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Li G, Zhao Z, Yu Z, Liao J, Zhang M. Machine learning for risk prediction of acute kidney injury in patients with diabetes mellitus combined with heart failure during hospitalization. Sci Rep 2025; 15:10728. [PMID: 40155666 PMCID: PMC11953463 DOI: 10.1038/s41598-025-87268-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2024] [Accepted: 01/17/2025] [Indexed: 04/01/2025] Open
Abstract
This study aimed to develop a machine learning (ML) model for predicting the risk of acute kidney injury (AKI) in diabetic patients with heart failure (HF) during hospitalization. Using data from 1,457 patients in the MIMIC-IV database, the study identified twenty independent risk factors for AKI through LASSO regression and logistic regression. Six ML algorithms were evaluated, including LightGBM, random forest, and neural networks. The LightGBM model demonstrated superior performance with the highest prediction accuracy, with AUC values of 0.973 and 0.804 in the training and validation sets, respectively. The Shapley additive explanations algorithm was used to visualize the model and identify the most relevant features for AKI risk. Clinical impact curves further confirmed the strong discriminatory ability and generalizability of the LightGBM model. This study highlights the potential of ML models, particularly LightGBM, to effectively predict AKI risk in diabetic patients with HF, enabling early identification of high-risk patients and timely interventions to improve prognosis.
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Affiliation(s)
- Guojing Li
- Department of Cardiology, Kunshan First People's Hospital, Affiliated Kunshan Hospital of Jiangsu University, No.566 Tongfeng East Road, Kunshan Development Zone, Kunshan, Suzhou City, 215300, Jiangsu Province, China
| | - Zhiqiang Zhao
- Department of Cardiology, Kunshan First People's Hospital, Affiliated Kunshan Hospital of Jiangsu University, No.566 Tongfeng East Road, Kunshan Development Zone, Kunshan, Suzhou City, 215300, Jiangsu Province, China
| | - Zongliang Yu
- Department of Cardiology, Kunshan First People's Hospital, Affiliated Kunshan Hospital of Jiangsu University, No.566 Tongfeng East Road, Kunshan Development Zone, Kunshan, Suzhou City, 215300, Jiangsu Province, China
| | - Junyi Liao
- Department of Cardiology, Kunshan First People's Hospital, Affiliated Kunshan Hospital of Jiangsu University, No.566 Tongfeng East Road, Kunshan Development Zone, Kunshan, Suzhou City, 215300, Jiangsu Province, China
| | - Mengyao Zhang
- Department of Cardiology, Kunshan First People's Hospital, Affiliated Kunshan Hospital of Jiangsu University, No.566 Tongfeng East Road, Kunshan Development Zone, Kunshan, Suzhou City, 215300, Jiangsu Province, China.
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Barreto TDO, Farias FLDO, Veras NVR, Cardoso PH, Silva GJPC, Pinheiro CDO, Medina MVB, Fernandes FRDS, Barbalho IMP, Cortez LR, dos Santos JPQ, de Morais AHF, de Souza GF, Machado GM, Lucena MJNR, Valentim RADM. Artificial intelligence applied to bed regulation in Rio Grande do Norte: Data analysis and application of machine learning on the "RegulaRN Leitos Gerais" platform. PLoS One 2024; 19:e0315379. [PMID: 39775276 PMCID: PMC11684685 DOI: 10.1371/journal.pone.0315379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2024] [Accepted: 11/25/2024] [Indexed: 01/11/2025] Open
Abstract
Bed regulation within Brazil's National Health System (SUS) plays a crucial role in managing care for patients in need of hospitalization. In Rio Grande do Norte, Brazil, the RegulaRN Leitos Gerais platform was the information system developed to register requests for bed regulation for COVID-19 cases. However, the platform was expanded to cover a range of diseases that require hospitalization. This study explored different machine learning models in the RegulaRN database, from October 2021 to January 2024, totaling 47,056 regulations. From the data obtained, 12 features were selected from the 24 available. After that, blank and inconclusive data were removed, as well as the outcomes that had values other than discharge and death, rendering a binary classification. Data was also correlated, balanced, and divided into training and test portions for application in machine learning models. The results showed better accuracy (87.77%) and recall (87.77%) for the XGBoost model, and higher precision (87.85%) and F1-Score (87.56%) for the Random Forest and Gradient Boosting models, respectively. As for Specificity (82.94%) and ROC-AUC (82.13%), the Multilayer Perceptron with SGD optimizer obtained the highest scores. The results evidenced which models could adequately assist medical regulators during the decision-making process for bed regulation, enabling even more effective regulation and, consequently, greater availability of beds and a decrease in waiting time for patients.
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Affiliation(s)
- Tiago de Oliveira Barreto
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
| | - Fernando Lucas de Oliveira Farias
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
| | - Nicolas Vinícius Rodrigues Veras
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, Rio Grande do Norte, Brazil
| | - Pablo Holanda Cardoso
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, Rio Grande do Norte, Brazil
| | | | | | | | - Felipe Ricardo dos Santos Fernandes
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
| | - Ingridy Marina Pierre Barbalho
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
| | - Lyane Ramalho Cortez
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
- Secretary of Public Health of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - João Paulo Queiroz dos Santos
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, Rio Grande do Norte, Brazil
| | - Antonio Higor Freire de Morais
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, Rio Grande do Norte, Brazil
| | - Gustavo Fontoura de Souza
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, Rio Grande do Norte, Brazil
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M. Odat R, Marsool Marsool MD, Nguyen D, Idrees M, Hussein AM, Ghabally M, A. Yasin J, Hanifa H, Sabet CJ, Dinh NH, Harky A, Jain J, Jain H. Presurgery and postsurgery: advancements in artificial intelligence and machine learning models for enhancing patient management in infective endocarditis. Int J Surg 2024; 110:7202-7214. [PMID: 39051669 PMCID: PMC11573050 DOI: 10.1097/js9.0000000000002003] [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: 04/28/2024] [Accepted: 07/15/2024] [Indexed: 07/27/2024]
Abstract
Infective endocarditis (IE) is a severe infection of the inner lining of the heart, known as the endocardium. It is characterized by a range of symptoms and has a complicated pattern of occurrence, leading to a significant number of deaths. IE poses significant diagnostic and treatment difficulties. This evaluation examines the utilization of artificial intelligence (AI) and machine learning (ML) models in addressing IE management. It focuses on the most recent advancements and possible applications. Through this paper, the authors observe that AI/ML can significantly enhance and outperform traditional diagnostic methods leading to more accurate risk stratification, personalized therapies, as well and real-time monitoring facilities. For example, early postsurgical mortality prediction models like SYSUPMIE achieved 'very good' area under the curve (AUROC) values exceeding 0.81. Additionally, AI/ML has improved diagnostic accuracy for prosthetic valve endocarditis, with PET-ML models increasing sensitivity from 59 to 72% when integrated into ESC criteria and reaching a high specificity of 83%. Furthermore, inflammatory biomarkers such as IL-15 and CCL4 have been identified as predictive markers, showing 91% accuracy in forecasting mortality, and identifying high-risk patients with specific CRP, IL-15, and CCL4 levels. Even simpler ML models, like Naïve Bayes, demonstrated an excellent accuracy of 92.30% in death rate prediction following valvular surgery for IE patients. Furthermore, this review provides a vital assessment of the advantages and disadvantages of such AI/ML models, such as better-quality decision support approaches like adaptive response systems on one hand, and data privacy threats or ethical concerns on the other hand. In conclusion, Al and ML must continue, through multicentric and validated research, to advance cardiovascular medicine, and overcome implementation challenges to boost patient outcomes and healthcare delivery.
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Affiliation(s)
- Ramez M. Odat
- Faculty of Medicine, Jordan University of Science and Technology, Irbid
| | | | - Dang Nguyen
- Massachusetts General Hospital, Corrigan Minehan Heart Center, Harvard Medical School, Boston, Massachusetts
| | | | | | - Mike Ghabally
- Division of Cardiology, Department of Internal Medicine, Faculty of Medicine, University of Aleppo, Aleppo
| | - Jehad A. Yasin
- School of Medicine, The University of Jordan, Amman, Jordan
| | - Hamdah Hanifa
- Faculty of Medicine, University of Kalamoon, Al-Nabk, Syria
| | | | - Nguyen H. Dinh
- Department of Cardiovascular and Thoracic Surgery, Faculty of Medicine, University of Medicine and Pharmacy at Ho Chi Minh City, Vietnam
| | - Amer Harky
- Department of Cardiothoracic Surgery, Liverpool Heart and Chest Hospital, Liverpool, UK
| | - Jyoti Jain
- Department of Internal Medicine, All India Institute of Medical Sciences (AIIMS), Jodhpur, India
| | - Hritvik Jain
- Department of Internal Medicine, All India Institute of Medical Sciences (AIIMS), Jodhpur, India
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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; 26:263-272. [PMID: 38780665 PMCID: PMC11457745 DOI: 10.1007/s11883-024-01210-w] [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] [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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Aghajanian S, Jafarabady K, Abbasi M, Mohammadifard F, Bakhshali Bakhtiari M, Shokouhi N, Saleh Gargari S, Bakhtiyari M. Prediction of post-delivery hemoglobin levels with machine learning algorithms. Sci Rep 2024; 14:13953. [PMID: 38886458 PMCID: PMC11183065 DOI: 10.1038/s41598-024-64278-z] [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: 12/23/2023] [Accepted: 06/06/2024] [Indexed: 06/20/2024] Open
Abstract
Predicting postpartum hemorrhage (PPH) before delivery is crucial for enhancing patient outcomes, enabling timely transfer and implementation of prophylactic therapies. We attempted to utilize machine learning (ML) using basic pre-labor clinical data and laboratory measurements to predict postpartum Hemoglobin (Hb) in non-complicated singleton pregnancies. The local databases of two academic care centers on patient delivery were incorporated into the current study. Patients with preexisting coagulopathy, traumatic cases, and allogenic blood transfusion were excluded from all analyses. The association of pre-delivery variables with 24-h post-delivery hemoglobin level was evaluated using feature selection with Elastic Net regression and Random Forest algorithms. A suite of ML algorithms was employed to predict post-delivery Hb levels. Out of 2051 pregnant women, 1974 were included in the final analysis. After data pre-processing and redundant variable removal, the top predictors selected via feature selection for predicting post-delivery Hb were parity (B: 0.09 [0.05-0.12]), gestational age, pre-delivery hemoglobin (B:0.83 [0.80-0.85]) and fibrinogen levels (B:0.01 [0.01-0.01]), and pre-labor platelet count (B*1000: 0.77 [0.30-1.23]). Among the trained algorithms, artificial neural network provided the most accurate model (Root mean squared error: 0.62), which was subsequently deployed as a web-based calculator: https://predictivecalculators.shinyapps.io/ANN-HB . The current study shows that ML models could be utilized as accurate predictors of indirect measures of PPH and can be readily incorporated into healthcare systems. Further studies with heterogenous population-based samples may further improve the generalizability of these models.
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Affiliation(s)
- Sepehr Aghajanian
- Student Research Committee, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran
- Neuroscience Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Kyana Jafarabady
- Student Research Committee, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran
| | - Mohammad Abbasi
- Student Research Committee, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran
| | - Fateme Mohammadifard
- Student Research Committee, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran
| | - Mina Bakhshali Bakhtiari
- Department of Obstetrics and Gynecology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Nasim Shokouhi
- Yas University Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Soraya Saleh Gargari
- Department of Obstetrics and Gynecology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
- Men's health and Reproductive health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Mahmood Bakhtiyari
- Department of Community Medicine, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran.
- Non-Communicable Diseases Research Center, Alborz University of Medical Sciences, Karaj, Iran.
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Tolu-Akinnawo O, Ezekwueme F, Awoyemi T. Telemedicine in Cardiology: Enhancing Access to Care and Improving Patient Outcomes. Cureus 2024; 16:e62852. [PMID: 38912070 PMCID: PMC11192510 DOI: 10.7759/cureus.62852] [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: 06/21/2024] [Indexed: 06/25/2024] Open
Abstract
Telemedicine has gained significant recognition, particularly since the COVID-19 pandemic. However, its roots date back to its significant role during major epidemic outbreaks such as severe acute respiratory syndrome (SARS), H1N1 and H7N9 influenza, and Middle East respiratory syndrome (MERS), where alternate means of accessing healthcare were adopted to combat the outbreak while limiting the spread of the virus. In Sub-Saharan Africa, telemedicine has supported healthcare delivery, patient and professional health education, disease prevention, and surveillance, starting with its first adoption in Ethiopia in 1980. In the United States, telemedicine has significantly impacted cardiology, particularly at-home monitoring programs, which have proven highly effective for patients with abnormal heart rhythms. Devices such as Holter monitors, blood pressure monitors, and implantable cardioverter-defibrillators have reduced mortality rates and hospital readmissions while improving healthcare efficiency by saving healthcare costs. However, the COVID-19 pandemic accelerated the adoption of telemedicine, as evidenced by a dramatic increase in telemedicine visits at institutions like New York University (NYU) Langone Health during and post-COVID-19 pandemic. In addition, telemedicine has also facilitated cardiac rehabilitation and improved access to specialized cardiology care in rural and underserved areas, reducing disparities in cardiovascular health outcomes. As technology advances, telemedicine is poised to play an increasingly significant role in cardiology and healthcare at large, enhancing patient management, healthcare efficiency, and cost reduction. This review underscores the significance of telemedicine in cardiology, its challenges, and future directions.
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Affiliation(s)
| | - Francis Ezekwueme
- Internal Medicine, University of Pittsburgh Medical Center, Pittsburg, USA
| | - Toluwalase Awoyemi
- Internal Medicine, Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, GBR
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Liu X, Zhang Y, Zhu H, Jia B, Wang J, He Y, Zhang H. Applications of artificial intelligence-powered prenatal diagnosis for congenital heart disease. Front Cardiovasc Med 2024; 11:1345761. [PMID: 38720920 PMCID: PMC11076681 DOI: 10.3389/fcvm.2024.1345761] [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: 12/12/2023] [Accepted: 04/08/2024] [Indexed: 05/12/2024] Open
Abstract
Artificial intelligence (AI) has made significant progress in the medical field in the last decade. The AI-powered analysis methods of medical images and clinical records can now match the abilities of clinical physicians. Due to the challenges posed by the unique group of fetuses and the dynamic organ of the heart, research into the application of AI in the prenatal diagnosis of congenital heart disease (CHD) is particularly active. In this review, we discuss the clinical questions and research methods involved in using AI to address prenatal diagnosis of CHD, including imaging, genetic diagnosis, and risk prediction. Representative examples are provided for each method discussed. Finally, we discuss the current limitations of AI in prenatal diagnosis of CHD, namely Volatility, Insufficiency and Independence (VII), and propose possible solutions.
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Affiliation(s)
- Xiangyu Liu
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
- Key Laboratory of Data Science and Intelligent Computing, International Innovation Institute, Beihang University, Hangzhou, China
| | - Yingying Zhang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
- Key Laboratory of Data Science and Intelligent Computing, International Innovation Institute, Beihang University, Hangzhou, China
| | - Haogang Zhu
- Key Laboratory of Data Science and Intelligent Computing, International Innovation Institute, Beihang University, Hangzhou, China
- State Key Laboratory of Software Development Environment, Beihang University, Beijing, China
- School of Computer Science and Engineering, Beihang University, Beijing, China
| | - Bosen Jia
- School of Biological Sciences, Victoria University of Wellington, Wellington, New Zealand
| | - Jingyi Wang
- Echocardiography Medical Center Beijing Anzhen Hospital, Capital Medical University, Beijing, China
- Maternal-Fetal Medicine Center in Fetal Heart Disease, Beijing Anzhen Hospital, Beijing, China
| | - Yihua He
- Echocardiography Medical Center Beijing Anzhen Hospital, Capital Medical University, Beijing, China
- Maternal-Fetal Medicine Center in Fetal Heart Disease, Beijing Anzhen Hospital, Beijing, China
| | - Hongjia Zhang
- Key Laboratory of Data Science and Intelligent Computing, International Innovation Institute, Beihang University, Hangzhou, China
- Beijing Lab for Cardiovascular Precision Medicine, Beijing, China
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Seringa J, Abreu J, Magalhaes T. Machine learning methods, applications and economic analysis to predict heart failure hospitalisation risk: a scoping review protocol. BMJ Open 2024; 14:e083188. [PMID: 38580361 PMCID: PMC11002361 DOI: 10.1136/bmjopen-2023-083188] [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: 12/13/2023] [Accepted: 03/22/2024] [Indexed: 04/07/2024] Open
Abstract
INTRODUCTION Machine learning (ML) has emerged as a powerful tool for uncovering patterns and generating new information. In cardiology, it has shown promising results in predictive outcomes risk assessment of heart failure (HF) patients, a chronic condition affecting over 64 million individuals globally.This scoping review aims to synthesise the evidence on ML methods, applications and economic analysis to predict the HF hospitalisation risk. METHODS AND ANALYSIS This scoping review will use the approach described by Arksey and O'Malley. This protocol will use the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Protocol, and the PRISMA extension for scoping reviews will be used to present the results. PubMed, Scopus and Web of Science are the databases that will be searched. Two reviewers will independently screen the full-text studies for inclusion and extract the data. All the studies focusing on ML models to predict the risk of hospitalisation from HF adult patients will be included. ETHICS AND DISSEMINATION Ethical approval is not required for this review. The dissemination strategy includes peer-reviewed publications, conference presentations and dissemination to relevant stakeholders.
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Affiliation(s)
- Joana Seringa
- NOVA National School of Public Health, NOVA University Lisbon, Lisbon, Portugal
- NOVA National School of Public Health, Public Health Research Centre, Comprehensive Health Research Center, CHRC, NOVA University Lisbon, Lisbon, Portugal
| | - João Abreu
- NOVA National School of Public Health, NOVA University Lisbon, Lisbon, Portugal
| | - Teresa Magalhaes
- NOVA National School of Public Health, NOVA University Lisbon, Lisbon, Portugal
- NOVA National School of Public Health, Public Health Research Centre, Comprehensive Health Research Center, CHRC, NOVA University Lisbon, Lisbon, Portugal
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Chang A, Wu X, Liu K. Deep learning from latent spatiotemporal information of the heart: Identifying advanced bioimaging markers from echocardiograms. BIOPHYSICS REVIEWS 2024; 5:011304. [PMID: 38559589 PMCID: PMC10978053 DOI: 10.1063/5.0176850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 03/01/2024] [Indexed: 04/04/2024]
Abstract
A key strength of echocardiography lies in its integration of comprehensive spatiotemporal cardiac imaging data in real-time, to aid frontline or bedside patient risk stratification and management. Nonetheless, its acquisition, processing, and interpretation are known to all be subject to heterogeneity from its reliance on manual and subjective human tracings, which challenges workflow and protocol standardization and final interpretation accuracy. In the era of advanced computational power, utilization of machine learning algorithms for big data analytics in echocardiography promises reduction in cost, cognitive errors, and intra- and inter-observer variability. Novel spatiotemporal deep learning (DL) models allow the integration of temporal arm information based on unlabeled pixel echocardiographic data for convolution of an adaptive semantic spatiotemporal calibration to construct personalized 4D heart meshes, assess global and regional cardiac function, detect early valve pathology, and differentiate uncommon cardiovascular disorders. Meanwhile, data visualization on spatiotemporal DL prediction models helps extract latent temporal imaging features to develop advanced imaging biomarkers in early disease stages and advance our understanding of pathophysiology to support the development of personalized prevention or treatment strategies. Since portable echocardiograms have been increasingly used as point-of-care imaging tools to aid rural care delivery, the application of these new spatiotemporal DL techniques show the potentials in streamlining echocardiographic acquisition, processing, and data analysis to improve workflow standardization and efficiencies, and provide risk stratification and decision supporting tools in real-time, to prompt the building of new imaging diagnostic networks to enhance rural healthcare engagement.
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Affiliation(s)
- Amanda Chang
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Iowa, Iowa City, Iowa 52242, USA
| | - Xiaodong Wu
- Department of Electrical and Computer Engineering, College of Engineering, University of Iowa, Iowa City, Iowa 52242, USA
| | - Kan Liu
- Division of Cardiology, Department of Internal Medicine, Washington University in St. Louis, St. Louis, Missouri 63110, USA
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Rudnicka Z, Pręgowska A, Glądys K, Perkins M, Proniewska K. Advancements in artificial intelligence-driven techniques for interventional cardiology. Cardiol J 2024; 31:321-341. [PMID: 38247435 PMCID: PMC11076027 DOI: 10.5603/cj.98650] [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: 12/22/2023] [Revised: 12/31/2023] [Accepted: 12/29/2023] [Indexed: 01/23/2024] Open
Abstract
This paper aims to thoroughly discuss the impact of artificial intelligence (AI) on clinical practice in interventional cardiology (IC) with special recognition of its most recent advancements. Thus, recent years have been exceptionally abundant in advancements in computational tools, including the development of AI. The application of AI development is currently in its early stages, nevertheless new technologies have proven to be a promising concept, particularly considering IC showing great impact on patient safety, risk stratification and outcomes during the whole therapeutic process. The primary goal is to achieve the integration of multiple cardiac imaging modalities, establish online decision support systems and platforms based on augmented and/or virtual realities, and finally to create automatic medical systems, providing electronic health data on patients. In a simplified way, two main areas of AI utilization in IC may be distinguished, namely, virtual and physical. Consequently, numerous studies have provided data regarding AI utilization in terms of automated interpretation and analysis from various cardiac modalities, including electrocardiogram, echocardiography, angiography, cardiac magnetic resonance imaging, and computed tomography as well as data collected during robotic-assisted percutaneous coronary intervention procedures. Thus, this paper aims to thoroughly discuss the impact of AI on clinical practice in IC with special recognition of its most recent advancements.
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Affiliation(s)
- Zofia Rudnicka
- Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Agnieszka Pręgowska
- Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Kinga Glądys
- Jagiellonian University Medical College, Krakow, Poland
| | - Mark Perkins
- Collegium Prometricum, the Business School for Healthcare, Sopot, Poland
- Royal Society of Arts, London, United Kingdom
| | - Klaudia Proniewska
- Department of Bioinformatics and Telemedicine, Jagiellonian University Medical College, Krakow, Poland.
- Center for Digital Medicine and Robotics, Jagiellonian University Medical College, Krakow, Poland.
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Houri O, Gil Y, Krispin E, Amitai-Komem D, Chen R, Hochberg A, Wiznitzer A, Hadar E. Predicting adverse perinatal outcomes among gestational diabetes complicated pregnancies using neural network algorithm. J Matern Fetal Neonatal Med 2023; 36:2286928. [PMID: 38044265 DOI: 10.1080/14767058.2023.2286928] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 11/19/2023] [Indexed: 12/05/2023]
Abstract
OBJECTIVE The primary aim of this study is to utilize a neural network model to predict adverse neonatal outcomes in pregnancies complicated by gestational diabetes (GDM). DESIGN Our model, based on XGBoost, was implemented using Python 3.6 with the Keras framework built on TensorFlow by Google. We sourced data from medical records of GDM-diagnosed individuals who delivered at our tertiary medical center between 2012 and 2016. The model included simple pregnancy parameters, maternal age, body mass index (BMI), parity, gravity, results of oral glucose tests, treatment modality, and glycemic control. The composite neonatal adverse outcomes defined as one of the following: large or small for gestational age, shoulder dystocia, fetal umbilical pH less than 7.2, neonatal intensive care unit (NICU) admission, respiratory distress syndrome (RDS), hyperbilirubinemia, or polycythemia. For the machine training phase, 70% of the cohort was randomly chosen. Each sample in this set consisted of baseline parameters and the composite outcome. The remaining samples were then employed to assess the accuracy of our model. RESULTS The study encompassed a total of 452 participants. The composite adverse outcome occurred in 29% of cases. Our model exhibited prediction accuracies of 82% at the time of GDM diagnosis and 91% at delivery. The factors most contributing to the prediction model were maternal age, pre-pregnancy BMI, and the results of the single 3-h 100 g oral glucose tolerance test. CONCLUSION Our advanced neural network algorithm has significant potential in predicting adverse neonatal outcomes in GDM-diagnosed individuals.
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Affiliation(s)
- Ohad Houri
- Helen Schneider Hospital for Women, Rabin Medical Center, Petach-Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Yotam Gil
- School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Eyal Krispin
- Helen Schneider Hospital for Women, Rabin Medical Center, Petach-Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Daphna Amitai-Komem
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Department of Obstetrics and Gynecology, Sheba Medical Center, Tel-Hashomer, Israel
| | - Rony Chen
- Helen Schneider Hospital for Women, Rabin Medical Center, Petach-Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Alyssa Hochberg
- Helen Schneider Hospital for Women, Rabin Medical Center, Petach-Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Arnon Wiznitzer
- Helen Schneider Hospital for Women, Rabin Medical Center, Petach-Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Eran Hadar
- Helen Schneider Hospital for Women, Rabin Medical Center, Petach-Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
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12
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Guo LL, Guo LY, Li J, Gu YW, Wang JY, Cui Y, Qian Q, Chen T, Jiang R, Zheng S. Characteristics and Admission Preferences of Pediatric Emergency Patients and Their Waiting Time Prediction Using Electronic Medical Record Data: Retrospective Comparative Analysis. J Med Internet Res 2023; 25:e49605. [PMID: 37910168 PMCID: PMC10652198 DOI: 10.2196/49605] [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: 06/03/2023] [Revised: 07/04/2023] [Accepted: 09/20/2023] [Indexed: 11/03/2023] Open
Abstract
BACKGROUND The growing number of patients visiting pediatric emergency departments could have a detrimental impact on the care provided to children who are triaged as needing urgent attention. Therefore, it has become essential to continuously monitor and analyze the admissions and waiting times of pediatric emergency patients. Despite the significant challenge posed by the shortage of pediatric medical resources in China's health care system, there have been few large-scale studies conducted to analyze visits to the pediatric emergency room. OBJECTIVE This study seeks to examine the characteristics and admission patterns of patients in the pediatric emergency department using electronic medical record (EMR) data. Additionally, it aims to develop and assess machine learning models for predicting waiting times for pediatric emergency department visits. METHODS This retrospective analysis involved patients who were admitted to the emergency department of Children's Hospital Capital Institute of Pediatrics from January 1, 2021, to December 31, 2021. Clinical data from these admissions were extracted from the electronic medical records, encompassing various variables of interest such as patient demographics, clinical diagnoses, and time stamps of clinical visits. These indicators were collected and compared. Furthermore, we developed and evaluated several computational models for predicting waiting times. RESULTS In total, 183,024 eligible admissions from 127,368 pediatric patients were included. During the 12-month study period, pediatric emergency department visits were most frequent among children aged less than 5 years, accounting for 71.26% (130,423/183,024) of the total visits. Additionally, there was a higher proportion of male patients (104,147/183,024, 56.90%) compared with female patients (78,877/183,024, 43.10%). Fever (50,715/183,024, 27.71%), respiratory infection (43,269/183,024, 23.64%), celialgia (9560/183,024, 5.22%), and emesis (6898/183,024, 3.77%) were the leading causes of pediatric emergency room visits. The average daily number of admissions was 501.44, and 18.76% (34,339/183,204) of pediatric emergency department visits resulted in discharge without a prescription or further tests. The median waiting time from registration to seeing a doctor was 27.53 minutes. Prolonged waiting times were observed from April to July, coinciding with an increased number of arrivals, primarily for respiratory diseases. In terms of waiting time prediction, machine learning models, specifically random forest, LightGBM, and XGBoost, outperformed regression methods. On average, these models reduced the root-mean-square error by approximately 17.73% (8.951/50.481) and increased the R2 by approximately 29.33% (0.154/0.525). The SHAP method analysis highlighted that the features "wait.green" and "department" had the most significant influence on waiting times. CONCLUSIONS This study offers a contemporary exploration of pediatric emergency room visits, revealing significant variations in admission rates across different periods and uncovering certain admission patterns. The machine learning models, particularly ensemble methods, delivered more dependable waiting time predictions. Patient volume awaiting consultation or treatment and the triage status emerged as crucial factors contributing to prolonged waiting times. Therefore, strategies such as patient diversion to alleviate congestion in emergency departments and optimizing triage systems to reduce average waiting times remain effective approaches to enhance the quality of pediatric health care services in China.
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Affiliation(s)
- Lin Lin Guo
- Children's Hospital Capital Institute of Pediatrics, Beijing, China
| | - Lin Ying Guo
- Children's Hospital Capital Institute of Pediatrics, Beijing, China
| | - Jiao Li
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yao Wen Gu
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jia Yang Wang
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ying Cui
- Children's Hospital Capital Institute of Pediatrics, Beijing, China
| | - Qing Qian
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ting Chen
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
| | - Rui Jiang
- Department of Automation, Tsinghua University, Beijing, China
| | - Si Zheng
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
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13
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Petretta M, Panico M, Mainolfi CG, Cuocolo A. Including myocardial flow reserve by PET in prediction models: Ready to fly? J Nucl Cardiol 2023; 30:2054-2057. [PMID: 37072671 DOI: 10.1007/s12350-023-03259-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 03/11/2023] [Indexed: 04/20/2023]
Affiliation(s)
- Mario Petretta
- IRCCS Synlab SDN, Via Gianturco 113, 80121, Naples, Italy
| | - Mariarosaria Panico
- Institute of Biostructure and Bioimaging, National Council of Research, Naples, Italy
| | | | - Alberto Cuocolo
- Department of Advanced Biomedical Sciences, University Federico II, Naples, Italy.
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14
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Kennedy L, Bates K, Therrien J, Grossman Y, Kodaira M, Pressacco J, Rosati A, Dagenais F, Leask RL, Lachapelle K. Thoracic Aortic Aneurysm Risk Assessment: A Machine Learning Approach. JACC. ADVANCES 2023; 2:100637. [PMID: 38938360 PMCID: PMC11198590 DOI: 10.1016/j.jacadv.2023.100637] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 05/25/2023] [Accepted: 06/20/2023] [Indexed: 06/29/2024]
Abstract
Background Traditional methods of risk assessment for thoracic aortic aneurysm (TAA) based on aneurysm size alone have been called into question as being unreliable in predicting complications. Biomechanical function of aortic tissue may be a better predictor of risk, but it is difficult to determine in vivo. Objectives This study investigates using a machine learning (ML) model as a correlative measure of energy loss, a measure of TAA biomechanical function. Methods Biaxial tensile testing was performed on resected TAA tissue collected from patients undergoing surgery. The energy loss of the tissue was calculated and used as the representative output. Input parameters were collected from clinical assessments including observations from medical scans and genetic paneling. Four ML algorithms including Gaussian process regression were trained in Matlab. Results A total of 158 patients were considered (mean age 62 years, range 22-89 years, 78% male), including 11 healthy controls. The mean ascending aortic diameter was 47 ± 10 mm, with 46% having a bicuspid aortic valve. The best-performing model was found to give a greater correlative measure to energy loss (R2 = 0.63) than the surprisingly poor performance of aortic diameter (R2 = 0.26) and indexed aortic size (R2 = 0.32). An echocardiogram-derived stiffness metric was investigated on a smaller subcohort of 67 patients as an additional input, improving the correlative performance from R2 = 0.46 to R2 = 0.62. Conclusions A preliminary set of models demonstrated the ability of a ML algorithm to improve prediction of the mechanical function of TAA tissue. This model can use clinical data to provide additional information for risk stratification.
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Affiliation(s)
- Lauren Kennedy
- Department of Chemical Engineering, McGill University, Montreal, Quebec, Canada
- Division of Cardiac Surgery, McGill University Health Centre, Montreal, Quebec, Canada
| | - Kevin Bates
- Department of Chemical Engineering, McGill University, Montreal, Quebec, Canada
- Division of Cardiac Surgery, McGill University Health Centre, Montreal, Quebec, Canada
| | - Judith Therrien
- Division of Cardiology, McGill University Health Centre, Montreal, Quebec, Canada
| | - Yoni Grossman
- Division of Cardiology, McGill University Health Centre, Montreal, Quebec, Canada
| | - Masaki Kodaira
- Division of Cardiology, McGill University Health Centre, Montreal, Quebec, Canada
| | - Josephine Pressacco
- Division of Diagnostic Radiology, McGill University Health Centre, Montreal, Quebec, Canada
| | - Anthony Rosati
- Department of Chemical Engineering, McGill University, Montreal, Quebec, Canada
- Division of Cardiac Surgery, McGill University Health Centre, Montreal, Quebec, Canada
| | - François Dagenais
- Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec, Quebec, Canada
| | - Richard L. Leask
- Department of Chemical Engineering, McGill University, Montreal, Quebec, Canada
| | - Kevin Lachapelle
- Division of Cardiac Surgery, McGill University Health Centre, Montreal, Quebec, Canada
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15
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Perillo T, de Giorgi M, Papace UM, Serino A, Cuocolo R, Manto A. Current role of machine learning and radiogenomics in precision neuro-oncology. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:545-555. [PMID: 37720347 PMCID: PMC10501892 DOI: 10.37349/etat.2023.00151] [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: 12/20/2022] [Accepted: 04/20/2023] [Indexed: 09/19/2023] Open
Abstract
In the past few years, artificial intelligence (AI) has been increasingly used to create tools that can enhance workflow in medicine. In particular, neuro-oncology has benefited from the use of AI and especially machine learning (ML) and radiogenomics, which are subfields of AI. ML can be used to develop algorithms that dynamically learn from available medical data in order to automatically do specific tasks. On the other hand, radiogenomics can identify relationships between tumor genetics and imaging features, thus possibly giving new insights into the pathophysiology of tumors. Therefore, ML and radiogenomics could help treatment tailoring, which is crucial in personalized neuro-oncology. The aim of this review is to illustrate current and possible future applications of ML and radiomics in neuro-oncology.
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Affiliation(s)
- Teresa Perillo
- Department of Neuroradiology, “Umberto I” Hospital, 84014 Norcera Inferiore, Italy
| | - Marco de Giorgi
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80138 Naples, Italy
| | - Umberto Maria Papace
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80138 Naples, Italy
| | - Antonietta Serino
- Department of Neuroradiology, “Umberto I” Hospital, 84014 Norcera Inferiore, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery, and Dentistry, University of Salerno, 84084 Fisciano, Italy
| | - Andrea Manto
- Department of Neuroradiology, “Umberto I” Hospital, 84014 Norcera Inferiore, Italy
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16
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Xie E, Sung E, Saad E, Trayanova N, Wu KC, Chrispin J. Advanced imaging for risk stratification for ventricular arrhythmias and sudden cardiac death. Front Cardiovasc Med 2022; 9:884767. [PMID: 36072882 PMCID: PMC9441865 DOI: 10.3389/fcvm.2022.884767] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 08/02/2022] [Indexed: 11/13/2022] Open
Abstract
Sudden cardiac death (SCD) is a leading cause of mortality, comprising approximately half of all deaths from cardiovascular disease. In the US, the majority of SCD (85%) occurs in patients with ischemic cardiomyopathy (ICM) and a subset in patients with non-ischemic cardiomyopathy (NICM), who tend to be younger and whose risk of mortality is less clearly delineated than in ischemic cardiomyopathies. The conventional means of SCD risk stratification has been the determination of the ejection fraction (EF), typically via echocardiography, which is currently a means of determining candidacy for primary prevention in the form of implantable cardiac defibrillators (ICDs). Advanced cardiac imaging methods such as cardiac magnetic resonance imaging (CMR), single-photon emission computerized tomography (SPECT) and positron emission tomography (PET), and computed tomography (CT) have emerged as promising and non-invasive means of risk stratification for sudden death through their characterization of the underlying myocardial substrate that predisposes to SCD. Late gadolinium enhancement (LGE) on CMR detects myocardial scar, which can inform ICD decision-making. Overall scar burden, region-specific scar burden, and scar heterogeneity have all been studied in risk stratification. PET and SPECT are nuclear methods that determine myocardial viability and innervation, as well as inflammation. CT can be used for assessment of myocardial fat and its association with reentrant circuits. Emerging methodologies include the development of "virtual hearts" using complex electrophysiologic modeling derived from CMR to attempt to predict arrhythmic susceptibility. Recent developments have paired novel machine learning (ML) algorithms with established imaging techniques to improve predictive performance. The use of advanced imaging to augment risk stratification for sudden death is increasingly well-established and may soon have an expanded role in clinical decision-making. ML could help shift this paradigm further by advancing variable discovery and data analysis.
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Affiliation(s)
- Eric Xie
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Eric Sung
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Elie Saad
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Natalia Trayanova
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Katherine C. Wu
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Jonathan Chrispin
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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17
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Pičulin M, Smole T, Žunkovič B, Kokalj E, Robnik-Šikonja M, Kukar M, Fotiadis DI, Pezoulas VC, Tachos NS, Barlocco F, Mazzarotto F, Popović D, Maier LS, Velicki L, Olivotto I, MacGowan GA, Jakovljević DG, Filipović N, Bosnić Z. Disease Progression of Hypertrophic Cardiomyopathy: Modeling Using Machine Learning. JMIR Med Inform 2022; 10:e30483. [PMID: 35107432 PMCID: PMC8851344 DOI: 10.2196/30483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 10/27/2021] [Accepted: 12/04/2021] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Cardiovascular disorders in general are responsible for 30% of deaths worldwide. Among them, hypertrophic cardiomyopathy (HCM) is a genetic cardiac disease that is present in about 1 of 500 young adults and can cause sudden cardiac death (SCD). OBJECTIVE Although the current state-of-the-art methods model the risk of SCD for patients, to the best of our knowledge, no methods are available for modeling the patient's clinical status up to 10 years ahead. In this paper, we propose a novel machine learning (ML)-based tool for predicting disease progression for patients diagnosed with HCM in terms of adverse remodeling of the heart during a 10-year period. METHODS The method consisted of 6 predictive regression models that independently predict future values of 6 clinical characteristics: left atrial size, left atrial volume, left ventricular ejection fraction, New York Heart Association functional classification, left ventricular internal diastolic diameter, and left ventricular internal systolic diameter. We supplemented each prediction with the explanation that is generated using the Shapley additive explanation method. RESULTS The final experiments showed that predictive error is lower on 5 of the 6 constructed models in comparison to experts (on average, by 0.34) or a consortium of experts (on average, by 0.22). The experiments revealed that semisupervised learning and the artificial data from virtual patients help improve predictive accuracies. The best-performing random forest model improved R2 from 0.3 to 0.6. CONCLUSIONS By engaging medical experts to provide interpretation and validation of the results, we determined the models' favorable performance compared to the performance of experts for 5 of 6 targets.
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Affiliation(s)
- Matej Pičulin
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Tim Smole
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Bojan Žunkovič
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Enja Kokalj
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Marko Robnik-Šikonja
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Matjaž Kukar
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Dimitrios I Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
| | - Vasileios C Pezoulas
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
| | - Nikolaos S Tachos
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
| | - Fausto Barlocco
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.,Cardiomyopathy Unit, Careggi University Hospital, University of Florence, Florence, Italy
| | - Francesco Mazzarotto
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.,Cardiomyopathy Unit, Careggi University Hospital, University of Florence, Florence, Italy.,National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Dejana Popović
- Clinic for Cardiology, Clinical Center of Serbia, University of Belgrade, Belgrade, Serbia
| | - Lars S Maier
- Department of Internal Medicine II (Cardiology, Pneumology, Intensive Care Medicine), University Hospital Regensburg, Regensburg, Germany
| | - Lazar Velicki
- Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia.,Institute of Cardiovascular Diseases Vojvodina, Sremska Kamenica, Serbia
| | - Iacopo Olivotto
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.,Cardiomyopathy Unit, Careggi University Hospital, University of Florence, Florence, Italy
| | - Guy A MacGowan
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Djordje G Jakovljević
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom.,Faculty of Health and Life Sciences, Coventry University, Coventry, United Kingdom
| | - Nenad Filipović
- Bioengineering Research and Development Center, Kragujevac, Serbia
| | - Zoran Bosnić
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
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18
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Radiomics in Cardiovascular Disease Imaging: from Pixels to the Heart of the Problem. CURRENT CARDIOVASCULAR IMAGING REPORTS 2022. [DOI: 10.1007/s12410-022-09563-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Abstract
Purpose of Review
This review of the literature aims to present potential applications of radiomics in cardiovascular radiology and, in particular, in cardiac imaging.
Recent Findings
Radiomics and machine learning represent a technological innovation which may be used to extract and analyze quantitative features from medical images. They aid in detecting hidden pattern in medical data, possibly leading to new insights in pathophysiology of different medical conditions. In the recent literature, radiomics and machine learning have been investigated for numerous potential applications in cardiovascular imaging. They have been proposed to improve image acquisition and reconstruction, for anatomical structure automated segmentation or automated characterization of cardiologic diseases.
Summary
The number of applications for radiomics and machine learning is continuing to rise, even though methodological and implementation issues still limit their use in daily practice. In the long term, they may have a positive impact in patient management.
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Ben Ali W, Pesaranghader A, Avram R, Overtchouk P, Perrin N, Laffite S, Cartier R, Ibrahim R, Modine T, Hussin JG. Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble. Front Cardiovasc Med 2021; 8:711401. [PMID: 34957230 PMCID: PMC8692711 DOI: 10.3389/fcvm.2021.711401] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 11/08/2021] [Indexed: 12/23/2022] Open
Abstract
Driven by recent innovations and technological progress, the increasing quality and amount of biomedical data coupled with the advances in computing power allowed for much progress in artificial intelligence (AI) approaches for health and biomedical research. In interventional cardiology, the hope is for AI to provide automated analysis and deeper interpretation of data from electrocardiography, computed tomography, magnetic resonance imaging, and electronic health records, among others. Furthermore, high-performance predictive models supporting decision-making hold the potential to improve safety, diagnostic and prognostic prediction in patients undergoing interventional cardiology procedures. These applications include robotic-assisted percutaneous coronary intervention procedures and automatic assessment of coronary stenosis during diagnostic coronary angiograms. Machine learning (ML) has been used in these innovations that have improved the field of interventional cardiology, and more recently, deep Learning (DL) has emerged as one of the most successful branches of ML in many applications. It remains to be seen if DL approaches will have a major impact on current and future practice. DL-based predictive systems also have several limitations, including lack of interpretability and lack of generalizability due to cohort heterogeneity and low sample sizes. There are also challenges for the clinical implementation of these systems, such as ethical limits and data privacy. This review is intended to bring the attention of health practitioners and interventional cardiologists to the broad and helpful applications of ML and DL algorithms to date in the field. Their implementation challenges in daily practice and future applications in the field of interventional cardiology are also discussed.
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Affiliation(s)
- Walid Ben Ali
- Service Médico-Chirurgical, Valvulopathies-Chirurgie Cardiaque-Cardiologie Interventionelle Structurelle, Hôpital Cardiologique de Haut Lévèque, Bordeaux, France.,Structural Heart Program and Interventional Cardiology, Université de Montréal, Montreal Heart Institute, Montréal, QC, Canada
| | - Ahmad Pesaranghader
- Faculty of Medicine, Research Center, Montreal Heart Institute, Université de Montréal, Montréal, QC, Canada.,Computer Science and Operations Research Department, Mila (Quebec Artificial Intelligence Institute), Montreal, QC, Canada
| | - Robert Avram
- Faculty of Medicine, Research Center, Montreal Heart Institute, Université de Montréal, Montréal, QC, Canada
| | - Pavel Overtchouk
- Interventional Cardiology and Cardiovascular Surgery Centre Hospitalier Regional Universitaire de Lille (CHRU de Lille), Lille, France
| | - Nils Perrin
- Structural Heart Program and Interventional Cardiology, Université de Montréal, Montreal Heart Institute, Montréal, QC, Canada
| | - Stéphane Laffite
- Service Médico-Chirurgical, Valvulopathies-Chirurgie Cardiaque-Cardiologie Interventionelle Structurelle, Hôpital Cardiologique de Haut Lévèque, Bordeaux, France
| | - Raymond Cartier
- Structural Heart Program and Interventional Cardiology, Université de Montréal, Montreal Heart Institute, Montréal, QC, Canada
| | - Reda Ibrahim
- Structural Heart Program and Interventional Cardiology, Université de Montréal, Montreal Heart Institute, Montréal, QC, Canada
| | - Thomas Modine
- Service Médico-Chirurgical, Valvulopathies-Chirurgie Cardiaque-Cardiologie Interventionelle Structurelle, Hôpital Cardiologique de Haut Lévèque, Bordeaux, France
| | - Julie G Hussin
- Faculty of Medicine, Research Center, Montreal Heart Institute, Université de Montréal, Montréal, QC, Canada
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A Comparison among Different Machine Learning Pretest Approaches to Predict Stress-Induced Ischemia at PET/CT Myocardial Perfusion Imaging. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:3551756. [PMID: 34873413 PMCID: PMC8643229 DOI: 10.1155/2021/3551756] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 10/29/2021] [Accepted: 11/15/2021] [Indexed: 12/30/2022]
Abstract
Traditional approach for predicting coronary artery disease (CAD) is based on demographic data, symptoms such as chest pain and dyspnea, and comorbidity related to cardiovascular diseases. Usually, these variables are analyzed by logistic regression to quantifying their relationship with the outcome; nevertheless, their predictive value is limited. In the present study, we aimed to investigate the value of different machine learning (ML) techniques for the evaluation of suspected CAD; having as gold standard, the presence of stress-induced ischemia by 82Rb positron emission tomography/computed tomography (PET/CT) myocardial perfusion imaging (MPI) ML was chosen on their clinical use and on the fact that they are representative of different classes of algorithms, such as deterministic (Support vector machine and Naïve Bayes), adaptive (ADA and AdaBoost), and decision tree (Random Forest, rpart, and XGBoost). The study population included 2503 consecutive patients, who underwent MPI for suspected CAD. To testing ML performances, data were split randomly into two parts: training/test (80%) and validation (20%). For training/test, we applied a 5-fold cross-validation, repeated 2 times. With this subset, we performed the tuning of free parameters for each algorithm. For all metrics, the best performance in training/test was observed for AdaBoost. The Naïve Bayes ML resulted to be more efficient in validation approach. The logistic and rpart algorithms showed similar metric values for the training/test and validation approaches. These results are encouraging and indicate that the ML algorithms can improve the evaluation of pretest probability of stress-induced myocardial ischemia.
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21
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Stevens BR, Pepine CJ. Emerging role of machine learning in cardiovascular disease investigation and translations. AMERICAN HEART JOURNAL PLUS : CARDIOLOGY RESEARCH AND PRACTICE 2021; 11:100050. [PMID: 38559318 PMCID: PMC10978128 DOI: 10.1016/j.ahjo.2021.100050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 08/02/2021] [Accepted: 09/01/2021] [Indexed: 04/04/2024]
Abstract
Unexpected insights and practical advances in cardiovascular disease (CVD) are being discovered by rapidly advancing developments in supercomputers and machine learning (ML) software algorithms. These have been accelerated during the COVID-19 pandemic, and the resulting CVD translational implications of ML are steering new measures of prevention and treatment, new tools for objective clinical diagnosis, and even opportunities for rethinking basic foundations of CVD nosology. As the usual cardiovascular specialist may not be familiar with these tools, the editor has invited this brief overview.
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Affiliation(s)
- Bruce R. Stevens
- Department of Physiology and Functional Genomics, University of Florida College of Medicine, Gainesville, FL, USA
| | - Carl J. Pepine
- Division of Cardiovascular Medicine, University of Florida College of Medicine, Gainesville, FL, USA
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Norori N, Hu Q, Aellen FM, Faraci FD, Tzovara A. Addressing bias in big data and AI for health care: A call for open science. PATTERNS (NEW YORK, N.Y.) 2021; 2:100347. [PMID: 34693373 PMCID: PMC8515002 DOI: 10.1016/j.patter.2021.100347] [Citation(s) in RCA: 191] [Impact Index Per Article: 47.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Artificial intelligence (AI) has an astonishing potential in assisting clinical decision making and revolutionizing the field of health care. A major open challenge that AI will need to address before its integration in the clinical routine is that of algorithmic bias. Most AI algorithms need big datasets to learn from, but several groups of the human population have a long history of being absent or misrepresented in existing biomedical datasets. If the training data is misrepresentative of the population variability, AI is prone to reinforcing bias, which can lead to fatal outcomes, misdiagnoses, and lack of generalization. Here, we describe the challenges in rendering AI algorithms fairer, and we propose concrete steps for addressing bias using tools from the field of open science.
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Affiliation(s)
- Natalia Norori
- Institute of Computer Science, University of Bern, Neubrückstrasse 10 3012 Bern, Switzerland
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 1UD, UK
| | - Qiyang Hu
- Institute of Computer Science, University of Bern, Neubrückstrasse 10 3012 Bern, Switzerland
| | | | - Francesca Dalia Faraci
- Institute of Digital Technologies for Personalized Healthcare (MeDiTech), Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, 6962 Lugano, Switzerland
| | - Athina Tzovara
- Institute of Computer Science, University of Bern, Neubrückstrasse 10 3012 Bern, Switzerland
- Sleep Wake Epilepsy Center | NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA 94720, USA
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Extracting Features from Poincaré Plots to Distinguish Congestive Heart Failure Patients According to NYHA Classes. Bioengineering (Basel) 2021; 8:bioengineering8100138. [PMID: 34677211 PMCID: PMC8533203 DOI: 10.3390/bioengineering8100138] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 09/29/2021] [Accepted: 09/30/2021] [Indexed: 11/30/2022] Open
Abstract
Heart-rate variability has proved a valid tool in prognosis definition of patients with congestive heart failure (CHF). Previous research has documented Poincaré plot analysis as a valuable approach to study heart-rate variability performance among different subjects. In this paper, we explored the possibility to feed machine-learning (ML) algorithms using unconventional quantitative parameters extracted from Poincaré plots (generated from 24-h electrocardiogram recordings) to classify patients with CHF belonging to different New York Heart Association (NYHA) classes. We performed in sequence the following investigations: first, a statistical analysis was carried out on 9 morphological parameters, automatically measured from Poincaré plots. Subsequently, a feature selection through a wrapper with a 10-fold cross-validation method was performed to find the best subset of features which maximized the classification accuracy for each considered ML algorithm. Finally, patient classification was assessed through a ML analysis using AdaBoost of Decision Tree, k-Nearest Neighbors and Naive Bayes algorithms. A univariate statistical analysis proved 5 out of 9 parameters presented statistically significant differences among patients of distinct NYHA classes; similarly, a multivariate logistic regression confirmed the importance of the parameter ρy in the separability between low-risk and high-risk classes. The ML analysis achieved promising results in terms of evaluation metrics (especially the Naive Bayes algorithm), with accuracies greater than 80% and Area Under the Receiver Operating Curve indices greater than 0.7 for the overall three algorithms. The study indicates the proposed features have a predictive power to discriminate the NYHA classes, to which the features seem evenly correlated. Despite the NYHA classification being subjective and easily recognized by cardiologists, the potential relevance in the clinical cardiology of the proposed features and the promising ML results implies the methodology could be a valuable approach to automatically classify CHF. Future investigations on enriched datasets may further confirm the presented evidence.
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Kim JH, Yee J, Chang BC, Gwak HS. Gene Polymorphisms of the Renin-Angiotensin System and Bleeding Complications of Warfarin: Genetic-Based Machine Learning Models. Pharmaceuticals (Basel) 2021; 14:ph14080824. [PMID: 34451921 PMCID: PMC8400908 DOI: 10.3390/ph14080824] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 08/12/2021] [Accepted: 08/17/2021] [Indexed: 12/12/2022] Open
Abstract
This study aimed to investigate the effects of genetic variants and haplotypes in the renin–angiotensin system (RAS) on the risk of warfarin-induced bleeding complications at therapeutic international normalized ratios (INRs). Four single nucleotide polymorphisms (SNPs) of AGT, two SNPs of REN, three SNPs of ACE, four SNPs of AGTR1, and one SNP of AGTR2, in addition to VKORC1 and CYP2C9 variants, were investigated. We utilized logistic regression and several machine learning methods for bleeding prediction. The study included 142 patients, among whom 21 experienced bleeding complications. We identified a haplotype, H2 (TCG), carrying three single nucleotide polymorphisms (SNPs) of ACE (rs1800764, rs4341, and rs4353), which showed a significant relation with bleeding complications. After adjusting covariates, patients with H2/H2 experienced a 0.12-fold (95% CI 0.02–0.99) higher risk of bleeding complications than the others. In addition, G allele carriers of AGT rs5050 and A allele carriers of AGTR1 rs2640543 had 5.0- (95% CI 1.8–14.1) and 3.2-fold (95% CI 1.1–8.9) increased risk of bleeding complications compared with the TT genotype and GG genotype carriers, respectively. The AUROC values (mean, 95% CI) across 10 random iterations using five-fold cross-validated multivariate logistic regression, elastic net, random forest, support vector machine (SVM)–linear kernel, and SVM–radial kernel models were 0.732 (0.694–0.771), 0.741 (0.612–0.870), 0.723 (0.589–0.857), 0.673 (0.517–0.828), and 0.680 (0.528–0.832), respectively. The highest quartile group (≥75th percentile) of weighted risk score had approximately 12.0 times (95% CI 3.1–46.7) increased risk of bleeding, compared to the 25–75th percentile group, respectively. This study demonstrated that RAS-related polymorphisms, including the H2 haplotype of the ACE gene, could affect bleeding complications during warfarin treatment for patients with mechanical heart valves. Our results could be used to develop individually tailored intervention strategies to prevent warfarin-induced bleeding.
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Affiliation(s)
- Joo-Hee Kim
- Institute of Pharmaceutical Science and Technology, College of Pharmacy, Ajou University, 206 Worldcup-ro, Yeongtong-gu, Suwon 16499, Korea;
| | - Jeong Yee
- Graduate School of Pharmaceutical Sciences, College of Pharmacy, Ewha Womans University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul 03760, Korea;
| | - Byung-Chul Chang
- Bundang CHA Medical Center, Department of Thoracic and Cardiovascular Surgery, CHA University, 59 Yatap-ro, Bundang-gu, Seongnam 13496, Korea;
- Yonsei University Medical Center, Department of Thoracic & Cardiovascular Surgery, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea
| | - Hye-Sun Gwak
- Graduate School of Pharmaceutical Sciences, College of Pharmacy, Ewha Womans University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul 03760, Korea;
- Correspondence: ; Tel.: +82-2-3277-4376; Fax: +82-2-3277-2851
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Castaldo A, De Lucia DR, Pontillo G, Gatti M, Cocozza S, Ugga L, Cuocolo R. State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma. Diagnostics (Basel) 2021; 11:1194. [PMID: 34209197 PMCID: PMC8307071 DOI: 10.3390/diagnostics11071194] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/24/2021] [Accepted: 06/24/2021] [Indexed: 12/12/2022] Open
Abstract
The most common liver malignancy is hepatocellular carcinoma (HCC), which is also associated with high mortality. Often HCC develops in a chronic liver disease setting, and early diagnosis as well as accurate screening of high-risk patients is crucial for appropriate and effective management of these patients. While imaging characteristics of HCC are well-defined in the diagnostic phase, challenging cases still occur, and current prognostic and predictive models are limited in their accuracy. Radiomics and machine learning (ML) offer new tools to address these issues and may lead to scientific breakthroughs with the potential to impact clinical practice and improve patient outcomes. In this review, we will present an overview of these technologies in the setting of HCC imaging across different modalities and a range of applications. These include lesion segmentation, diagnosis, prognostic modeling and prediction of treatment response. Finally, limitations preventing clinical application of radiomics and ML at the present time are discussed, together with necessary future developments to bring the field forward and outside of a purely academic endeavor.
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Affiliation(s)
- Anna Castaldo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Davide Raffaele De Lucia
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Giuseppe Pontillo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Marco Gatti
- Radiology Unit, Department of Surgical Sciences, University of Turin, 10124 Turin, Italy;
| | - Sirio Cocozza
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Renato Cuocolo
- Department of Clinical Medicine and Surgery, University of Naples “Federico II”, 80131 Naples, Italy
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Sabottke C, Breaux M, Lee R, Foreman A, Spieler B. Analysis of Potential for User Errors in Mobile Deployment of Radiology Deep Learning for Cardiac Rhythm Device Detection. J Digit Imaging 2021; 34:572-580. [PMID: 33742333 PMCID: PMC8329116 DOI: 10.1007/s10278-021-00443-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 11/28/2020] [Accepted: 03/01/2021] [Indexed: 11/28/2022] Open
Abstract
We examine how convolutional neural networks (CNNs) for cardiac rhythm device detection can exhibit failures in performance under suboptimal deployment scenarios and examine how medically adversarial image presentation can further impair neural network performance. We validated the publicly available Pacemaker-ID web server and mobile app on 43 local hospital emergency department (ED) cases of patients presenting with a cardiac rhythm device on anterior-posterior (AP) chest radiograph and assessed performance using Cohen's kappa coefficient for inter-rater reliability. To illustrate adversarial performance concerns, we then produced example CNN models using the 65,379 patient MIMIC-CXR chest radiograph retrospective database and evaluated performance with area under the receiver operating characteristic (AUROC). In retrospective review of 43 patients with cardiac rhythm devices on AP chest radiographs during our study period (January 1, 2020 to March 1, 2020), 74.4% (32/43) had device manufacturer information readily available within the electronic medical record. A total of 25.6% of patients (11/43) did not have this information documented in the patient chart and could ostensibly benefit from CNN-based identification of device manufacturer. For patients with known device manufacturer, the Pacemaker-ID prediction was accurate in 87.5% of cases (28/32). Mobile app accuracy varied from 62.5 to 93.75% depending on image capture settings and presentation. Cohen's kappa coefficient varied from 0.448 to 0.897 depending on mobile image capture conditions. For our additional analysis of medically adversarial performance failures with a DenseNet121 trained on MIMIC-CXR images, we showed that an AUROC of 0.9807 ± 0.0051 could be achieved on an example testing dataset while masking a 30% false positive rate in identification of cardiac rhythm devices versus clinically distinct entities such as vagal nerve stimulators. Despite the promise of CNN approaches for cardiac rhythm device analysis on chest radiographs, further study is warranted to assess potential for errors driven by user misuse when deploying these models to mobile devices as well as for cases when performance can be impaired by the presence of other support apparatuses.
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Affiliation(s)
- Carl Sabottke
- Department of Medical Imaging, University of Arizona College of Medicine, Tucson, AZ, USA.
| | - Marc Breaux
- School of Medicine, Louisiana State University Health Sciences Center, New Orleans, LA, USA
| | - Rebecca Lee
- Department of Internal Medicine, University Hospital and Clinics, Lafayette, LA, USA
| | - Adam Foreman
- Department of Neurology, Lafayette General Medical Center, Lafayette, LA, USA
| | - Bradley Spieler
- Department of Radiology, Louisiana State University Health Sciences Center, New Orleans, LA, USA
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Computational Models Used to Predict Cardiovascular Complications in Chronic Kidney Disease Patients: A Systematic Review. ACTA ACUST UNITED AC 2021; 57:medicina57060538. [PMID: 34072159 PMCID: PMC8227302 DOI: 10.3390/medicina57060538] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 05/25/2021] [Accepted: 05/26/2021] [Indexed: 12/11/2022]
Abstract
Background and objectives: cardiovascular complications (CVC) are the leading cause of death in patients with chronic kidney disease (CKD). Standard cardiovascular disease risk prediction models used in the general population are not validated in patients with CKD. We aim to systematically review the up-to-date literature on reported outcomes of computational methods such as artificial intelligence (AI) or regression-based models to predict CVC in CKD patients. Materials and methods: the electronic databases of MEDLINE/PubMed, EMBASE, and ScienceDirect were systematically searched. The risk of bias and reporting quality for each study were assessed against transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) and the prediction model risk of bias assessment tool (PROBAST). Results: sixteen papers were included in the present systematic review: 15 non-randomized studies and 1 ongoing clinical trial. Twelve studies were found to perform AI or regression-based predictions of CVC in CKD, either through single or composite endpoints. Four studies have come up with computational solutions for other CV-related predictions in the CKD population. Conclusions: the identified studies represent palpable trends in areas of clinical promise with an encouraging present-day performance. However, there is a clear need for more extensive application of rigorous methodologies. Following the future prospective, randomized clinical trials, and thorough external validations, computational solutions will fill the gap in cardiovascular predictive tools for chronic kidney disease.
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Machine learning algorithms for predicting coronary artery disease: efforts toward an open source solution. Future Sci OA 2021; 7:FSO698. [PMID: 34046201 PMCID: PMC8147740 DOI: 10.2144/fsoa-2020-0206] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Aim: The development of coronary artery disease (CAD), a highly prevalent disease worldwide, is influenced by several modifiable risk factors. Predictive models built using machine learning (ML) algorithms may assist clinicians in timely detection of CAD and may improve outcomes. Materials & methods: In this study, we applied six different ML algorithms to predict the presence of CAD amongst patients listed in ‘the Cleveland dataset.’ The generated computer code is provided as a working open source solution with the ultimate goal to achieve a viable clinical tool for CAD detection. Results: All six ML algorithms achieved accuracies greater than 80%, with the ‘neural network’ algorithm achieving accuracy greater than 93%. The recall achieved with the ‘neural network’ model is also the highest of the six models (0.93), indicating that predictive ML models may provide diagnostic value in CAD. Coronary artery disease (CAD) is correlated with many preventable risk factors. Early diagnosis of CAD allows for prevention of worsening of CAD and its complications. This study aims to utilize machine learning (ML) algorithms to predict for CAD in patients. Our results indicate that ML algorithms can accurately predict for CAD. Furthermore, by providing our code publicly, we hope to improve the ability for ML algorithms as a diagnostic tool for CAD.
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Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis. Neuroradiology 2021; 63:1293-1304. [PMID: 33649882 PMCID: PMC8295153 DOI: 10.1007/s00234-021-02668-0] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 02/03/2021] [Indexed: 02/07/2023]
Abstract
Purpose To systematically review and evaluate the methodological quality of studies using radiomics for diagnostic and predictive purposes in patients with intracranial meningioma. To perform a meta-analysis of machine learning studies for the prediction of intracranial meningioma grading from pre-operative brain MRI. Methods Articles published from the year 2000 on radiomics and machine learning applications in brain imaging of meningioma patients were included. Their methodological quality was assessed by three readers with the radiomics quality score, using the intra-class correlation coefficient (ICC) to evaluate inter-reader reproducibility. A meta-analysis of machine learning studies for the preoperative evaluation of meningioma grading was performed and their risk of bias was assessed with the Quality Assessment of Diagnostic Accuracy Studies tool. Results In all, 23 studies were included in the systematic review, 8 of which were suitable for the meta-analysis. Total (possible range, −8 to 36) and percentage radiomics quality scores were respectively 6.96 ± 4.86 and 19 ± 13% with a moderate to good inter-reader reproducibility (ICC = 0.75, 95% confidence intervals, 95%CI = 0.54–0.88). The meta-analysis showed an overall AUC of 0.88 (95%CI = 0.84–0.93) with a standard error of 0.02. Conclusions Machine learning and radiomics have been proposed for multiple applications in the imaging of meningiomas, with promising results for preoperative lesion grading. However, future studies with adequate standardization and higher methodological quality are required prior to their introduction in clinical practice. Supplementary Information The online version contains supplementary material available at 10.1007/s00234-021-02668-0.
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Pang L, Liu Z, Wei F, Cai C, Yang X. Improving cardiotoxicity prediction in cancer treatment: integration of conventional circulating biomarkers and novel exploratory tools. Arch Toxicol 2021; 95:791-805. [PMID: 33219404 DOI: 10.1007/s00204-020-02952-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 11/05/2020] [Indexed: 12/31/2022]
Abstract
Early detection strategies and improvements in cancer treatment have dramatically reduced the cancer mortality rate in the United States (US). However, cardiovascular (CV) side effects of cancer therapy are frequent among the 17 million cancer survivors in the US today, and cardiovascular disease (CVD) has become the second leading cause of morbidity and mortality among cancer survivors. Circulating biomarkers are ideal for detecting and monitoring CV side effects of cancer therapy. Here, we summarize the current state of clinical studies on conventional serum and plasma CVD biomarkers to detect and prevent cardiac injury during cancer treatment. We also review how novel exploratory tools such as genetic testing, human stem cell-derived cardiomyocytes, Omics technologies, and artificial intelligence can elucidate underlying molecular and genetic mechanisms of CV injury and to improve predicting cancer therapy-related cardiotoxicity (CTRC). Current regulatory requirements for biomarker qualifications are also addressed. We present generally applicable lessons learned from published studies, particularly on how to improve reproducibility. The combination of conventional circulating biomarkers and novel exploratory tools will pave the way for precision medicine and improve the clinical practice of prediction, detection, and management of CTRC.
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Affiliation(s)
- Li Pang
- Division of Systems Biology, National Center for Toxicological Research, US. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA.
| | - Zhichao Liu
- Division of Bioinformation and Biostatistics, National Center for Toxicological Research, US. Food and Drug Administration, Jefferson, AR, USA
| | - Feng Wei
- Department of Structural Heart Disease, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China
| | - Chengzhong Cai
- Division of Systems Biology, National Center for Toxicological Research, US. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA
| | - Xi Yang
- Division of Pharmacology & Toxicology, Office of Cardiology, Hematology, Endocrinology, & Nephrology, Office of New Drug, Center for Drug Evaluation and Research, US. Food and Drug Administration, Silver Spring, MD, USA
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Machine Learning in Electrocardiography and Echocardiography: Technological Advances in Clinical Cardiology. Curr Cardiol Rep 2020; 22:161. [PMID: 33037949 DOI: 10.1007/s11886-020-01416-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/03/2020] [Indexed: 02/07/2023]
Abstract
PURPOSE OF REVIEW Electrocardiography (ECG) and echocardiography are the most widely used diagnostic tools in clinical cardiology. This review focuses on recent advancements in applying machine learning (ML) in ECG and echocardiography and potential synergistic ML integration of ECG and echocardiography. RECENT FINDINGS ML algorithms have been used in ECG for technical quality assurance, arrhythmia identification, and prognostic predictions, and in echocardiography to recognize image views, quantify measurements, and identify pathologic patterns. Synergistic application of ML in ECG and echocardiograph has demonstrated the potential to optimize therapeutic response, improve risk stratification, and generate new disease classification. There is mounting evidence that ML potentially outperforms in disease diagnoses and outcome prediction with ECG and echocardiography when compared with trained healthcare professionals. The applications of ML in ECG and echocardiography are playing increasingly greater roles in medical research and clinical practice, particularly for their contributions to developing novel diagnostic/prognostic prediction models. The automation in data acquisition, processing, and interpretation help streamline the workflows of ECG and echocardiography in contemporary cardiology practice.
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Guerriero E, Ugga L, Cuocolo R. Artificial intelligence and pituitary adenomas: A review. Artif Intell Med Imaging 2020; 1:70-77. [DOI: 10.35711/aimi.v1.i2.70] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 07/15/2020] [Accepted: 08/21/2020] [Indexed: 02/06/2023] Open
Abstract
The aim of this review was to provide an overview of the main concepts in machine learning (ML) and to analyze the ML applications in the imaging of pituitary adenomas. After describing the clinical, pathological and imaging features of pituitary tumors, we defined the difference between ML and classical rule-based algorithms, we illustrated the fundamental ML techniques: supervised, unsupervised and reinforcement learning and explained the characteristic of deep learning, a ML approach employing networks inspired by brain’s structure. Pre-treatment assessment and neurosurgical outcome prediction were the potential ML applications using magnetic resonance imaging. Regarding pre-treatment assessment, ML methods were used to have information about tumor consistency, predict cavernous sinus invasion and high proliferative index, discriminate null cell adenomas, which respond to neo-adjuvant radiotherapy from other subtypes, predict somatostatin analogues response and visual pathway injury. Regarding neurosurgical outcome prediction, the following applications were discussed: Gross total resection prediction, evaluation of Cushing disease recurrence after transsphenoidal surgery and prediction of cerebrospinal fluid fistula’s formation after surgery. Although clinical applicability requires more replicability, generalizability and validation, results are promising, and ML software can be a potential power to facilitate better clinical decision making in pituitary tumor patients.
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Affiliation(s)
- Elvira Guerriero
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples 80131, Italy
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples 80131, Italy
| | - Renato Cuocolo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples 80131, Italy
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Artificial Intelligence (AI) and Cardiovascular Diseases: An Unexpected Alliance. Cardiol Res Pract 2020; 2020:4972346. [PMID: 32676206 PMCID: PMC7336209 DOI: 10.1155/2020/4972346] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Accepted: 06/10/2020] [Indexed: 12/13/2022] Open
Abstract
Cardiovascular disease (CVD), despite the significant advances in the diagnosis and treatments, still represents the leading cause of morbidity and mortality worldwide. In order to improve and optimize CVD outcomes, artificial intelligence techniques have the potential to radically change the way we practice cardiology, especially in imaging, offering us novel tools to interpret data and make clinical decisions. AI techniques such as machine learning and deep learning can also improve medical knowledge due to the increase of the volume and complexity of the data, unlocking clinically relevant information. Likewise, the use of emerging communication and information technologies is becoming pivotal to create a pervasive healthcare service through which elderly and chronic disease patients can receive medical care at their home, reducing hospitalizations and improving quality of life. The aim of this review is to describe the contemporary state of artificial intelligence and digital health applied to cardiovascular medicine as well as to provide physicians with their potential not only in cardiac imaging but most of all in clinical practice.
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