.); (A.D.M.)
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Rhee TM, Ko YK, Kim HK, Lee SB, Kim BS, Choi HM, Hwang IC, Park JB, Yoon YE, Kim YJ, Cho GY. Machine Learning-Based Discrimination of Cardiovascular Outcomes in Patients With Hypertrophic Cardiomyopathy. JACC. ASIA 2024; 4:375-386. [PMID: 38765660 PMCID: PMC11099823 DOI: 10.1016/j.jacasi.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 11/07/2023] [Accepted: 12/04/2023] [Indexed: 05/22/2024]
Abstract
Background Current risk stratification strategies for patients with hypertrophic cardiomyopathy (HCM) are limited to traditional methodologies. Objectives The authors aimed to establish machine learning (ML)-based models to discriminate major cardiovascular events in patients with HCM. Methods We enrolled consecutive HCM patients from 2 tertiary referral centers and used 25 clinical and echocardiographic features to discriminate major adverse cardiovascular events (MACE), including all-cause death, admission for heart failure (HF-adm), and stroke. The best model was selected for each outcome using the area under the receiver operating characteristic curve (AUROC) with 20-fold cross-validation. After testing in the external validation cohort, the relative importance of features in discriminating each outcome was determined using the SHapley Additive exPlanations (SHAP) method. Results In total, 2,111 patients with HCM (age 61.4 ± 13.6 years; 67.6% men) were analyzed. During the median 4.0 years of follow-up, MACE occurred in 341 patients (16.2%). Among the 4 ML models, the logistic regression model achieved the best AUROC of 0.800 (95% CI: 0.760-0.841) for MACE, 0.789 (95% CI: 0.736-0.841) for all-cause death, 0.798 (95% CI: 0.736-0.860) for HF-adm, and 0.807 (95% CI: 0.754-0.859) for stroke. The discriminant ability of the logistic regression model remained excellent when applied to the external validation cohort for MACE (AUROC = 0.768), all-cause death (AUROC = 0.750), and HF-adm (AUROC = 0.806). The SHAP analysis identified left atrial diameter and hypertension as important variables for all outcomes of interest. Conclusions The proposed ML models incorporating various phenotypes from patients with HCM accurately discriminated adverse cardiovascular events and provided variables with high importance for each outcome.
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Affiliation(s)
- Tae-Min Rhee
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea
| | - Yeon-Kyoung Ko
- Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul, Republic of Korea
- Department of Medical Informatics, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Hyung-Kwan Kim
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Seung-Bo Lee
- Department of Medical Informatics, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Bong-Seong Kim
- Department of Statistics and Actuarial Science, The Soongsil University, Seoul, Republic of Korea
| | - Hong-Mi Choi
- Cardiovascular Center and Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - In-Chang Hwang
- Cardiovascular Center and Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Jun-Bean Park
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Yeonyee E. Yoon
- Cardiovascular Center and Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Yong-Jin Kim
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Goo-Yeong Cho
- Cardiovascular Center and Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
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8
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Zhang J, Xiao S, Zhu Y, Zhang Z, Cao H, Xie M, Zhang L. Advances in the Application of Artificial Intelligence in Fetal Echocardiography. J Am Soc Echocardiogr 2024; 37:550-561. [PMID: 38199332 DOI: 10.1016/j.echo.2023.12.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 12/23/2023] [Accepted: 12/23/2023] [Indexed: 01/12/2024]
Abstract
Congenital heart disease is a severe health risk for newborns. Early detection of abnormalities in fetal cardiac structure and function during pregnancy can help patients seek timely diagnostic and therapeutic advice, and early intervention planning can significantly improve fetal survival rates. Echocardiography is one of the most accessible and widely used diagnostic tools in the diagnosis of fetal congenital heart disease. However, traditional fetal echocardiography has limitations due to fetal, maternal, and ultrasound equipment factors and is highly dependent on the skill level of the operator. Artificial intelligence (AI) technology, with its rapid development utilizing advanced computer algorithms, has great potential to empower sonographers in time-saving and accurate diagnosis and to bridge the skill gap in different regions. In recent years, AI-assisted fetal echocardiography has been successfully applied to a wide range of ultrasound diagnoses. This review systematically reviews the applications of AI in the field of fetal echocardiography over the years in terms of image processing, biometrics, and disease diagnosis and provides an outlook for future research.
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Affiliation(s)
- Junmin Zhang
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Clinical Research Center for Medical Imaging, Hubei Province, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Sushan Xiao
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Clinical Research Center for Medical Imaging, Hubei Province, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Ye Zhu
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Clinical Research Center for Medical Imaging, Hubei Province, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Zisang Zhang
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Clinical Research Center for Medical Imaging, Hubei Province, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Haiyan Cao
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Clinical Research Center for Medical Imaging, Hubei Province, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Mingxing Xie
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Clinical Research Center for Medical Imaging, Hubei Province, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Li Zhang
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Clinical Research Center for Medical Imaging, Hubei Province, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China.
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9
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Sigfstead S, Jiang R, Avram R, Davies B, Krahn AD, Cheung CC. Applying Artificial Intelligence for Phenotyping of Inherited Arrhythmia Syndromes. Can J Cardiol 2024:S0828-282X(24)00335-0. [PMID: 38670456 DOI: 10.1016/j.cjca.2024.04.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 04/08/2024] [Accepted: 04/21/2024] [Indexed: 04/28/2024] Open
Abstract
Inherited arrhythmia disorders account for a significant proportion of sudden cardiac death, particularly among young individuals. Recent advances in our understanding of these syndromes have improved patient diagnosis and care, yet certain clinical gaps remain, particularly within case ascertainment, access to genetic testing and risk stratification. Artificial intelligence (AI), specifically machine learning and its subset deep learning, present promising solutions to these challenges. The capacity of AI to process vast amounts of patient data and identify disease patterns differentiates them from traditional methods, which are time and resource intensive. To date, AI models have demonstrated immense potential in condition detection (including asymptomatic/concealed disease) and genotype and phenotype identification, exceeding expert cardiologists in these tasks. Additionally, they have exhibited applicability for general population screening, improving case ascertainment in a set of conditions that are often asymptomatic such as left ventricular dysfunction. Third, models have displayed ability to improve testing protocols, as through model identification of disease and genotype, specific clinical testing (e.g. drug challenges or further diagnostic imaging) can be avoided, reducing health care expenses, speeding diagnosis, and possibly allowing for more incremental or targeted genetic testing approaches. These significant benefits warrant continued investigation of the field, particularly regarding the development and implementation of clinically applicable screening tools. This review summarizes key developments in the field, including studies in Long QT Syndrome, Brugada Syndrome, Hypertrophic Cardiomyopathy, and Arrhythmogenic Cardiomyopathies, and provides direction for effective future AI implementation in clinical practice.
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Affiliation(s)
- Sophie Sigfstead
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB
| | - River Jiang
- Division of Cardiology, University of British Columbia, Vancouver, BC
| | - Robert Avram
- Heartwise (heartwise.ai), Montreal Heart Institute, Montreal, QC; Department of Medicine, Montreal Heart Institute, Université de Montréal, Montreal, QC
| | - Brianna Davies
- Center for Cardiovascular Innovation, Division of Cardiology, Department of Medicine, University of British Columbia, Vancouver, BC
| | - Andrew D Krahn
- Center for Cardiovascular Innovation, Division of Cardiology, Department of Medicine, University of British Columbia, Vancouver, BC.
| | - Christopher C Cheung
- Division of Cardiology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON
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10
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Salehi MA, Harandi H, Mohammadi S, Shahrabi Farahani M, Shojaei S, Saleh RR. Diagnostic Performance of Artificial Intelligence in Detection of Hepatocellular Carcinoma: A Meta-analysis. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01058-1. [PMID: 38438694 DOI: 10.1007/s10278-024-01058-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 02/18/2024] [Accepted: 02/19/2024] [Indexed: 03/06/2024]
Abstract
Due to the increasing interest in the use of artificial intelligence (AI) algorithms in hepatocellular carcinoma detection, we performed a systematic review and meta-analysis to pool the data on diagnostic performance metrics of AI and to compare them with clinicians' performance. A search in PubMed and Scopus was performed in January 2024 to find studies that evaluated and/or validated an AI algorithm for the detection of HCC. We performed a meta-analysis to pool the data on the metrics of diagnostic performance. Subgroup analysis based on the modality of imaging and meta-regression based on multiple parameters were performed to find potential sources of heterogeneity. The risk of bias was assessed using Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction Model Study Risk of Bias Assessment Tool (PROBAST) reporting guidelines. Out of 3177 studies screened, 44 eligible studies were included. The pooled sensitivity and specificity for internally validated AI algorithms were 84% (95% CI: 81,87) and 92% (95% CI: 90,94), respectively. Externally validated AI algorithms had a pooled sensitivity of 85% (95% CI: 78,89) and specificity of 84% (95% CI: 72,91). When clinicians were internally validated, their pooled sensitivity was 70% (95% CI: 60,78), while their pooled specificity was 85% (95% CI: 77,90). This study implies that AI can perform as a diagnostic supplement for clinicians and radiologists by screening images and highlighting regions of interest, thus improving workflow.
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Affiliation(s)
| | - Hamid Harandi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Antibiotic Stewardship and Antimicrobial Resistance, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Soheil Mohammadi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
| | | | - Shayan Shojaei
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Ramy R Saleh
- Department of Oncology, McGill University, Montreal, QC, H3A 0G4, Canada
- Division of Medical Oncology, McGill University Health Centre, Montreal, QC, H4A 3J1, Canada
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11
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Ding CCA, Dokos S, Bakir AA, Zamberi NJ, Liew YM, Chan BT, Md Sari NA, Avolio A, Lim E. Simulating impaired left ventricular-arterial coupling in aging and disease: a systematic review. Biomed Eng Online 2024; 23:24. [PMID: 38388416 PMCID: PMC10885508 DOI: 10.1186/s12938-024-01206-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 01/11/2024] [Indexed: 02/24/2024] Open
Abstract
Aortic stenosis, hypertension, and left ventricular hypertrophy often coexist in the elderly, causing a detrimental mismatch in coupling between the heart and vasculature known as ventricular-vascular (VA) coupling. Impaired left VA coupling, a critical aspect of cardiovascular dysfunction in aging and disease, poses significant challenges for optimal cardiovascular performance. This systematic review aims to assess the impact of simulating and studying this coupling through computational models. By conducting a comprehensive analysis of 34 relevant articles obtained from esteemed databases such as Web of Science, Scopus, and PubMed until July 14, 2022, we explore various modeling techniques and simulation approaches employed to unravel the complex mechanisms underlying this impairment. Our review highlights the essential role of computational models in providing detailed insights beyond clinical observations, enabling a deeper understanding of the cardiovascular system. By elucidating the existing models of the heart (3D, 2D, and 0D), cardiac valves, and blood vessels (3D, 1D, and 0D), as well as discussing mechanical boundary conditions, model parameterization and validation, coupling approaches, computer resources and diverse applications, we establish a comprehensive overview of the field. The descriptions as well as the pros and cons on the choices of different dimensionality in heart, valve, and circulation are provided. Crucially, we emphasize the significance of evaluating heart-vessel interaction in pathological conditions and propose future research directions, such as the development of fully coupled personalized multidimensional models, integration of deep learning techniques, and comprehensive assessment of confounding effects on biomarkers.
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Affiliation(s)
- Corina Cheng Ai Ding
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia
- Graduate School of Biomedical Engineering, Faculty of Engineering, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Socrates Dokos
- Graduate School of Biomedical Engineering, Faculty of Engineering, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Azam Ahmad Bakir
- University of Southampton Malaysia Campus, 79200, Iskandar Puteri, Johor, Malaysia
| | - Nurul Jannah Zamberi
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia
- Graduate School of Biomedical Engineering, Faculty of Engineering, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Yih Miin Liew
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Bee Ting Chan
- Department of Mechanical, Materials and Manufacturing Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, 43500, Selangor, Malaysia
| | - Nor Ashikin Md Sari
- Department of Medicine, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Alberto Avolio
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, 2109, Australia
| | - Einly Lim
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia.
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12
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Li F, Rasmy L, Xiang Y, Feng J, Abdelhameed A, Hu X, Sun Z, Aguilar D, Dhoble A, Du J, Wang Q, Niu S, Dang Y, Zhang X, Xie Z, Nian Y, He J, Zhou Y, Li J, Prosperi M, Bian J, Zhi D, Tao C. Dynamic Prognosis Prediction for Patients on DAPT After Drug-Eluting Stent Implantation: Model Development and Validation. J Am Heart Assoc 2024; 13:e029900. [PMID: 38293921 PMCID: PMC11056175 DOI: 10.1161/jaha.123.029900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 12/01/2023] [Indexed: 02/01/2024]
Abstract
BACKGROUND The rapid evolution of artificial intelligence (AI) in conjunction with recent updates in dual antiplatelet therapy (DAPT) management guidelines emphasizes the necessity for innovative models to predict ischemic or bleeding events after drug-eluting stent implantation. Leveraging AI for dynamic prediction has the potential to revolutionize risk stratification and provide personalized decision support for DAPT management. METHODS AND RESULTS We developed and validated a new AI-based pipeline using retrospective data of drug-eluting stent-treated patients, sourced from the Cerner Health Facts data set (n=98 236) and Optum's de-identified Clinformatics Data Mart Database (n=9978). The 36 months following drug-eluting stent implantation were designated as our primary forecasting interval, further segmented into 6 sequential prediction windows. We evaluated 5 distinct AI algorithms for their precision in predicting ischemic and bleeding risks. Model discriminative accuracy was assessed using the area under the receiver operating characteristic curve, among other metrics. The weighted light gradient boosting machine stood out as the preeminent model, thus earning its place as our AI-DAPT model. The AI-DAPT demonstrated peak accuracy in the 30 to 36 months window, charting an area under the receiver operating characteristic curve of 90% [95% CI, 88%-92%] for ischemia and 84% [95% CI, 82%-87%] for bleeding predictions. CONCLUSIONS Our AI-DAPT excels in formulating iterative, refined dynamic predictions by assimilating ongoing updates from patients' clinical profiles, holding value as a novel smart clinical tool to facilitate optimal DAPT duration management with high accuracy and adaptability.
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Affiliation(s)
- Fang Li
- McWilliams School of Biomedical InformaticsUniversity of Texas Health Science Center at HoustonHoustonTXUSA
- Department of Artificial Intelligence and InformaticsMayo ClinicJacksonvilleFLUSA
| | - Laila Rasmy
- McWilliams School of Biomedical InformaticsUniversity of Texas Health Science Center at HoustonHoustonTXUSA
| | - Yang Xiang
- Peng Cheng LaboratoryShenzhenGuangdongChina
| | - Jingna Feng
- McWilliams School of Biomedical InformaticsUniversity of Texas Health Science Center at HoustonHoustonTXUSA
- Department of Artificial Intelligence and InformaticsMayo ClinicJacksonvilleFLUSA
| | - Ahmed Abdelhameed
- McWilliams School of Biomedical InformaticsUniversity of Texas Health Science Center at HoustonHoustonTXUSA
- Department of Artificial Intelligence and InformaticsMayo ClinicJacksonvilleFLUSA
| | - Xinyue Hu
- McWilliams School of Biomedical InformaticsUniversity of Texas Health Science Center at HoustonHoustonTXUSA
- Department of Artificial Intelligence and InformaticsMayo ClinicJacksonvilleFLUSA
| | - Zenan Sun
- McWilliams School of Biomedical InformaticsUniversity of Texas Health Science Center at HoustonHoustonTXUSA
| | - David Aguilar
- Department of Internal Medicine, McGovern Medical SchoolUniversity of Texas Health Science Center at HoustonHoustonTXUSA
- LSU School of Medicine, LSU Health New OrleansNew OrleansLAUSA
| | - Abhijeet Dhoble
- Department of Internal Medicine, McGovern Medical SchoolUniversity of Texas Health Science Center at HoustonHoustonTXUSA
| | - Jingcheng Du
- McWilliams School of Biomedical InformaticsUniversity of Texas Health Science Center at HoustonHoustonTXUSA
| | - Qing Wang
- McWilliams School of Biomedical InformaticsUniversity of Texas Health Science Center at HoustonHoustonTXUSA
| | - Shuteng Niu
- McWilliams School of Biomedical InformaticsUniversity of Texas Health Science Center at HoustonHoustonTXUSA
| | - Yifang Dang
- McWilliams School of Biomedical InformaticsUniversity of Texas Health Science Center at HoustonHoustonTXUSA
| | - Xinyuan Zhang
- McWilliams School of Biomedical InformaticsUniversity of Texas Health Science Center at HoustonHoustonTXUSA
| | - Ziqian Xie
- McWilliams School of Biomedical InformaticsUniversity of Texas Health Science Center at HoustonHoustonTXUSA
| | - Yi Nian
- McWilliams School of Biomedical InformaticsUniversity of Texas Health Science Center at HoustonHoustonTXUSA
| | - JianPing He
- McWilliams School of Biomedical InformaticsUniversity of Texas Health Science Center at HoustonHoustonTXUSA
| | - Yujia Zhou
- McWilliams School of Biomedical InformaticsUniversity of Texas Health Science Center at HoustonHoustonTXUSA
| | - Jianfu Li
- McWilliams School of Biomedical InformaticsUniversity of Texas Health Science Center at HoustonHoustonTXUSA
- Department of Artificial Intelligence and InformaticsMayo ClinicJacksonvilleFLUSA
| | - Mattia Prosperi
- Data Intelligence Systems Lab, Department of Epidemiology, College of Public Health and Health Professions & College of MedicineUniversity of FloridaGainesvilleFLUSA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of MedicineUniversity of FloridaGainesvilleFLUSA
| | - Degui Zhi
- McWilliams School of Biomedical InformaticsUniversity of Texas Health Science Center at HoustonHoustonTXUSA
| | - Cui Tao
- McWilliams School of Biomedical InformaticsUniversity of Texas Health Science Center at HoustonHoustonTXUSA
- Department of Artificial Intelligence and InformaticsMayo ClinicJacksonvilleFLUSA
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13
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Alwan L, Benz DC, Cuddy SAM, Dobner S, Shiri I, Caobelli F, Bernhard B, Stämpfli SF, Eberli F, Reyes M, Kwong RY, Falk RH, Dorbala S, Gräni C. Current and Evolving Multimodality Cardiac Imaging in Managing Transthyretin Amyloid Cardiomyopathy. JACC Cardiovasc Imaging 2024; 17:195-211. [PMID: 38099914 DOI: 10.1016/j.jcmg.2023.10.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 10/06/2023] [Accepted: 10/18/2023] [Indexed: 01/29/2024]
Abstract
Amyloid transthyretin (ATTR) amyloidosis is a protein-misfolding disease characterized by fibril accumulation in the extracellular space that can result in local tissue disruption and organ dysfunction. Cardiac involvement drives morbidity and mortality, and the heart is the major organ affected by ATTR amyloidosis. Multimodality cardiac imaging (ie, echocardiography, scintigraphy, and cardiac magnetic resonance) allows accurate diagnosis of ATTR cardiomyopathy (ATTR-CM), and this is of particular importance because ATTR-targeting therapies have become available and probably exert their greatest benefit at earlier disease stages. Apart from establishing the diagnosis, multimodality cardiac imaging may help to better understand pathogenesis, predict prognosis, and monitor treatment response. The aim of this review is to give an update on contemporary and evolving cardiac imaging methods and their role in diagnosing and managing ATTR-CM. Further, an outlook is presented on how artificial intelligence in cardiac imaging may improve future clinical decision making and patient management in the setting of ATTR-CM.
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Affiliation(s)
- Louhai Alwan
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Dominik C Benz
- Amyloidosis Program, Division of Cardiology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA; Division of Nuclear Medicine, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA; CV Imaging Program, Cardiovascular Division, Department of Medicine and Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA; Cardiac Imaging, Department of Cardiology and Nuclear Medicine, Zurich University Hospital, Zurich, Switzerland
| | - Sarah A M Cuddy
- Amyloidosis Program, Division of Cardiology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA; Division of Nuclear Medicine, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA; CV Imaging Program, Cardiovascular Division, Department of Medicine and Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Stephan Dobner
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Isaac Shiri
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Federico Caobelli
- University Clinic of Nuclear Medicine, Inselspital, Bern University Hospital, Switzerland
| | - Benedikt Bernhard
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland; CV Imaging Program, Cardiovascular Division, Department of Medicine and Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Simon F Stämpfli
- Department of Cardiology, Heart Centre Lucerne, Luzerner Kantonsspital, Lucerne, Switzerland
| | - Franz Eberli
- Department of Cardiology, Triemli Hospital (Triemlispital), Zurich, Switzerland
| | - Mauricio Reyes
- Insel Data Science Center, Inselspital, Bern University Hospital, Bern, Switzerland; Artificial Intelligence in Medical Imaging, ARTORG Center for Biomedical Research, University of Bern, Bern, Switzerland
| | - Raymond Y Kwong
- CV Imaging Program, Cardiovascular Division, Department of Medicine and Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Rodney H Falk
- Amyloidosis Program, Division of Cardiology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Sharmila Dorbala
- Amyloidosis Program, Division of Cardiology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA; Division of Nuclear Medicine, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA; CV Imaging Program, Cardiovascular Division, Department of Medicine and Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Christoph Gräni
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
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14
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Terranova C, Cestonaro C, Fava L, Cinquetti A. AI and professional liability assessment in healthcare. A revolution in legal medicine? Front Med (Lausanne) 2024; 10:1337335. [PMID: 38259835 PMCID: PMC10800912 DOI: 10.3389/fmed.2023.1337335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024] Open
Abstract
The adoption of advanced artificial intelligence (AI) systems in healthcare is transforming the healthcare-delivery landscape. Artificial intelligence may enhance patient safety and improve healthcare outcomes, but it presents notable ethical and legal dilemmas. Moreover, as AI streamlines the analysis of the multitude of factors relevant to malpractice claims, including informed consent, adherence to standards of care, and causation, the evaluation of professional liability might also benefit from its use. Beginning with an analysis of the basic steps in assessing professional liability, this article examines the potential new medical-legal issues that an expert witness may encounter when analyzing malpractice cases and the potential integration of AI in this context. These changes related to the use of integrated AI, will necessitate efforts on the part of judges, experts, and clinicians, and may require new legislative regulations. A new expert witness will be likely necessary in the evaluation of professional liability cases. On the one hand, artificial intelligence will support the expert witness; however, on the other hand, it will introduce specific elements into the activities of healthcare workers. These elements will necessitate an expert witness with a specialized cultural background. Examining the steps of professional liability assessment indicates that the likely path for AI in legal medicine involves its role as a collaborative and integrated tool. The combination of AI with human judgment in these assessments can enhance comprehensiveness and fairness. However, it is imperative to adopt a cautious and balanced approach to prevent complete automation in this field.
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Affiliation(s)
- Claudio Terranova
- Legal Medicine and Toxicology, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padua, Padua, Italy
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15
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TerKonda SP, TerKonda AA, Sacks JM, Kinney BM, Gurtner GC, Nachbar JM, Reddy SK, Jeffers LL. Artificial Intelligence: Singularity Approaches. Plast Reconstr Surg 2024; 153:204e-217e. [PMID: 37075274 DOI: 10.1097/prs.0000000000010572] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2023]
Abstract
SUMMARY Artificial intelligence (AI) has been a disruptive technology within health care, from the development of simple care algorithms to complex deep-learning models. AI has the potential to reduce the burden of administrative tasks, advance clinical decision-making, and improve patient outcomes. Unlocking the full potential of AI requires the analysis of vast quantities of clinical information. Although AI holds tremendous promise, widespread adoption within plastic surgery remains limited. Understanding the basics is essential for plastic surgeons to evaluate the potential uses of AI. This review provides an introduction of AI, including the history of AI, key concepts, applications of AI in plastic surgery, and future implications.
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Affiliation(s)
- Sarvam P TerKonda
- From the Division of Plastic and Reconstructive Surgery, Mayo Clinic Florida
| | - Anurag A TerKonda
- Division of Plastic and Reconstructive Surgery, Washington University School of Medicine in St. Louis
| | - Justin M Sacks
- Division of Plastic and Reconstructive Surgery, Washington University School of Medicine in St. Louis
| | - Brian M Kinney
- Division of Plastic Surgery, University of Southern California
| | - Geoff C Gurtner
- Division of Plastic and Reconstructive Surgery, Stanford University
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16
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Fang Y, Chen X, Cao C. Cancer immunotherapy efficacy and machine learning. Expert Rev Anticancer Ther 2024; 24:21-28. [PMID: 38288663 DOI: 10.1080/14737140.2024.2311684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 01/25/2024] [Indexed: 02/03/2024]
Abstract
INTRODUCTION Immunotherapy is one of the major breakthroughs in the treatment of cancer, and it has become a powerful clinical strategy, however, not all patients respond to immune checkpoint blockade and other immunotherapy strategies. Applying machine learning (ML) techniques to predict the efficacy of cancer immunotherapy is useful for clinical decision-making. AREAS COVERED Applying ML including deep learning (DL) in radiomics, pathomics, tumor microenvironment (TME) and immune-related genes analysis to predict immunotherapy efficacy. The studies in this review were searched from PubMed and ClinicalTrials.gov (January 2023). EXPERT OPINION An increasing number of studies indicate that ML has been applied to various aspects of oncology research, with the potential to provide more effective individualized immunotherapy strategies and enhance treatment decisions. With advances in ML technology, more efficient methods of predicting the efficacy of immunotherapy may become available in the future.
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Affiliation(s)
- Yuting Fang
- Department of Radiation Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences; Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China
| | - Xiaozhong Chen
- Department of Radiation Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences; Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China
| | - Caineng Cao
- Department of Radiation Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences; Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China
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17
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Chokesuwattanaskul R, Petchlorlian A, Lertsanguansinchai P, Suttirut P, Prasitlumkum N, Srimahachota S, Buddhari W. Machine Learning-Based Predictive Model of Aortic Valve Replacement Modality Selection in Severe Aortic Stenosis Patients. Med Sci (Basel) 2023; 12:3. [PMID: 38249079 PMCID: PMC10801609 DOI: 10.3390/medsci12010003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 12/13/2023] [Accepted: 12/20/2023] [Indexed: 01/23/2024] Open
Abstract
The current recommendation for bioprosthetic valve replacement in severe aortic stenosis (AS) is either surgical aortic valve replacement (SAVR) or transcatheter aortic valve replacement (TAVR). We evaluated the performance of a machine learning-based predictive model using existing periprocedural variables for valve replacement modality selection. We analyzed 415 patients in a retrospective longitudinal cohort of adult patients undergoing aortic valve replacement for aortic stenosis. A total of 72 clinical variables including demographic data, patient comorbidities, and preoperative investigation characteristics were collected on each patient. We fit models using LASSO (least absolute shrinkage and selection operator) and decision tree techniques. The accuracy of the prediction on confusion matrix was used to assess model performance. The most predictive independent variable for valve selection by LASSO regression was frailty score. Variables that predict SAVR consisted of low frailty score (value at or below 2) and complex coronary artery diseases (DVD/TVD). Variables that predicted TAVR consisted of high frailty score (at or greater than 6), history of coronary artery bypass surgery (CABG), calcified aorta, and chronic kidney disease (CKD). The LASSO-generated predictive model achieved 98% accuracy on valve replacement modality selection from testing data. The decision tree model consisted of fewer important parameters, namely frailty score, CKD, STS score, age, and history of PCI. The most predictive factor for valve replacement selection was frailty score. The predictive models using different statistical learning methods achieved an excellent concordance predictive accuracy rate of between 93% and 98%.
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Affiliation(s)
- Ronpichai Chokesuwattanaskul
- Division of Cardiovascular Medicine, Department of Medicine, Faculty of Medicine, Center of Excellence in Arrhythmia Research, Chulalongkorn University, Bangkok 10330, Thailand; (R.C.); (P.L.); (P.S.); (S.S.); (W.B.)
- Cardiac Center, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok 10330, Thailand
| | - Aisawan Petchlorlian
- Division of Geriatric Medicine, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand;
- Geriatric Excellence Center, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok 10330, Thailand
| | - Piyoros Lertsanguansinchai
- Division of Cardiovascular Medicine, Department of Medicine, Faculty of Medicine, Center of Excellence in Arrhythmia Research, Chulalongkorn University, Bangkok 10330, Thailand; (R.C.); (P.L.); (P.S.); (S.S.); (W.B.)
- Cardiac Center, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok 10330, Thailand
| | - Paramaporn Suttirut
- Division of Cardiovascular Medicine, Department of Medicine, Faculty of Medicine, Center of Excellence in Arrhythmia Research, Chulalongkorn University, Bangkok 10330, Thailand; (R.C.); (P.L.); (P.S.); (S.S.); (W.B.)
- Cardiac Center, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok 10330, Thailand
| | - Narut Prasitlumkum
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 559020, USA
| | - Suphot Srimahachota
- Division of Cardiovascular Medicine, Department of Medicine, Faculty of Medicine, Center of Excellence in Arrhythmia Research, Chulalongkorn University, Bangkok 10330, Thailand; (R.C.); (P.L.); (P.S.); (S.S.); (W.B.)
- Cardiac Center, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok 10330, Thailand
| | - Wacin Buddhari
- Division of Cardiovascular Medicine, Department of Medicine, Faculty of Medicine, Center of Excellence in Arrhythmia Research, Chulalongkorn University, Bangkok 10330, Thailand; (R.C.); (P.L.); (P.S.); (S.S.); (W.B.)
- Cardiac Center, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok 10330, Thailand
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18
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Kumari V, Kumar N, Kumar K S, Kumar A, Skandha SS, Saxena S, Khanna NN, Laird JR, Singh N, Fouda MM, Saba L, Singh R, Suri JS. Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look. J Cardiovasc Dev Dis 2023; 10:485. [PMID: 38132653 PMCID: PMC10743870 DOI: 10.3390/jcdd10120485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 10/15/2023] [Accepted: 11/07/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND AND MOTIVATION Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. METHODS Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. FINDINGS AND CONCLUSIONS UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach.
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Affiliation(s)
- Vandana Kumari
- School of Computer Science and Engineering, Galgotias University, Greater Noida 201310, India; (V.K.); (S.K.K.)
| | - Naresh Kumar
- Department of Applied Computational Science and Engineering, G L Bajaj Institute of Technology and Management, Greater Noida 201310, India
| | - Sampath Kumar K
- School of Computer Science and Engineering, Galgotias University, Greater Noida 201310, India; (V.K.); (S.K.K.)
| | - Ashish Kumar
- School of CSET, Bennett University, Greater Noida 201310, India;
| | - Sanagala S. Skandha
- Department of CSE, CMR College of Engineering and Technology, Hyderabad 501401, India;
| | - Sanjay Saxena
- Department of Computer Science and Engineering, IIT Bhubaneswar, Bhubaneswar 751003, India;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA;
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era, Deemed to be University, Dehradun 248002, India;
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA;
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09100 Cagliari, Italy;
| | - Rajesh Singh
- Department of Research and Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, India;
| | - Jasjit S. Suri
- Stroke Diagnostics and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA
- Department of Computer Science & Engineering, Graphic Era, Deemed to be University, Dehradun 248002, India
- Monitoring and Diagnosis Division, AtheroPoint™, Roseville, CA 95661, USA
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19
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Wen F, Jiang S, Yuan P, Liu J, Bai X, Zhao H, Chen X, Gong YB, Asmar R, Wang H. Vascular Health Promotion Project and Vascular Medicine in China-CCVM2004-2023. Vasc Health Risk Manag 2023; 19:741-751. [PMID: 38025518 PMCID: PMC10656854 DOI: 10.2147/vhrm.s432656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023] Open
Abstract
Cardiovascular disease(CVD) has become a major disease burden affecting people's health in China. Blood vessels are very important for human health and are the "sentinel" for the development of many cardiovascular and cerebrovascular diseases. The key to effectively preventing fatal, disabling heart, brain and peripheral vascular events lies in controlling traditional and non-traditional risk factors for vascular health from the source, and early assessment and intervention of early vascular lesions. Since 2004, China government promoted the early detection technology of vascular lesions and vascular medicine, and proposed the Beijing Vascular Health Stratification (BVHS) to provide suggestions for the examination, evaluation and management of risk factors, and to provide new ideas for lifelong maintenance of vascular health. This review mainly introduces the establishment and development of the clinical discipline of "vascular medicine" in the past 20 years in China, introduces the indicators for detecting vascular function and structure and the predictive value of vascular events, and carries out intelligent and digital management of vascular health throughout the life cycle of individualized prevention, treatment and rehabilitation for people with different parts or degrees of lesions, effectively reducing the occurrence and development of cardiovascular and cerebrovascular diseases, and the prospect of new technology in maintaining vascular health.
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Affiliation(s)
- Fang Wen
- Department of Vascular Medicine, Peking University Shougang Hospital, Beijing, People’s Republic of China
| | - Shantong Jiang
- Department of Vascular Medicine, Peking University Shougang Hospital, Beijing, People’s Republic of China
- Vascular Health Research Center of Peking University Health Science Center (VHRC-PKUHSC), Beijing, People’s Republic of China
- Beijing Shijingshan District Key Clinical Specialty of Vascular Medicine, Beijing, People’s Republic of China
- Heart and Vascular Health Research Center of Peking University Clinical Research Institute (HVHRC-PUCRI), Beijing, People’s Republic of China
| | - Ping Yuan
- Department of Vascular Medicine, Peking University Shougang Hospital, Beijing, People’s Republic of China
- Vascular Health Research Center of Peking University Health Science Center (VHRC-PKUHSC), Beijing, People’s Republic of China
- Beijing Shijingshan District Key Clinical Specialty of Vascular Medicine, Beijing, People’s Republic of China
- Heart and Vascular Health Research Center of Peking University Clinical Research Institute (HVHRC-PUCRI), Beijing, People’s Republic of China
- Department of Scientific Research, Peking University Shougang Hospital, Beijing, People’s Republic of China
| | - Jinbo Liu
- Department of Vascular Medicine, Peking University Shougang Hospital, Beijing, People’s Republic of China
- Vascular Health Research Center of Peking University Health Science Center (VHRC-PKUHSC), Beijing, People’s Republic of China
- Beijing Shijingshan District Key Clinical Specialty of Vascular Medicine, Beijing, People’s Republic of China
- Heart and Vascular Health Research Center of Peking University Clinical Research Institute (HVHRC-PUCRI), Beijing, People’s Republic of China
| | - Xiu Bai
- Department of Vascular Medicine, Peking University Shougang Hospital, Beijing, People’s Republic of China
| | - Hongwei Zhao
- Department of Vascular Medicine, Peking University Shougang Hospital, Beijing, People’s Republic of China
- Vascular Health Research Center of Peking University Health Science Center (VHRC-PKUHSC), Beijing, People’s Republic of China
- Beijing Shijingshan District Key Clinical Specialty of Vascular Medicine, Beijing, People’s Republic of China
- Heart and Vascular Health Research Center of Peking University Clinical Research Institute (HVHRC-PUCRI), Beijing, People’s Republic of China
- State Key Laboratory of Vascular Homeostasis and Remodeling, (Peking University), Beijing, People’s Republic of China
- Heart and Vascular Health Research Center of Chengdu Medical College (HVHRC-CMC), Chengdu, People’s Republic of China
| | - Xin Chen
- Department of Vascular Medicine, Peking University Shougang Hospital, Beijing, People’s Republic of China
- Vascular Health Research Center of Peking University Health Science Center (VHRC-PKUHSC), Beijing, People’s Republic of China
- Beijing Shijingshan District Key Clinical Specialty of Vascular Medicine, Beijing, People’s Republic of China
- Heart and Vascular Health Research Center of Peking University Clinical Research Institute (HVHRC-PUCRI), Beijing, People’s Republic of China
- Heart and Vascular Health Research Center of Chengdu Medical College (HVHRC-CMC), Chengdu, People’s Republic of China
| | - Yan-Bing Gong
- Department of Scientific Research, Peking University Shougang Hospital, Beijing, People’s Republic of China
| | - Roland Asmar
- Foundation-Medical Research Institutes, Beirut, Lebanon
| | - Hongyu Wang
- Department of Vascular Medicine, Peking University Shougang Hospital, Beijing, People’s Republic of China
- Vascular Health Research Center of Peking University Health Science Center (VHRC-PKUHSC), Beijing, People’s Republic of China
- Beijing Shijingshan District Key Clinical Specialty of Vascular Medicine, Beijing, People’s Republic of China
- Heart and Vascular Health Research Center of Peking University Clinical Research Institute (HVHRC-PUCRI), Beijing, People’s Republic of China
- State Key Laboratory of Vascular Homeostasis and Remodeling, (Peking University), Beijing, People’s Republic of China
- Heart and Vascular Health Research Center of Chengdu Medical College (HVHRC-CMC), Chengdu, People’s Republic of China
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20
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Aromiwura AA, Settle T, Umer M, Joshi J, Shotwell M, Mattumpuram J, Vorla M, Sztukowska M, Contractor S, Amini A, Kalra DK. Artificial intelligence in cardiac computed tomography. Prog Cardiovasc Dis 2023; 81:54-77. [PMID: 37689230 DOI: 10.1016/j.pcad.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 09/04/2023] [Indexed: 09/11/2023]
Abstract
Artificial Intelligence (AI) is a broad discipline of computer science and engineering. Modern application of AI encompasses intelligent models and algorithms for automated data analysis and processing, data generation, and prediction with applications in visual perception, speech understanding, and language translation. AI in healthcare uses machine learning (ML) and other predictive analytical techniques to help sort through vast amounts of data and generate outputs that aid in diagnosis, clinical decision support, workflow automation, and prognostication. Coronary computed tomography angiography (CCTA) is an ideal union for these applications due to vast amounts of data generation and analysis during cardiac segmentation, coronary calcium scoring, plaque quantification, adipose tissue quantification, peri-operative planning, fractional flow reserve quantification, and cardiac event prediction. In the past 5 years, there has been an exponential increase in the number of studies exploring the use of AI for cardiac computed tomography (CT) image acquisition, de-noising, analysis, and prognosis. Beyond image processing, AI has also been applied to improve the imaging workflow in areas such as patient scheduling, urgent result notification, report generation, and report communication. In this review, we discuss algorithms applicable to AI and radiomic analysis; we then present a summary of current and emerging clinical applications of AI in cardiac CT. We conclude with AI's advantages and limitations in this new field.
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Affiliation(s)
| | - Tyler Settle
- Medical Imaging Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA
| | - Muhammad Umer
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Jonathan Joshi
- Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA
| | - Matthew Shotwell
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Jishanth Mattumpuram
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Mounica Vorla
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Maryta Sztukowska
- Clinical Trials Unit, University of Louisville, Louisville, KY, USA; University of Information Technology and Management, Rzeszow, Poland
| | - Sohail Contractor
- Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA
| | - Amir Amini
- Medical Imaging Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA; Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA
| | - Dinesh K Kalra
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA; Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA.
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21
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Samant S, Bakhos JJ, Wu W, Zhao S, Kassab GS, Khan B, Panagopoulos A, Makadia J, Oguz UM, Banga A, Fayaz M, Glass W, Chiastra C, Burzotta F, LaDisa JF, Iaizzo P, Murasato Y, Dubini G, Migliavacca F, Mickley T, Bicek A, Fontana J, West NEJ, Mortier P, Boyers PJ, Gold JP, Anderson DR, Tcheng JE, Windle JR, Samady H, Jaffer FA, Desai NR, Lansky A, Mena-Hurtado C, Abbott D, Brilakis ES, Lassen JF, Louvard Y, Stankovic G, Serruys PW, Velazquez E, Elias P, Bhatt DL, Dangas G, Chatzizisis YS. Artificial Intelligence, Computational Simulations, and Extended Reality in Cardiovascular Interventions. JACC Cardiovasc Interv 2023; 16:2479-2497. [PMID: 37879802 DOI: 10.1016/j.jcin.2023.07.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 07/11/2023] [Accepted: 07/13/2023] [Indexed: 10/27/2023]
Abstract
Artificial intelligence, computational simulations, and extended reality, among other 21st century computational technologies, are changing the health care system. To collectively highlight the most recent advances and benefits of artificial intelligence, computational simulations, and extended reality in cardiovascular therapies, we coined the abbreviation AISER. The review particularly focuses on the following applications of AISER: 1) preprocedural planning and clinical decision making; 2) virtual clinical trials, and cardiovascular device research, development, and regulatory approval; and 3) education and training of interventional health care professionals and medical technology innovators. We also discuss the obstacles and constraints associated with the application of AISER technologies, as well as the proposed solutions. Interventional health care professionals, computer scientists, biomedical engineers, experts in bioinformatics and visualization, the device industry, ethics committees, and regulatory agencies are expected to streamline the use of AISER technologies in cardiovascular interventions and medicine in general.
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Affiliation(s)
- Saurabhi Samant
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Jules Joel Bakhos
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Wei Wu
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Shijia Zhao
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Ghassan S Kassab
- California Medical Innovations Institute, San Diego, California, USA
| | - Behram Khan
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Anastasios Panagopoulos
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Janaki Makadia
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Usama M Oguz
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Akshat Banga
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Muhammad Fayaz
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - William Glass
- Interprofessional Experiential Center for Enduring Learning, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Claudio Chiastra
- PoliTo(BIO)Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy
| | - Francesco Burzotta
- Department of Cardiovascular Sciences, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - John F LaDisa
- Departments of Biomedical Engineering and Pediatrics - Division of Cardiology, Herma Heart Institute, Children's Wisconsin and the Medical College of Wisconsin, and the MARquette Visualization Lab, Marquette University, Milwaukee, Wisconsin, USA
| | - Paul Iaizzo
- Visible Heart Laboratories, Department of Surgery, University of Minnesota, Minnesota, USA
| | - Yoshinobu Murasato
- Department of Cardiology, National Hospital Organization Kyushu Medical Center, Fukuoka, Japan
| | - Gabriele Dubini
- Department of Chemistry, Materials and Chemical Engineering 'Giulio Natta', Politecnico di Milano, Milan, Italy
| | - Francesco Migliavacca
- Department of Chemistry, Materials and Chemical Engineering 'Giulio Natta', Politecnico di Milano, Milan, Italy
| | | | - Andrew Bicek
- Boston Scientific Inc, Marlborough, Massachusetts, USA
| | | | | | | | - Pamela J Boyers
- Interprofessional Experiential Center for Enduring Learning, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Jeffrey P Gold
- Interprofessional Experiential Center for Enduring Learning, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Daniel R Anderson
- Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - James E Tcheng
- Cardiovascular Division, Duke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina, USA
| | - John R Windle
- Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Habib Samady
- Georgia Heart Institute, Gainesville, Georgia, USA
| | - Farouc A Jaffer
- Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Nihar R Desai
- Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Alexandra Lansky
- Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Carlos Mena-Hurtado
- Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Dawn Abbott
- Cardiovascular Institute, Warren Alpert Medical School at Brown University, Providence, Rhode Island, USA
| | - Emmanouil S Brilakis
- Center for Advanced Coronary Interventions, Minneapolis Heart Institute, Minneapolis, Minnesota, USA
| | - Jens Flensted Lassen
- Department of Cardiology B, Odense University Hospital, Odense, Syddanmark, Denmark
| | - Yves Louvard
- Institut Cardiovasculaire Paris Sud, Massy, France
| | - Goran Stankovic
- Department of Cardiology, Clinical Center of Serbia, Belgrade, Serbia
| | - Patrick W Serruys
- Department of Cardiology, National University of Ireland, Galway, Galway, Ireland
| | - Eric Velazquez
- Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Pierre Elias
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, New York, New York, USA
| | - Deepak L Bhatt
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - George Dangas
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yiannis S Chatzizisis
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA.
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22
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Bradley AJ, Ghawanmeh M, Govi AM, Covas P, Panjrath G, Choi AD. Emerging Roles for Artificial Intelligence in Heart Failure Imaging. Heart Fail Clin 2023; 19:531-543. [PMID: 37714592 DOI: 10.1016/j.hfc.2023.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/17/2023]
Abstract
Artificial intelligence (AI) applications are expanding in cardiac imaging. AI research has shown promise in workflow optimization, disease diagnosis, and integration of clinical and imaging data to predict patient outcomes. The diagnostic and prognostic paradigm of heart failure is heavily reliant on cardiac imaging. As AI becomes increasingly validated and integrated into clinical practice, AI influence on heart failure management will grow. This review discusses areas of current research and potential clinical applications in AI as applied to heart failure cardiac imaging.
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Affiliation(s)
- Andrew J Bradley
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA.
| | - Malik Ghawanmeh
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Ashley M Govi
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Pedro Covas
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Gurusher Panjrath
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA. https://twitter.com/PanjrathG
| | - Andrew D Choi
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA. https://twitter.com/AChoiHeart
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23
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Zhou Q, Chen Z, Wu B, Lin D, Hu Y, Zhang X, Liu J. A Pilot Study: Detrusor Overactivity Diagnosis Method Based on Deep Learning. Urology 2023; 179:188-195. [PMID: 37315592 DOI: 10.1016/j.urology.2023.04.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 04/24/2023] [Accepted: 04/26/2023] [Indexed: 06/16/2023]
Abstract
OBJECTIVE To develop two intelligent diagnosis models of detrusor overactivity (DO) based on deep learning to assist doctors no longer heavily rely on visual observation of urodynamic study (UDS) curves. METHODS UDS curves of 92 patients were collected during 2019. We constructed two DO event recognition models based on convolutional neural network (CNN) with 44 samples, and tested the model performance with the remaining 48 samples by comparing other four classical machine learning models. During the testing phase, we developed a threshold screening strategy to quickly filter out suspected DO event segments in each patient's UDS curve. If two or more DO event fragments are determined to be DO by the diagnostic model, the patient is diagnosed as having DO. RESULTS We extracted 146 DO event samples and 1863 non-DO event samples from the UDS curves of 44 patients to train CNN models. Through 10-fold cross-validation, the training accuracy and validation accuracy of our models achieved the highest accuracy. In the model testing phase, we used a threshold screening strategy to quickly screen out the suspected DO event samples in the UDS curve of another 48 patients, and then input them into the trained models. Finally, the diagnostic accuracy of patients without DO and patients with DO was 78.12% and 100%, respectively. CONCLUSION Under the available data, the accuracy of the DO diagnostic model based on CNN is satisfactory. With the increase of the amount of data, the deep learning model is likely to have better performance. CLINICAL TRIAL REGISTRATION This experiment was certified by the Chinese Clinical Trial Registry (ChiCTR2200063467).
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Affiliation(s)
- Quan Zhou
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Zhong Chen
- Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Department of Urology, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Bo Wu
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Dongxu Lin
- Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Youmin Hu
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China.
| | - Xin Zhang
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Jie Liu
- School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, China
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24
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Svennberg E, Caiani EG, Bruining N, Desteghe L, Han JK, Narayan SM, Rademakers FE, Sanders P, Duncker D. The digital journey: 25 years of digital development in electrophysiology from an Europace perspective. Europace 2023; 25:euad176. [PMID: 37622574 PMCID: PMC10450797 DOI: 10.1093/europace/euad176] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 06/03/2023] [Indexed: 08/26/2023] Open
Abstract
AIMS Over the past 25 years there has been a substantial development in the field of digital electrophysiology (EP) and in parallel a substantial increase in publications on digital cardiology.In this celebratory paper, we provide an overview of the digital field by highlighting publications from the field focusing on the EP Europace journal. RESULTS In this journey across the past quarter of a century we follow the development of digital tools commonly used in the clinic spanning from the initiation of digital clinics through the early days of telemonitoring, to wearables, mobile applications, and the use of fully virtual clinics. We then provide a chronicle of the field of artificial intelligence, a regulatory perspective, and at the end of our journey provide a future outlook for digital EP. CONCLUSION Over the past 25 years Europace has published a substantial number of papers on digital EP, with a marked expansion in digital publications in recent years.
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Affiliation(s)
- Emma Svennberg
- Department of Medicine Huddinge, Karolinska Institutet, Karolinska University Hospital Huddinge, SE-141 86 Stockholm, Sweden
| | - Enrico G Caiani
- Politecnico di Milano, Electronic, Information and Biomedical Engineering Department, Milan, Italy
- Istituto Auxologico Italiano IRCCS, Milan, Italy
| | - Nico Bruining
- Department of Clinical and Experimental Information processing (Digital Cardiology), Erasmus Medical Center, Thoraxcenter, Rotterdam, The Netherlands
| | - Lien Desteghe
- Research Group Cardiovascular Diseases, University of Antwerp, 2000 Antwerp, Belgium
- Department of Cardiology, Antwerp University Hospital, 2056 Edegem, Belgium
- Faculty of Medicine and Life Sciences, Hasselt University, 3500 Hasselt, Belgium
- Department of Cardiology, Heart Centre Hasselt, Jessa Hospital, 3500 Hasselt, Belgium
| | - Janet K Han
- Division of Cardiology, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA 90073, USA
- Cardiac Arrhythmia Center, University of California Los Angeles, Los Angeles, CA, USA
| | - Sanjiv M Narayan
- Cardiology Division, Cardiovascular Institute and Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
| | | | - Prashanthan Sanders
- Centre for Heart Rhythm Disorders, University of Adelaide and Royal Adelaide Hospital, 5005 Adelaide, Australia
| | - David Duncker
- Hannover Heart Rhythm Center, Department of Cardiology and Angiology, Hannover Medical School, Hannover, Germany
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25
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Wawak M, Tekieli Ł, Badacz R, Pieniążek P, Maciejewski D, Trystuła M, Przewłocki T, Kabłak-Ziembicka A. Clinical Characteristics and Outcomes of Aortic Arch Emergencies: Takayasu Disease, Fibromuscular Dysplasia, and Aortic Arch Pathologies: A Retrospective Study and Review of the Literature. Biomedicines 2023; 11:2207. [PMID: 37626704 PMCID: PMC10452526 DOI: 10.3390/biomedicines11082207] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/02/2023] [Accepted: 08/04/2023] [Indexed: 08/27/2023] Open
Abstract
Non-atherosclerotic aortic arch pathologies (NA-AAPs) and anatomical variants are characterized as rare cardiovascular diseases with a low incidence rate, below 1 case per 2000 population, but enormous heterogeneity in terms of anatomical variants, i.e., Takayasu disease (TAK) and fibromuscular dysplasia (FMD). In specific clinical scenarios, NA-AAPs constitute life-threatening disorders. METHODS In this study, 82 (1.07%) consecutive patients with NA-AAPs (including 38 TAKs, 26 FMDs, and 18 other AAPs) out of 7645 patients who underwent endovascular treatment (EVT) for the aortic arch and its side-branch diseases at a single institution between 2002 and 2022 were retrospectively reviewed. The recorded demographic, biochemical, diagnostic, operative, and postoperative factors were reviewed, and the functional outcomes were determined during follow-up. A systematic review of the literature was also performed. RESULTS The study group comprised 65 (79.3%) female and 17 (21.7%) male subjects with a mean age of 46.1 ± 14.9 years. Overall, 62 (75.6%) patients were diagnosed with either cerebral ischemia symptoms or aortic arch dissection on admission. The EVT was feasible in 59 (72%) patients, whereas 23 (28%) patients were referred for medical treatment. In EVT patients, severe periprocedural complications occurred in two (3.39%) patients, including one periprocedural death and one cerebral hyperperfusion syndrome. During a median follow-up period of 64 months, cardiovascular events occurred in 24 (29.6%) patients (5 deaths, 13 ISs, and 6 myocardial infarctions). Repeated EVT for the index lesion was performed in 21/59 (35.6%) patients, including 19/33 (57.6%) in TAK and 2/13 (15.4%) in FMD. In the AAP group, one patient required additional stent-graft implantation for progressing dissection to the iliac arteries at 12 months. A baseline white blood count (odds ratio [HR]: 1.25, 95% confidence interval [CI]: 1.11-1.39; p < 0.001) was the only independent prognostic factor for recurrent stenosis, while a baseline hemoglobin level (HR: 0.73, 95%CI: 0.59-0.89; p = 0.002) and coronary involvement (HR: 4.11, 95%CI: 1.74-9.71; p = 0.001) were independently associated with a risk of major cardiac and cerebral events according to the multivariate Cox proportional hazards regression analysis. CONCLUSIONS This study showed that AAPs should not be neglected in clinical settings, as it can be a life-threatening condition requiring a multidisciplinary approach. The knowledge of prognostic risk factors for adverse outcomes may improve surveillance in this group of patients.
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Affiliation(s)
- Magdalena Wawak
- Department of Interventional Cardiology, The John Paul II Hospital, Prądnicka 80, 31-202 Kraków, Poland
| | - Łukasz Tekieli
- Department of Interventional Cardiology, The John Paul II Hospital, Prądnicka 80, 31-202 Kraków, Poland
| | - Rafał Badacz
- Department of Interventional Cardiology, The John Paul II Hospital, Prądnicka 80, 31-202 Kraków, Poland
- Department of Interventional Cardiology, Institute of Cardiology, Jagiellonian University Medical College, św. Anny 12, 31-007 Kraków, Poland
| | - Piotr Pieniążek
- Department of Interventional Cardiology, The John Paul II Hospital, Prądnicka 80, 31-202 Kraków, Poland
- Department of Cardiac and Vascular Diseases, Institute of Cardiology, Jagiellonian University Medical College, św. Anny 12, 31-007 Kraków, Poland
| | - Damian Maciejewski
- Department of Interventional Cardiology, The John Paul II Hospital, Prądnicka 80, 31-202 Kraków, Poland
| | - Mariusz Trystuła
- Department of Vascular and Endovascular Surgery, The John Paul II Hospital, Prądnicka 80, 31-202 Kraków, Poland;
| | - Tadeusz Przewłocki
- Department of Interventional Cardiology, The John Paul II Hospital, Prądnicka 80, 31-202 Kraków, Poland
- Department of Cardiac and Vascular Diseases, Institute of Cardiology, Jagiellonian University Medical College, św. Anny 12, 31-007 Kraków, Poland
| | - Anna Kabłak-Ziembicka
- Department of Interventional Cardiology, Institute of Cardiology, Jagiellonian University Medical College, św. Anny 12, 31-007 Kraków, Poland
- Noninvasive Cardiovascular Laboratory, The John Paul II Hospital, Prądnicka 80, 31-202 Kraków, Poland
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26
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Cobo M, Pérez-Rojas F, Gutiérrez-Rodríguez C, Heredia I, Maragaño-Lizama P, Yung-Manriquez F, Lloret Iglesias L, Vega JA. Novel deep learning method for coronary artery tortuosity detection through coronary angiography. Sci Rep 2023; 13:11137. [PMID: 37429940 PMCID: PMC10333289 DOI: 10.1038/s41598-023-37868-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 06/28/2023] [Indexed: 07/12/2023] Open
Abstract
Coronary artery tortuosity is usually an undetected condition in patients undergoing coronary angiography. This condition requires a longer examination by the specialist to be detected. Yet, detailed knowledge of the morphology of coronary arteries is essential for planning any interventional treatment, such as stenting. We aimed to analyze coronary artery tortuosity in coronary angiography with artificial intelligence techniques to develop an algorithm capable of automatically detecting this condition in patients. This work uses deep learning techniques, in particular, convolutional neural networks, to classify patients into tortuous or non-tortuous based on their coronary angiography. The developed model was trained both on left (Spider) and right (45°/0°) coronary angiographies following a fivefold cross-validation procedure. A total of 658 coronary angiographies were included. Experimental results demonstrated satisfactory performance of our image-based tortuosity detection system, with a test accuracy of (87 ± 6)%. The deep learning model had a mean area under the curve of 0.96 ± 0.03 over the test sets. The sensitivity, specificity, positive predictive values, and negative predictive values of the model for detecting coronary artery tortuosity were (87 ± 10)%, (88 ± 10)%, (89 ± 8)%, and (88 ± 9)%, respectively. Deep learning convolutional neural networks were found to have comparable sensitivity and specificity with independent experts' radiological visual examination for detecting coronary artery tortuosity for a conservative threshold of 0.5. These findings have promising applications in the field of cardiology and medical imaging.
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Affiliation(s)
- Miriam Cobo
- Advanced Computing Research Group, Institute of Physics of Cantabria (IFCA), CSIC - UC, 39005, Santander, Cantabria, Spain.
| | - Francisco Pérez-Rojas
- Grupo de Investigación MEXPA, Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Talca, Chile
- Grupo de Investigación SINPOS, Departamento de Morfología y Biología Celular, Universidad de Oviedo, 3306, Oviedo, Principality of Asturias, Spain
| | | | - Ignacio Heredia
- Advanced Computing Research Group, Institute of Physics of Cantabria (IFCA), CSIC - UC, 39005, Santander, Cantabria, Spain
| | | | | | - Lara Lloret Iglesias
- Advanced Computing Research Group, Institute of Physics of Cantabria (IFCA), CSIC - UC, 39005, Santander, Cantabria, Spain
| | - José A Vega
- Grupo de Investigación MEXPA, Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Talca, Chile
- Grupo de Investigación SINPOS, Departamento de Morfología y Biología Celular, Universidad de Oviedo, 3306, Oviedo, Principality of Asturias, Spain
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Hwang I, Trivedi H, Brown-Mulry B, Zhang L, Nalla V, Gastounioti A, Gichoya J, Seyyed-Kalantari L, Banerjee I, Woo M. Impact of multi-source data augmentation on performance of convolutional neural networks for abnormality classification in mammography. FRONTIERS IN RADIOLOGY 2023; 3:1181190. [PMID: 37588666 PMCID: PMC10426498 DOI: 10.3389/fradi.2023.1181190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 05/30/2023] [Indexed: 08/18/2023]
Abstract
Introduction To date, most mammography-related AI models have been trained using either film or digital mammogram datasets with little overlap. We investigated whether or not combining film and digital mammography during training will help or hinder modern models designed for use on digital mammograms. Methods To this end, a total of six binary classifiers were trained for comparison. The first three classifiers were trained using images only from Emory Breast Imaging Dataset (EMBED) using ResNet50, ResNet101, and ResNet152 architectures. The next three classifiers were trained using images from EMBED, Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM), and Digital Database for Screening Mammography (DDSM) datasets. All six models were tested only on digital mammograms from EMBED. Results The results showed that performance degradation to the customized ResNet models was statistically significant overall when EMBED dataset was augmented with CBIS-DDSM/DDSM. While the performance degradation was observed in all racial subgroups, some races are subject to more severe performance drop as compared to other races. Discussion The degradation may potentially be due to ( 1) a mismatch in features between film-based and digital mammograms ( 2) a mismatch in pathologic and radiological information. In conclusion, use of both film and digital mammography during training may hinder modern models designed for breast cancer screening. Caution is required when combining film-based and digital mammograms or when utilizing pathologic and radiological information simultaneously.
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Affiliation(s)
- InChan Hwang
- School of Data Science and Analytics, Kennesaw State University, Kennesaw, GA, United States
| | - Hari Trivedi
- Department of Radiology, Emory University, Atlanta, GA, United States
| | - Beatrice Brown-Mulry
- School of Data Science and Analytics, Kennesaw State University, Kennesaw, GA, United States
| | - Linglin Zhang
- School of Data Science and Analytics, Kennesaw State University, Kennesaw, GA, United States
| | - Vineela Nalla
- Department of Information Technology, Kennesaw State University, Kennesaw, GA, United States
| | - Aimilia Gastounioti
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, United States
| | - Judy Gichoya
- Department of Radiology, Emory University, Atlanta, GA, United States
| | - Laleh Seyyed-Kalantari
- Department of Electrical Engineering and Computer Science, York University, Toronto, ON, Canada
| | - Imon Banerjee
- Department of Radiology, Mayo Clinic Arizona, Phoenix, AZ, United States
| | - MinJae Woo
- School of Data Science and Analytics, Kennesaw State University, Kennesaw, GA, United States
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28
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Fujihara K, Yamada Harada M, Horikawa C, Iwanaga M, Tanaka H, Nomura H, Sui Y, Tanabe K, Yamada T, Kodama S, Kato K, Sone H. Machine learning approach to predict body weight in adults. Front Public Health 2023; 11:1090146. [PMID: 37397751 PMCID: PMC10308016 DOI: 10.3389/fpubh.2023.1090146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 05/10/2023] [Indexed: 07/04/2023] Open
Abstract
Background Obesity is an established risk factor for non-communicable diseases such as type 2 diabetes mellitus, hypertension and cardiovascular disease. Thus, weight control is a key factor in the prevention of non-communicable diseases. A simple and quick method to predict weight change over a few years could be helpful for weight management in clinical settings. Methods We examined the ability of a machine learning model that we constructed to predict changes in future body weight over 3 years using big data. Input in the machine learning model were three-year data on 50,000 Japanese persons (32,977 men) aged 19-91 years who underwent annual health examinations. The predictive formulas that used heterogeneous mixture learning technology (HMLT) to predict body weight in the subsequent 3 years were validated for 5,000 persons. The root mean square error (RMSE) was used to evaluate accuracy compared with multiple regression. Results The machine learning model utilizing HMLT automatically generated five predictive formulas. The influence of lifestyle on body weight was found to be large in people with a high body mass index (BMI) at baseline (BMI ≥29.93 kg/m2) and in young people (<24 years) with a low BMI (BMI <23.44 kg/m2). The RMSE was 1.914 in the validation set which reflects ability comparable to that of the multiple regression model of 1.890 (p = 0.323). Conclusion The HMLT-based machine learning model could successfully predict weight change over 3 years. Our model could automatically identify groups whose lifestyle profoundly impacted weight loss and factors the influenced body weight change in individuals. Although this model must be validated in other populations, including other ethnic groups, before being widely implemented in global clinical settings, results suggested that this machine learning model could contribute to individualized weight management.
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Affiliation(s)
- Kazuya Fujihara
- Department of Endocrinology and Metabolism, Faculty of Medicine, Niigata University, Niigata, Japan
| | - Mayuko Yamada Harada
- Department of Endocrinology and Metabolism, Faculty of Medicine, Niigata University, Niigata, Japan
| | - Chika Horikawa
- Department of Health and Nutrition, Faculty of Human Life Studies, University of Niigata Prefecture, Niigata, Japan
| | - Midori Iwanaga
- Department of Endocrinology and Metabolism, Faculty of Medicine, Niigata University, Niigata, Japan
| | | | | | | | | | - Takaho Yamada
- Department of Endocrinology and Metabolism, Faculty of Medicine, Niigata University, Niigata, Japan
| | - Satoru Kodama
- Department of Endocrinology and Metabolism, Faculty of Medicine, Niigata University, Niigata, Japan
| | - Kiminori Kato
- Department of Prevention of Noncommunicable Diseases and Promotion of Health Checkup, Niigata University, Niigata, Japan
| | - Hirohito Sone
- Department of Endocrinology and Metabolism, Faculty of Medicine, Niigata University, Niigata, Japan
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29
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Stevenson LW, Ross HJ, Rathman LD, Boehmer JP. Remote Monitoring for Heart Failure Management at Home. J Am Coll Cardiol 2023; 81:2272-2291. [PMID: 37286258 DOI: 10.1016/j.jacc.2023.04.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 06/09/2023]
Abstract
Early telemonitoring of weights and symptoms did not decrease heart failure hospitalizations but helped identify steps toward effective monitoring programs. A signal that is accurate and actionable with response kinetics for early re-assessment is required for the treatment of patients at high risk, while signal specifications differ for surveillance of low-risk patients. Tracking of congestion with cardiac filling pressures or lung water content has shown most impact to decrease hospitalizations, while multiparameter scores from implanted rhythm devices have identified patients at increased risk. Algorithms require better personalization of signal thresholds and interventions. The COVID-19 epidemic accelerated transition to remote care away from clinics, preparing for new digital health care platforms to accommodate multiple technologies and empower patients. Addressing inequities will require bridging the digital divide and the deep gap in access to HF care teams, who will not be replaced by technology but by care teams who can embrace it.
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Affiliation(s)
| | - Heather J Ross
- Ted Rogers Centre for Heart Research, Peter Munk Centre, Toronto, Ontario, Canada
| | - Lisa D Rathman
- PENN Medicine Lancaster General Health, Lancaster, Pennsylvania, USA
| | - John P Boehmer
- Penn State Health Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA
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30
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Ye Y, Lv Y, Mao Y, Li L, Chen X, Zheng R, Hou X, Yu C, Gabriella C, Fu GS. Cardiovascular imaging in conduction system pacing: What does the clinician need? Pacing Clin Electrophysiol 2023; 46:548-557. [PMID: 36516139 DOI: 10.1111/pace.14644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 12/03/2022] [Accepted: 12/07/2022] [Indexed: 12/15/2022]
Abstract
Permanent pacemakers are used for symptomatic bradycardia and biventricular pacing (BVP)-cardiac resynchronization therapy (BVP-CRT) is established for heart failure (HF) patients traditionally. According to guidelines, patients' selection for CRT is based on QRS duration (QRSd) and morphology by surface electrocardiogram (ECG). Cardiovascular imaging techniques evaluate cardiac structure and function as well as identify pathophysiological substrate changes including the presence of scar. Cardiovascular imaging helps by improving the selection of candidates, guiding left ventricular (LV) lead placement, and optimization devices during the follow-up. Conduction system pacing (CSP) includes His bundle pacing (HBP) and left bundle branch pacing (LBBP) which is screwed into the interventricular septum. CSP maintains and restores ventricular synchrony in patients with native narrow QRSd and left bundle branch block (LBBB), respectively. LBBP is more feasible than HBP due to a wider target area. This review highlights the role of multimodality cardiovascular imaging including fluoroscopy, echocardiography, cardiac magnetic resonance (CMR), myocardial scintigraphy, and computed tomography (CT) in the pre-procedure assessment for CSP, better selection for CSP candidates, the guidance of CSP lead implantation, and the optimization of devices programming after the procedure. We also compare the different characteristics of multimodality imaging and discuss their potential roles in future CSP implantation.
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Affiliation(s)
- Yang Ye
- Department of Cardiology, Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
| | - Yuan Lv
- Department of Cardiology, Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
| | - Yankai Mao
- Department of Diagnostic Ultrasound and Echocardiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Lin Li
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Xueying Chen
- Shanghai Institution of Cardiovascular Disease, Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Rujie Zheng
- Department of Cardiology, The First Affiliated Hospital of Wenzhou Medical University, Key Lab of Cardiovascular Disease of Wenzhou, Wenzhou, China
| | - Xiaofeng Hou
- Department of Cardiology, First Affiliated Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Chan Yu
- Department of Diagnostic Ultrasound and Echocardiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Captur Gabriella
- Institute of Cardiovascular Science, University College London, London, UK
- Centre for Inherited Heart Muscle Conditions, Department of Cardiology, Royal Free London NHS Foundation Trust, London, UK
- Medical Research Council Unit for Lifelong Health and Ageing at UCL, London, UK
| | - Guo-Sheng Fu
- Department of Cardiology, Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
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31
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Garg Y, Seetharam K, Sharma M, Rohita DK, Nabi W. Role of Deep Learning in Computed Tomography. Cureus 2023; 15:e39160. [PMID: 37332431 PMCID: PMC10275744 DOI: 10.7759/cureus.39160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/17/2023] [Indexed: 06/20/2023] Open
Abstract
Computed tomography has played an instrumental role in the understanding of the pathophysiology of atherosclerosis in coronary artery disease. It enables visualization of the plaque obstruction and vessel stenosis in a comprehensive manner. As technology for computed tomography is constantly evolving, coronary applications and possibilities are constantly expanding. This influx of information can overwhelm a physician's ability to interpret information in this era of big data. Machine learning is a revolutionary approach that can help provide limitless pathways in patient management. Within these machine algorithms, deep learning has tremendous potential and can revolutionize computed tomography and cardiovascular imaging. In this review article, we highlight the role of deep learning in various aspects of computed tomography.
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Affiliation(s)
- Yash Garg
- Internal Medicine, Wyckoff Heights Medical Center, New York, USA
| | | | - Manjari Sharma
- Internal Medicine, Wyckoff Heights Medical Center, New York, USA
| | - Dipesh K Rohita
- Internal Medicine, Wyckoff Heights Medical Center, New York, USA
| | - Waseem Nabi
- Internal Medicine, Wyckoff Heights Medical Center, New York, USA
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32
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Krittanawong C, Omar AMS, Narula S, Sengupta PP, Glicksberg BS, Narula J, Argulian E. Deep Learning for Echocardiography: Introduction for Clinicians and Future Vision: State-of-the-Art Review. Life (Basel) 2023; 13:life13041029. [PMID: 37109558 PMCID: PMC10145844 DOI: 10.3390/life13041029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 03/30/2023] [Accepted: 04/03/2023] [Indexed: 04/29/2023] Open
Abstract
Exponential growth in data storage and computational power is rapidly narrowing the gap between translating findings from advanced clinical informatics into cardiovascular clinical practice. Specifically, cardiovascular imaging has the distinct advantage in providing a great quantity of data for potentially rich insights, but nuanced interpretation requires a high-level skillset that few individuals possess. A subset of machine learning, deep learning (DL), is a modality that has shown promise, particularly in the areas of image recognition, computer vision, and video classification. Due to a low signal-to-noise ratio, echocardiographic data tend to be challenging to classify; however, utilization of robust DL architectures may help clinicians and researchers automate conventional human tasks and catalyze the extraction of clinically useful data from the petabytes of collected imaging data. The promise is extending far and beyond towards a contactless echocardiographic exam-a dream that is much needed in this time of uncertainty and social distancing brought on by a stunning pandemic culture. In the current review, we discuss state-of-the-art DL techniques and architectures that can be used for image and video classification, and future directions in echocardiographic research in the current era.
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Affiliation(s)
- Chayakrit Krittanawong
- Cardiology Division, NYU Langone Health, NYU School of Medicine, New York, NY 10016, USA
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, New York, NY 10029, USA
| | - Alaa Mabrouk Salem Omar
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, New York, NY 10029, USA
- Division of Cardiovascular Medicine, Icahn School of Medicine at Mount Sinai Morningside, Mount Sinai Heart, New York, NY 10029, USA
| | - Sukrit Narula
- Department of Medicine, Yale School of Medicine, New Haven, CT 06512, USA
| | - Partho P Sengupta
- Robert Wood Johnson University Hospital, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08901, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jagat Narula
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, New York, NY 10029, USA
- Division of Cardiovascular Medicine, Icahn School of Medicine at Mount Sinai Morningside, Mount Sinai Heart, New York, NY 10029, USA
| | - Edgar Argulian
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, New York, NY 10029, USA
- Division of Cardiovascular Medicine, Icahn School of Medicine at Mount Sinai Morningside, Mount Sinai Heart, New York, NY 10029, USA
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33
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Arnould L, Meriaudeau F, Guenancia C, Germanese C, Delcourt C, Kawasaki R, Cheung CY, Creuzot-Garcher C, Grzybowski A. Using Artificial Intelligence to Analyse the Retinal Vascular Network: The Future of Cardiovascular Risk Assessment Based on Oculomics? A Narrative Review. Ophthalmol Ther 2023; 12:657-674. [PMID: 36562928 PMCID: PMC10011267 DOI: 10.1007/s40123-022-00641-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 12/09/2022] [Indexed: 12/24/2022] Open
Abstract
The healthcare burden of cardiovascular diseases remains a major issue worldwide. Understanding the underlying mechanisms and improving identification of people with a higher risk profile of systemic vascular disease through noninvasive examinations is crucial. In ophthalmology, retinal vascular network imaging is simple and noninvasive and can provide in vivo information of the microstructure and vascular health. For more than 10 years, different research teams have been working on developing software to enable automatic analysis of the retinal vascular network from different imaging techniques (retinal fundus photographs, OCT angiography, adaptive optics, etc.) and to provide a description of the geometric characteristics of its arterial and venous components. Thus, the structure of retinal vessels could be considered a witness of the systemic vascular status. A new approach called "oculomics" using retinal image datasets and artificial intelligence algorithms recently increased the interest in retinal microvascular biomarkers. Despite the large volume of associated research, the role of retinal biomarkers in the screening, monitoring, or prediction of systemic vascular disease remains uncertain. A PubMed search was conducted until August 2022 and yielded relevant peer-reviewed articles based on a set of inclusion criteria. This literature review is intended to summarize the state of the art in oculomics and cardiovascular disease research.
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Affiliation(s)
- Louis Arnould
- Ophthalmology Department, Dijon University Hospital, 14 Rue Paul Gaffarel, 21079, Dijon CEDEX, France. .,University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR U1219, 33000, Bordeaux, France.
| | - Fabrice Meriaudeau
- Laboratory ImViA, IFTIM, Université Bourgogne Franche-Comté, 21078, Dijon, France
| | - Charles Guenancia
- Pathophysiology and Epidemiology of Cerebro-Cardiovascular Diseases, (EA 7460), Faculty of Health Sciences, Université de Bourgogne Franche-Comté, Dijon, France.,Cardiology Department, Dijon University Hospital, Dijon, France
| | - Clément Germanese
- Ophthalmology Department, Dijon University Hospital, 14 Rue Paul Gaffarel, 21079, Dijon CEDEX, France
| | - Cécile Delcourt
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR U1219, 33000, Bordeaux, France
| | - Ryo Kawasaki
- Artificial Intelligence Center for Medical Research and Application, Osaka University Hospital, Osaka, Japan
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Catherine Creuzot-Garcher
- Ophthalmology Department, Dijon University Hospital, 14 Rue Paul Gaffarel, 21079, Dijon CEDEX, France.,Centre des Sciences du Goût et de l'Alimentation, AgroSup Dijon, CNRS, INRAE, Université Bourgogne Franche-Comté, Dijon, France
| | - Andrzej Grzybowski
- Department of Ophthalmology, University of Warmia and Mazury, Olsztyn, Poland.,Institute for Research in Ophthalmology, Poznan, Poland
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Zhou J, You D, Bai J, Chen X, Wu Y, Wang Z, Tang Y, Zhao Y, Feng G. Machine Learning Methods in Real-World Studies of Cardiovascular Disease. CARDIOVASCULAR INNOVATIONS AND APPLICATIONS 2023. [DOI: 10.15212/cvia.2023.0011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023] Open
Abstract
Objective:Cardiovascular disease (CVD) is one of the leading causes of death worldwide, and answers are urgently needed regarding many aspects, particularly risk identification and prognosis prediction. Real-world studies with large numbers of observations provide an important basis for CVD research but are constrained by high dimensionality, and missing or unstructured data. Machine learning (ML) methods, including a variety of supervised and unsupervised algorithms, are useful for data governance, and are effective for high dimensional data analysis and imputation in real-world studies. This article reviews the theory, strengths and limitations, and applications of several commonly used ML methods in the CVD field, to provide a reference for further application.Methods:This article introduces the origin, purpose, theory, advantages and limitations, and applications of multiple commonly used ML algorithms, including hierarchical and k-means clustering, principal component analysis, random forest, support vector machine, and neural networks. An example uses a random forest on the Systolic Blood Pressure Intervention Trial (SPRINT) data to demonstrate the process and main results of ML application in CVD.Conclusion:ML methods are effective tools for producing real-world evidence to support clinical decisions and meet clinical needs. This review explains the principles of multiple ML methods in plain language, to provide a reference for further application. Future research is warranted to develop accurate ensemble learning methods for wide application in the medical field.
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35
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D’Amario D, Laborante R, Delvinioti A, Lenkowicz J, Iacomini C, Masciocchi C, Luraschi A, Damiani A, Rodolico D, Restivo A, Ciliberti G, Paglianiti DA, Canonico F, Patarnello S, Cesario A, Valentini V, Scambia G, Crea F. GENERATOR HEART FAILURE DataMart: An integrated framework for heart failure research. Front Cardiovasc Med 2023; 10:1104699. [PMID: 37034335 PMCID: PMC10073733 DOI: 10.3389/fcvm.2023.1104699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 03/07/2023] [Indexed: 04/11/2023] Open
Abstract
Background Heart failure (HF) is a multifaceted clinical syndrome characterized by different etiologies, risk factors, comorbidities, and a heterogeneous clinical course. The current model, based on data from clinical trials, is limited by the biases related to a highly-selected sample in a protected environment, constraining the applicability of evidence in the real-world scenario. If properly leveraged, the enormous amount of data from real-world may have a groundbreaking impact on clinical care pathways. We present, here, the development of an HF DataMart framework for the management of clinical and research processes. Methods Within our institution, Fondazione Policlinico Universitario A. Gemelli in Rome (Italy), a digital platform dedicated to HF patients has been envisioned (GENERATOR HF DataMart), based on two building blocks: 1. All retrospective information has been integrated into a multimodal, longitudinal data repository, providing in one single place the description of individual patients with drill-down functionalities in multiple dimensions. This functionality might allow investigators to dynamically filter subsets of patient populations characterized by demographic characteristics, biomarkers, comorbidities, and clinical events (e.g., re-hospitalization), enabling agile analyses of the outcomes by subsets of patients. 2. With respect to expected long-term health status and response to treatments, the use of the disease trajectory toolset and predictive models for the evolution of HF has been implemented. The methodological scaffolding has been constructed in respect of a set of the preferred standards recommended by the CODE-EHR framework. Results Several examples of GENERATOR HF DataMart utilization are presented as follows: to select a specific retrospective cohort of HF patients within a particular period, along with their clinical and laboratory data, to explore multiple associations between clinical and laboratory data, as well as to identify a potential cohort for enrollment in future studies; to create a multi-parametric predictive models of early re-hospitalization after discharge; to cluster patients according to their ejection fraction (EF) variation, investigating its potential impact on hospital admissions. Conclusion The GENERATOR HF DataMart has been developed to exploit a large amount of data from patients with HF from our institution and generate evidence from real-world data. The two components of the HF platform might provide the infrastructural basis for a combined patient support program dedicated to continuous monitoring and remote care, assisting patients, caregivers, and healthcare professionals.
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Affiliation(s)
- Domenico D’Amario
- Department of Cardiovascular and Pulmonary Sciences, Catholic University of the Sacred Heart, Rome, Italy
- Department of Cardiovascular Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Università del Piemonte Orientale, Dipartimento Medicina Translazionale, Azienda Ospedaliero-Universitaria Maggiore della Carità, Dipartimento Toraco-Cardio-Vascolare, Unità Operativa Complessa di Cardiologia 1, Novara, Italy
| | - Renzo Laborante
- Department of Cardiovascular and Pulmonary Sciences, Catholic University of the Sacred Heart, Rome, Italy
| | - Agni Delvinioti
- Gemelli Generator, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Jacopo Lenkowicz
- Gemelli Generator, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Chiara Iacomini
- Gemelli Generator, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Carlotta Masciocchi
- Gemelli Generator, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Alice Luraschi
- Gemelli Generator, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Andrea Damiani
- Gemelli Generator, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Daniele Rodolico
- Department of Cardiovascular and Pulmonary Sciences, Catholic University of the Sacred Heart, Rome, Italy
| | - Attilio Restivo
- Department of Cardiovascular and Pulmonary Sciences, Catholic University of the Sacred Heart, Rome, Italy
| | - Giuseppe Ciliberti
- Department of Cardiovascular and Pulmonary Sciences, Catholic University of the Sacred Heart, Rome, Italy
| | - Donato Antonio Paglianiti
- Department of Cardiovascular and Pulmonary Sciences, Catholic University of the Sacred Heart, Rome, Italy
| | - Francesco Canonico
- Department of Cardiovascular and Pulmonary Sciences, Catholic University of the Sacred Heart, Rome, Italy
| | - Stefano Patarnello
- Gemelli Generator, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Alfredo Cesario
- Gemelli Generator, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Vincenzo Valentini
- Department of Bioimaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Università Cattolica S. Cuore, Rome, Italy
| | - Giovanni Scambia
- Gemelli Generator, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Filippo Crea
- Department of Cardiovascular and Pulmonary Sciences, Catholic University of the Sacred Heart, Rome, Italy
- Department of Cardiovascular Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
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Accurate Detection of Arrhythmias on Raw Electrocardiogram Images:An Aggregation Attention Multi-label Model for Diagnostic Assistance. Med Eng Phys 2023; 114:103964. [PMID: 37030892 DOI: 10.1016/j.medengphy.2023.103964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 12/24/2022] [Accepted: 02/23/2023] [Indexed: 03/06/2023]
Abstract
BACKGROUND The low rate of detection of abnormalities has been a major problem with current artificial intelligence-based electrocardiogram diagnostic algorithms, particularly when applied under real-world clinical scenarios. METHODS We proposed an aggregation attention multilabel electrocardiogram classification model (AA-ECG) that can be applied directly to raw images to identify cardiac abnormalities using image-level annotation only. To develop and validate the model, we conducted a prospective two-site study to build two large-scale real-world datasets of 12-lead electrocardiogram images, annotated by clinical experts in a multilabeled manner. We compared the proposed model with seven state-of-the-art classifiers on both datasets in 27 main categories. RESULTS In total, 47,733 electrocardiogram images from 37,442 consecutive patients were included in the development set, while 18,581 from 18,345 in the external set. The proposed model achieved better overall performance than the other seven models.The visualization of the attention maps provided an approach to build medical interpretability for machine intelligence. CONCLUSIONS The proposed model had high diagnostic accuracy in identifying cardiac abnormalities on two real-world datasets. It has the potential to help clinicians provide more efficient cardiac care with fewer medical resources.
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Liem DA, Cadeiras M, Setty SP. Insights and perspectives into clinical biomarker discovery in pediatric heart failure and congenital heart disease-a narrative review. Cardiovasc Diagn Ther 2023; 13:83-99. [PMID: 36864972 PMCID: PMC9971290 DOI: 10.21037/cdt-22-386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 12/02/2022] [Indexed: 01/11/2023]
Abstract
Background and Objective Heart failure (HF) in the pediatric population is a multi-factorial process with a wide spectrum of etiologies and clinical manifestations, that are distinct from the adult HF population, with congenital heart disease (CHD) as the most common cause. CHD has high morbidity/mortality with nearly 60% developing HF during the first 12 months of life. Hence, early discovery and diagnosis of CHD in neonates is pivotal. Plasma B-type natriuretic peptide (BNP) is an increasingly popular clinical marker in pediatric HF, however, in contrast to adult HF, it is not yet included in pediatric HF guidelines and there is no standardized reference cut-off value. We explore the current trends and prospects of biomarkers in pediatric HF, including CHD that can aid in diagnosis and management. Methods As a narrative review, we will analyze biomarkers with respect to diagnosis and monitoring in specific anatomical types of CHD in the pediatric population considering all English PubMed publications till June 2022. Key Content and Findings We present a concise description of our own experience in applying plasma BNP as a clinical biomarker in pediatric HF and CHD (tetralogy of fallot vs. ventricular septal defect) in the context of surgical correction, as well as untargeted metabolomics analyses. In the current age of Information Technology and large data sets we also explored new biomarker discovery using Text Mining of 33M manuscripts currently on PubMed. Conclusions (Multi) Omics studies from patient samples as well as Data Mining can be considered for the discovery of potential pediatric HF biomarkers useful in clinical care. Future research should focus on validation and defining evidence-based value limits and reference ranges for specific indications using the most up-to-date assays in parallel to commonly used studies.
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Affiliation(s)
- David A. Liem
- Department of Medicine, Division of Cardiovascular Disease, University of California, Davis, CA, USA
| | - Martin Cadeiras
- Department of Medicine, Division of Cardiovascular Disease, University of California, Davis, CA, USA
| | - Shaun P. Setty
- Department of Pediatric and Adult Congenital Cardiac Surgery, Miller Children’s and Women’s Hospital and Long Beach Memorial Hospital, Long Beach, CA, USA
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Deep-learning-based prognostic modeling for incident heart failure in patients with diabetes using electronic health records: A retrospective cohort study. PLoS One 2023; 18:e0281878. [PMID: 36809251 PMCID: PMC9943005 DOI: 10.1371/journal.pone.0281878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 02/02/2023] [Indexed: 02/23/2023] Open
Abstract
Patients with type 2 diabetes mellitus (T2DM) have more than twice the risk of developing heart failure (HF) compared to patients without diabetes. The present study is aimed to build an artificial intelligence (AI) prognostic model that takes in account a large and heterogeneous set of clinical factors and investigates the risk of developing HF in diabetic patients. We carried out an electronic health records- (EHR-) based retrospective cohort study that included patients with cardiological clinical evaluation and no previous diagnosis of HF. Information consists of features extracted from clinical and administrative data obtained as part of routine medical care. The primary endpoint was diagnosis of HF (during out-of-hospital clinical examination or hospitalization). We developed two prognostic models using (1) elastic net regularization for Cox proportional hazard model (COX) and (2) a deep neural network survival method (PHNN), in which a neural network was used to represent a non-linear hazard function and explainability strategies are applied to estimate the influence of predictors on the risk function. Over a median follow-up of 65 months, 17.3% of the 10,614 patients developed HF. The PHNN model outperformed COX both in terms of discrimination (c-index 0.768 vs 0.734) and calibration (2-year integrated calibration index 0.008 vs 0.018). The AI approach led to the identification of 20 predictors of different domains (age, body mass index, echocardiographic and electrocardiographic features, laboratory measurements, comorbidities, therapies) whose relationship with the predicted risk correspond to known trends in the clinical practice. Our results suggest that prognostic models for HF in diabetic patients may improve using EHRs in combination with AI techniques for survival analysis, which provide high flexibility and better performance with respect to standard approaches.
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Zheng Y, Xu Z, Xiao A. Deep learning in economics: a systematic and critical review. Artif Intell Rev 2023; 56:1-43. [PMID: 36777109 PMCID: PMC9898707 DOI: 10.1007/s10462-022-10272-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
From the perspective of historical review, the methodology of economics develops from qualitative to quantitative, from a small sampling of data to a vast amount of data. Because of the superiority in learning inherent law and representative level, deep learning models assist in realizing intelligent decision-making in economics. After presenting some statistical results of relevant researches, this paper systematically investigates deep learning in economics, including a survey of frequently-used deep learning models in economics, several applications of deep learning models used in economics. Then, some critical reviews of deep learning in economics are provided, including models and applications, why and how to implement deep learning in economics, research gap and future challenges, respectively. It is obvious that several deep learning models and their variants have been widely applied in different subfields of economics, e.g., financial economics, macroeconomics and monetary economics, agricultural and natural resource economics, industrial organization, urban, rural, regional, real estate and transportation economics, health, education and welfare, business administration and microeconomics, etc. We are very confident that decision-making in economics will be more intelligent with the development of deep learning, because the research of deep learning in economics has become a hot and important topic recently.
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Affiliation(s)
- Yuanhang Zheng
- College of Computer Science, Sichuan University, 610064 Chengdu, PR China
| | - Zeshui Xu
- Business School, Sichuan University, 610064 Chengdu, PR China
| | - Anran Xiao
- Business School, Sichuan University, 610064 Chengdu, PR China
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40
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Precision medicine for long QT syndrome: patient-specific iPSCs take the lead. Expert Rev Mol Med 2023; 25:e5. [PMID: 36597672 DOI: 10.1017/erm.2022.43] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Long QT syndrome (LQTS) is a detrimental arrhythmia syndrome mainly caused by dysregulated expression or aberrant function of ion channels. The major clinical symptoms of ventricular arrhythmia, palpitations and syncope vary among LQTS subtypes. Susceptibility to malignant arrhythmia is a result of delayed repolarisation of the cardiomyocyte action potential (AP). There are 17 distinct subtypes of LQTS linked to 15 autosomal dominant genes with monogenic mutations. However, due to the presence of modifier genes, the identical mutation may result in completely different clinical manifestations in different carriers. In this review, we describe the roles of various ion channels in orchestrating APs and discuss molecular aetiologies of various types of LQTS. We highlight the usage of patient-specific induced pluripotent stem cell (iPSC) models in characterising fundamental mechanisms associated with LQTS. To mitigate the outcomes of LQTS, treatment strategies are initially focused on small molecules targeting ion channel activities. Next-generation treatments will reap the benefits from development of LQTS patient-specific iPSC platform, which is bolstered by the state-of-the-art technologies including whole-genome sequencing, CRISPR genome editing and machine learning. Deep phenotyping and high-throughput drug testing using LQTS patient-specific cardiomyocytes herald the upcoming precision medicine in LQTS.
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Uslu F, Bharath AA. TMS-Net: A segmentation network coupled with a run-time quality control method for robust cardiac image segmentation. Comput Biol Med 2023; 152:106422. [PMID: 36535210 DOI: 10.1016/j.compbiomed.2022.106422] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 12/02/2022] [Accepted: 12/11/2022] [Indexed: 12/15/2022]
Abstract
Recently, deep networks have shown impressive performance for the segmentation of cardiac Magnetic Resonance Imaging (MRI) images. However, their achievement is proving slow to transition to widespread use in medical clinics because of robustness issues leading to low trust of clinicians to their results. Predicting run-time quality of segmentation masks can be useful to warn clinicians against poor results. Despite its importance, there are few studies on this problem. To address this gap, we propose a quality control method based on the agreement across decoders of a multi-view network, TMS-Net, measured by the cosine similarity. The network takes three view inputs resliced from the same 3D image along different axes. Different from previous multi-view networks, TMS-Net has a single encoder and three decoders, leading to better noise robustness, segmentation performance and run-time quality estimation in our experiments on the segmentation of the left atrium on STACOM 2013 and STACOM 2018 challenge datasets. We also present a way to generate poor segmentation masks by using noisy images generated with engineered noise and Rician noise to simulate undertraining, high anisotropy and poor imaging settings problems. Our run-time quality estimation method show a good classification of poor and good quality segmentation masks with an AUC reaching to 0.97 on STACOM 2018. We believe that TMS-Net and our run-time quality estimation method has a high potential to increase the thrust of clinicians to automatic image analysis tools.
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Affiliation(s)
- Fatmatülzehra Uslu
- Bursa Technical University, Electrical and Electronics Engineering Department, Bursa, 16310, Turkey.
| | - Anil A Bharath
- Imperial College London, Bioengineering Department, London, SW7 2AZ, UK.
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42
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Rana M, Bhushan M. Machine learning and deep learning approach for medical image analysis: diagnosis to detection. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 82:1-39. [PMID: 36588765 PMCID: PMC9788870 DOI: 10.1007/s11042-022-14305-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 11/01/2022] [Accepted: 12/10/2022] [Indexed: 06/17/2023]
Abstract
Computer-aided detection using Deep Learning (DL) and Machine Learning (ML) shows tremendous growth in the medical field. Medical images are considered as the actual origin of appropriate information required for diagnosis of disease. Detection of disease at the initial stage, using various modalities, is one of the most important factors to decrease mortality rate occurring due to cancer and tumors. Modalities help radiologists and doctors to study the internal structure of the detected disease for retrieving the required features. ML has limitations with the present modalities due to large amounts of data, whereas DL works efficiently with any amount of data. Hence, DL is considered as the enhanced technique of ML where ML uses the learning techniques and DL acquires details on how machines should react around people. DL uses a multilayered neural network to get more information about the used datasets. This study aims to present a systematic literature review related to applications of ML and DL for the detection along with classification of multiple diseases. A detailed analysis of 40 primary studies acquired from the well-known journals and conferences between Jan 2014-2022 was done. It provides an overview of different approaches based on ML and DL for the detection along with the classification of multiple diseases, modalities for medical imaging, tools and techniques used for the evaluation, description of datasets. Further, experiments are performed using MRI dataset to provide a comparative analysis of ML classifiers and DL models. This study will assist the healthcare community by enabling medical practitioners and researchers to choose an appropriate diagnosis technique for a given disease with reduced time and high accuracy.
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Affiliation(s)
- Meghavi Rana
- School of Computing, DIT University, Dehradun, India
| | - Megha Bhushan
- School of Computing, DIT University, Dehradun, India
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43
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Baykaner T, Narayan S. Machine learning of adipose tissue in atrial fibrillation. Heart Rhythm 2022; 19:2042-2043. [PMID: 36041687 PMCID: PMC10115142 DOI: 10.1016/j.hrthm.2022.08.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Accepted: 08/22/2022] [Indexed: 12/14/2022]
Affiliation(s)
- Tina Baykaner
- Department of Internal Medicine, Division of Cardiology, Stanford University, Stanford, California
| | - Sanjiv Narayan
- Department of Internal Medicine, Division of Cardiology, Stanford University, Stanford, California.
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Mashraqi AM, Allehyani B. Current trends on the application of artificial intelligence in medical sciences. Bioinformation 2022; 18:1050-1061. [PMID: 37693078 PMCID: PMC10484692 DOI: 10.6026/973206300181050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 11/29/2022] [Accepted: 11/30/2022] [Indexed: 09/12/2023] Open
Abstract
Artificial Intelligence (AI) is expanding with colossal applications in various sectors. In the healthcare sector, it is booming to make life simpler with utmost accuracy by predicting, diagnosing and up to care with the help of Machine Learning (ML) and Deep Learning (DL) applications. Modern computer algorithms have attained accuracy levels comparable to those of human specialists in medical sciences, although computers often do jobs more quickly than people do. It is also expected that there will not be a mandate for humans to be present for the jobs that machines can do, and it is gaining the highest peak because of good trained artificial models in the medical field. ML enhances the therapeutic process and improves health by encouraging more patient participation. ML may get more accurate patient data when used with the Internet of Medical Things (IoMT) and automate message notifications that prompt patients to respond at certain times. The motivation behind this article is to make a comprehensive review of the on-going implementation of ML in medical science, what challenges it is facing now, and how it can be simplified for future researchers to contribute better to medical sciences while applying it to the practitioners' jobs easier. In this review, we have extensively mined the data and brought up systematised applications of AI in healthcare, what challenges have been faced by the experts, and what ethical responsibilities are liable to them while taking the data. We also tabulated which algorithms will be helpful for what kind of data and disease conditions will be useful for future researchers and developers. This article will provide a better insight into AI and ML for the beginner to the advanced developer and researcher to understand the concepts from the basics.
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Affiliation(s)
- Aisha Mousa Mashraqi
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, UAE
| | - Budoor Allehyani
- Department of Information System, College of Computers and Information Systems, Umm Al-Qura University, UAE
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45
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Shi Y, Zheng Z, Liu Y, Wu Y, Wang P, Liu J. Leveraging Machine Learning Techniques to Forecast Chronic Total Occlusion before Coronary Angiography. J Clin Med 2022; 11:jcm11236993. [PMID: 36498568 PMCID: PMC9739483 DOI: 10.3390/jcm11236993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/17/2022] [Accepted: 11/24/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Chronic total occlusion (CTO) remains the most challenging procedure in coronary artery disease (CAD) for interventional cardiology. Although some clinical risk factors for CAD have been identified, there is no personalized prognosis test available to confidently identify patients at high or low risk for CTO CAD. This investigation aimed to use a machine learning algorithm for clinical features from clinical routine to develop a precision medicine tool to predict CTO before CAG. METHODS Data from 1473 CAD patients were obtained, including 1105 in the training cohort and 368 in the testing cohort. The baseline clinical characteristics were collected. Univariate and multivariate logistic regression analyses were conducted to identify independent risk factors that impact the diagnosis of CTO. A CTO predicting model was established and validated based on the independent predictors using a machine learning algorithm. The area under the curve (AUC) was used to evaluate the model. RESULTS The CTO prediction model was developed with the training cohort using the machine learning algorithm. Eight variables were confirmed as 'important': gender (male), neutrophil percentage (NE%), hematocrit (HCT), total cholesterol (TC), high-density lipoprotein cholesterol (HDL), ejection fraction (EF), troponin I (TnI), and N-terminal pro-B-type natriuretic peptide (NT-proBNP). The model achieved good concordance indices of 0.724 and 0.719 in the training and testing cohorts, respectively. CONCLUSIONS An easy-to-use tool to predict CTO in patients with CAD was developed and validated. More research with larger cohorts are warranted to improve the prediction model, which can support clinician decisions on the early discerning CTO in CAD patients.
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Affiliation(s)
- Yuchen Shi
- Center for Coronary Artery Disease (CCAD), Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, 2 Anzhen Road, Chaoyang District, Beijing 100029, China
| | - Ze Zheng
- Center for Coronary Artery Disease (CCAD), Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, 2 Anzhen Road, Chaoyang District, Beijing 100029, China
| | - Yanci Liu
- Center for Coronary Artery Disease (CCAD), Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, 2 Anzhen Road, Chaoyang District, Beijing 100029, China
| | - Yongxin Wu
- Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou 450007, China
| | - Ping Wang
- Center for Coronary Artery Disease (CCAD), Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, 2 Anzhen Road, Chaoyang District, Beijing 100029, China
| | - Jinghua Liu
- Center for Coronary Artery Disease (CCAD), Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, 2 Anzhen Road, Chaoyang District, Beijing 100029, China
- Correspondence: ; Fax: +86-010-64456998
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46
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Deep Learning Improves Prediction of Cardiovascular Disease-Related Mortality and Admission in Patients with Hypertension: Analysis of the Korean National Health Information Database. J Clin Med 2022; 11:jcm11226677. [PMID: 36431154 PMCID: PMC9697313 DOI: 10.3390/jcm11226677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/06/2022] [Accepted: 11/09/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE The aim of this study was to develop, compare, and validate models for predicting cardiovascular disease (CVD) mortality and hospitalization with hypertension using a conventional statistical model and a deep learning model. METHODS Using the database of Korean National Health Insurance Service, 2,037,027 participants with hypertension were identified. Among them, CVD (myocardial infarction or stroke) death and/or hospitalization that occurred within one year after the last visit were analyzed. Oversampling was performed using the synthetic minority oversampling algorithm to resolve imbalances in the number of samples between case and control groups. The logistic regression method and deep neural network (DNN) method were used to train models for assessing the risk of mortality and hospitalization. FINDINGS Deep learning-based prediction model showed a higher performance in all datasets than the logistic regression model in predicting CVD hospitalization (accuracy, 0.863 vs. 0.655; F1-score, 0.854 vs. 0.656; AUC, 0.932 vs. 0.655) and CVD death (accuracy, 0.925 vs. 0.780; F1-score, 0.924 vs. 0.783; AUC, 0.979 vs. 0.780). INTERPRETATION The deep learning model could accurately predict CVD hospitalization and death within a year in patients with hypertension. The findings of this study could allow for prevention and monitoring by allocating resources to high-risk patients.
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47
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Kim KH, Kwon JM, Pereira T, Attia ZI, Pereira NL. Artificial Intelligence Applied to Cardiomyopathies: Is It Time for Clinical Application? Curr Cardiol Rep 2022; 24:1547-1555. [PMID: 36048306 DOI: 10.1007/s11886-022-01776-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/16/2022] [Indexed: 01/11/2023]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) techniques have the potential to remarkably change the practice of cardiology in order to improve and optimize outcomes in heart failure and specifically cardiomyopathies, offering us novel tools to interpret data and make clinical decisions. The aim of this review is to describe the contemporary state of AI and digital health applied to cardiomyopathies as well as to define a potential pivotal role of its application by physicians in clinical practice. RECENT FINDINGS Many studies have been undertaken in recent years on cardiomyopathy screening especially using AI-enhanced electrocardiography (ECG). Even with mild left ventricular (LV) dysfunction, AI-ECG screening for amyloidosis, hypertrophic cardiomyopathy, or dilated cardiomyopathy is now feasible. Introduction of AI-ECG in routine clinical care has resulted in higher detection of LV systolic dysfunction; however, clinical research on a broader scale with diverse populations is necessary and ongoing. In the area of cardiac-imaging, AI automatically assesses the thickness and characteristics of myocardium to differentiate cardiomyopathies, but research on its prognostic capability has yet to be conducted. AI is also being applied to cardiomyopathy genomics, especially to predict pathogenicity of variants and identify whether these variants are clinically actionable. While the implementation of AI in the diagnosis and treatment of cardiomyopathies is still in its infancy, an ever-growing clinical research strategy will ascertain the clinical utility of these AI tools to help improve diagnosis of and outcomes in cardiomyopathies. We also need to standardize the tools used to monitor the performance of AI-based systems which can then be used to expedite decision-making and rectify any hidden biases. Given its potential important role in clinical practice, healthcare providers need to familiarize themselves with the promise and limitations of this technology.
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Affiliation(s)
- Kyung-Hee Kim
- Internal Medicine, Department of Cardiology, Incheon Sejong Hospital, Incheon, South Korea.,Department of Cardiovascular Medicine, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA
| | - Joon-Myung Kwon
- Department of Critical Care and Emergency Medicine, Incheon Sejong Hospital, Incheon, South Korea.,Medical Research Team, Medical AI, Co, Seoul, South Korea
| | - Tara Pereira
- Artificial Intelligence Development, Center for Digital Health, Mayo Clinic, Rochester, USA
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA
| | - Naveen L Pereira
- Department of Cardiovascular Medicine, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA. .,Department of Molecular Pharmacology and Therapeutics, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.
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48
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Miller RJH, Kuronuma K, Singh A, Otaki Y, Hayes S, Chareonthaitawee P, Kavanagh P, Parekh T, Tamarappoo BK, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Carli MD, Cadet S, Liang JX, Dey D, Berman DS, Slomka PJ. Explainable Deep Learning Improves Physician Interpretation of Myocardial Perfusion Imaging. J Nucl Med 2022; 63:1768-1774. [PMID: 35512997 PMCID: PMC9635672 DOI: 10.2967/jnumed.121.263686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 04/18/2022] [Indexed: 11/16/2022] Open
Abstract
Artificial intelligence may improve accuracy of myocardial perfusion imaging (MPI) but will likely be implemented as an aid to physician interpretation rather than an autonomous tool. Deep learning (DL) has high standalone diagnostic accuracy for obstructive coronary artery disease (CAD), but its influence on physician interpretation is unknown. We assessed whether access to explainable DL predictions improves physician interpretation of MPI. Methods: We selected a representative cohort of patients who underwent MPI with reference invasive coronary angiography. Obstructive CAD, defined as stenosis ≥50% in the left main artery or ≥70% in other coronary segments, was present in half of the patients. We used an explainable DL model (CAD-DL), which was previously developed in a separate population from different sites. Three physicians interpreted studies first with clinical history, stress, and quantitative perfusion, then with all the data plus the DL results. Diagnostic accuracy was assessed using area under the receiver-operating-characteristic curve (AUC). Results: In total, 240 patients with a median age of 65 y (interquartile range 58-73) were included. The diagnostic accuracy of physician interpretation with CAD-DL (AUC 0.779) was significantly higher than that of physician interpretation without CAD-DL (AUC 0.747, P = 0.003) and stress total perfusion deficit (AUC 0.718, P < 0.001). With matched specificity, CAD-DL had higher sensitivity when operating autonomously compared with readers without DL results (P < 0.001), but not compared with readers interpreting with DL results (P = 0.122). All readers had numerically higher accuracy with CAD-DL, with AUC improvement 0.02-0.05, and interpretation with DL resulted in overall net reclassification improvement of 17.2% (95% CI 9.2%-24.4%, P < 0.001). Conclusion: Explainable DL predictions lead to meaningful improvements in physician interpretation; however, the improvement varied across the readers, reflecting the acceptance of this new technology. This technique could be implemented as an aid to physician diagnosis, improving the diagnostic accuracy of MPI.
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Affiliation(s)
- Robert J H Miller
- Division of Artificial Intelligence in Medicine, Department of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
- Department of Cardiac Sciences, University of Calgary and Libin Cardiovascular Institute, Calgary, Alberta, Canada
| | - Keiichiro Kuronuma
- Division of Artificial Intelligence in Medicine, Department of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
- Department of Cardiology, Nihon University, Tokyo, Japan
| | - Ananya Singh
- Division of Artificial Intelligence in Medicine, Department of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Yuka Otaki
- Division of Artificial Intelligence in Medicine, Department of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Sean Hayes
- Division of Artificial Intelligence in Medicine, Department of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | | | - Paul Kavanagh
- Division of Artificial Intelligence in Medicine, Department of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Tejas Parekh
- Division of Artificial Intelligence in Medicine, Department of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Balaji K Tamarappoo
- Division of Artificial Intelligence in Medicine, Department of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Tali Sharir
- Department of Nuclear Cardiology, Assuta Medical Centers, Tel Aviv, Israel and Ben Gurion University of the Negev, Beer Sheba, Israel
| | - Andrew J Einstein
- Division of Cardiology, Department of Medicine, and Department of Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, New York
| | - Mathews B Fish
- Oregon Heart and Vascular Institute, Sacred Heart Medical Center, Springfield, Oregon
| | - Terrence D Ruddy
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Philipp A Kaufmann
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Zurich, Switzerland
| | - Albert J Sinusas
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut
| | | | - Sharmila Dorbala
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Brigham and Women's Hospital, Boston, Massachusetts
| | - Marcelo Di Carli
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Brigham and Women's Hospital, Boston, Massachusetts
| | - Sebastien Cadet
- Division of Artificial Intelligence in Medicine, Department of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Joanna X Liang
- Division of Artificial Intelligence in Medicine, Department of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Damini Dey
- Division of Artificial Intelligence in Medicine, Department of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Daniel S Berman
- Division of Artificial Intelligence in Medicine, Department of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Piotr J Slomka
- Division of Artificial Intelligence in Medicine, Department of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California;
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49
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Karužas A, Balčiūnas J, Fukson M, Verikas D, Matuliauskas D, Platūkis T, Vaičiulienė D, Jurgaitytė J, Kupstytė-Krištaponė N, Dirsienė R, Jaruševičius G, Šakalytė G, Plisienė J, Lesauskaitė V. Artificial intelligence for automated evaluation of aortic measurements in 2D echocardiography: Feasibility, accuracy, and reproducibility. Echocardiography 2022; 39:1439-1445. [PMID: 36266744 DOI: 10.1111/echo.15475] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 09/14/2022] [Accepted: 10/02/2022] [Indexed: 12/01/2022] Open
Abstract
AIMS This study sought to examine the feasibility, accuracy and reproducibility of a novel, fully automated 2D transthoracic echocardiography (2D TTE) parasternal long axis (PLAX) view aortic measurements quantification software compared to board-certified cardiologists in controlled clinical setting. METHODS AND RESULTS Aortic Annulus (AoA), Aortic Sinus (AoS), Sinotubular Junction (STJ) and Proximal Ascending Aorta (AAo) diameter measurements were performed retrospectively on each of 58 subjects in two different ways: twice using a fully automated software (Ligence Heart version 2) and twice manually by three cardiologists (ORG) and one expert cardiologist (EC). Out of 58 studies AoA was measured in 54 (93%), AoS in 55 (95%), STJ in 55 (95%) and AAo in 54 (93%) studies. Automated measurements had a stronger correlation with EC when compared to ORG with the largest correlation difference of .1 for STJ measurements and lowest difference of .01 for AoS measurements. Automated software was in higher agreement with ground truth intervals (ORG measurements mean +- SEM) in three out of four measurements. CONCLUSION Fully automated 2D TTE PLAX view aortic measurements using a novel AI-based quantification software are feasible and yield results that are in close agreement with what experienced readers measure manually while providing better reproducibility. This approach may prove to have important clinical implications in the automation of the aortic root and ascending aorta assessment to improve workflow efficiency.
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Affiliation(s)
- Arnas Karužas
- Institute of Cardiology, Lithuanian University of Health Sciences, 15 Sukileliu street, Kaunas, 50103, Lithuania.,Ligence, UAB, Goštauto Str. 8, Vilnius, 01108, Lithuania
| | | | - Mark Fukson
- Ligence, UAB, Goštauto Str. 8, Vilnius, 01108, Lithuania
| | - Dovydas Verikas
- Institute of Cardiology, Lithuanian University of Health Sciences, 15 Sukileliu street, Kaunas, 50103, Lithuania.,Ligence, UAB, Goštauto Str. 8, Vilnius, 01108, Lithuania
| | | | - Tautvydas Platūkis
- Republican Siauliai Hospital, 99 V. Kudirkos street, Siauliai, 76231, Lithuania
| | - Dovilė Vaičiulienė
- Republican Siauliai Hospital, 99 V. Kudirkos street, Siauliai, 76231, Lithuania
| | - Julija Jurgaitytė
- Republican Siauliai Hospital, 99 V. Kudirkos street, Siauliai, 76231, Lithuania
| | | | - Rūta Dirsienė
- Cardiology clinic, Lithuanian University of Health Sciences, 2 Eiveniu street, Kaunas, Lithuania
| | - Gediminas Jaruševičius
- Institute of Cardiology, Lithuanian University of Health Sciences, 15 Sukileliu street, Kaunas, 50103, Lithuania
| | - Gintarė Šakalytė
- Institute of Cardiology, Lithuanian University of Health Sciences, 15 Sukileliu street, Kaunas, 50103, Lithuania
| | - Jurgita Plisienė
- Cardiology clinic, Lithuanian University of Health Sciences, 2 Eiveniu street, Kaunas, Lithuania
| | - Vaiva Lesauskaitė
- Institute of Cardiology, Lithuanian University of Health Sciences, 15 Sukileliu street, Kaunas, 50103, Lithuania
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50
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Huang JD, Wang J, Ramsey E, Leavey G, Chico TJA, Condell J. Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:8002. [PMID: 36298352 PMCID: PMC9610988 DOI: 10.3390/s22208002] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/06/2022] [Accepted: 10/13/2022] [Indexed: 06/06/2023]
Abstract
Cardiovascular disease (CVD) is the world's leading cause of mortality. There is significant interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as wearables to provide an earlier and more accurate prediction and diagnosis of heart disease. Digital health technologies that fuse AI and sensing devices may help disease prevention and reduce the substantial morbidity and mortality caused by CVD worldwide. In this review, we identify and describe recent developments in the application of digital health for CVD, focusing on AI approaches for CVD detection, diagnosis, and prediction through AI models driven by data collected from wearables. We summarise the literature on the use of wearables and AI in cardiovascular disease diagnosis, followed by a detailed description of the dominant AI approaches applied for modelling and prediction using data acquired from sensors such as wearables. We discuss the AI algorithms and models and clinical applications and find that AI and machine-learning-based approaches are superior to traditional or conventional statistical methods for predicting cardiovascular events. However, further studies evaluating the applicability of such algorithms in the real world are needed. In addition, improvements in wearable device data accuracy and better management of their application are required. Lastly, we discuss the challenges that the introduction of such technologies into routine healthcare may face.
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Affiliation(s)
- Jian-Dong Huang
- School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK
| | - Jinling Wang
- School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK
| | - Elaine Ramsey
- Department of Global Business & Enterprise, Ulster University at Magee, Londonderry BT48 7JL, UK
| | - Gerard Leavey
- School of Psychology, Ulster University at Coleraine, Londonderry BT52 1SA, UK
| | - Timothy J. A. Chico
- Department of Infection, Immunity and Cardiovascular Disease, The Medical School, The University of Sheffield, Beech Hill Road, Sheffield S10 2RX, UK
| | - Joan Condell
- School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK
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