1
|
Elias P, Jain SS, Poterucha T, Randazzo M, Lopez Jimenez F, Khera R, Perez M, Ouyang D, Pirruccello J, Salerno M, Einstein AJ, Avram R, Tison GH, Nadkarni G, Natarajan V, Pierson E, Beecy A, Kumaraiah D, Haggerty C, Avari Silva JN, Maddox TM. Artificial Intelligence for Cardiovascular Care-Part 1: Advances: JACC Review Topic of the Week. J Am Coll Cardiol 2024; 83:2472-2486. [PMID: 38593946 DOI: 10.1016/j.jacc.2024.03.400] [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: 03/01/2024] [Accepted: 03/14/2024] [Indexed: 04/11/2024]
Abstract
Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on automating measurements, enhancing image quality, and detecting diseases using novel methods. Applications span wearables, electrocardiograms, echocardiography, angiography, genetics, and more. AI models detect diseases from electrocardiograms at accuracy not previously achieved by technology or human experts, including reduced ejection fraction, valvular heart disease, and other cardiomyopathies. However, AI's unique characteristics necessitate rigorous validation by addressing training methods, real-world efficacy, equity concerns, and long-term reliability. Despite an exponentially growing number of studies in cardiovascular AI, trials showing improvement in outcomes remain lacking. A number are currently underway. Embracing this rapidly evolving technology while setting a high evaluation benchmark will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.
Collapse
Affiliation(s)
- Pierre Elias
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA; Department of Biomedical Informatics Columbia University Irving Medical Center, New York, New York, USA
| | - Sneha S Jain
- Division of Cardiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - Timothy Poterucha
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Michael Randazzo
- Division of Cardiology, University of Chicago Medical Center, Chicago, Illinois, USA
| | | | - Rohan Khera
- Division of Cardiology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Marco Perez
- Division of Cardiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - David Ouyang
- Division of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - James Pirruccello
- Division of Cardiology, University of California-San Francisco, San Francisco, California, USA
| | - Michael Salerno
- Division of Cardiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - Andrew J Einstein
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Robert Avram
- Division of Cardiology, Montreal Heart Institute, Montreal, Quebec, Canada
| | - Geoffrey H Tison
- Division of Cardiology, University of California-San Francisco, San Francisco, California, USA
| | - Girish Nadkarni
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Emma Pierson
- Department of Computer Science, Cornell Tech, New York, New York, USA
| | - Ashley Beecy
- NewYork-Presbyterian Health System, New York, New York, USA; Division of Cardiology, Weill Cornell Medical College, New York, New York, USA
| | - Deepa Kumaraiah
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA; NewYork-Presbyterian Health System, New York, New York, USA
| | - Chris Haggerty
- Department of Biomedical Informatics Columbia University Irving Medical Center, New York, New York, USA; NewYork-Presbyterian Health System, New York, New York, USA
| | - Jennifer N Avari Silva
- Division of Cardiology, Washington University School of Medicine, St Louis, Missouri, USA
| | - Thomas M Maddox
- Division of Cardiology, Washington University School of Medicine, St Louis, Missouri, USA.
| |
Collapse
|
2
|
Seetharam K, Thyagaturu H, Ferreira GL, Patel A, Patel C, Elahi A, Pachulski R, Shah J, Mir P, Thodimela A, Pala M, Thet Z, Hamirani Y. Broadening Perspectives of Artificial Intelligence in Echocardiography. Cardiol Ther 2024; 13:267-279. [PMID: 38703292 PMCID: PMC11093957 DOI: 10.1007/s40119-024-00368-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 04/11/2024] [Indexed: 05/06/2024] Open
Abstract
Echocardiography frequently serves as the first-line treatment of diagnostic imaging for several pathological entities in cardiology. Artificial intelligence (AI) has been growing substantially in information technology and various commercial industries. Machine learning (ML), a branch of AI, has been shown to expand the capabilities and potential of echocardiography. ML algorithms expand the field of echocardiography by automated assessment of the ejection fraction and left ventricular function, integrating novel approaches such as speckle tracking or tissue Doppler echocardiography or vector flow mapping, improved phenotyping, distinguishing between cardiac conditions, and incorporating information from mobile health and genomics. In this review article, we assess the impact of AI and ML in echocardiography.
Collapse
Affiliation(s)
- Karthik Seetharam
- Division of Cardiovascular Disease, West Virgina University, Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA.
- Wyckoff Heights Medical Center, Brooklyn, NY, USA.
| | - Harshith Thyagaturu
- Division of Cardiovascular Disease, West Virgina University, Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA
| | | | - Aditya Patel
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Chinmay Patel
- University of Pittsburg Medical Center, Harrisburg, PA, USA
| | - Asim Elahi
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Roman Pachulski
- St. John's Episcopal Hospital - South Shore, New York, NY, USA
| | - Jilan Shah
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Parvez Mir
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | | | - Manya Pala
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Zeyar Thet
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Yasmin Hamirani
- Robert Woods Johnson University Hospital/Rutgers University, New Brusnwick, NJ, USA
| |
Collapse
|
3
|
Wazzan AA, Taconne M, Rolle VL, Forsaa MI, Haugaa KH, Galli E, Hernandez A, Edvardsen T, Donal E. Risk profiles for ventricular arrhythmias in hypertrophic cardiomyopathy through clustering analysis including left ventricular strain. Int J Cardiol 2024; 409:132167. [PMID: 38797198 DOI: 10.1016/j.ijcard.2024.132167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Revised: 04/21/2024] [Accepted: 05/11/2024] [Indexed: 05/29/2024]
Abstract
AIMS The prediction of ventricular arrhythmia (VA) in hypertrophic cardiomyopathy (HCM) remains challenging. We sought to characterize the VA risk profile in HCM patients through clustering analysis combining clinical and conventional imaging parameters with information derived from left ventricular longitudinal strain analysis (LV-LS). METHODS A total of 434 HCM patients (65% men, mean age 56 years) were included from two referral centers and followed longitudinally (mean duration 6 years). Mechanical and temporal parameters were automatically extracted from the LV-LS segmental curves of each patient in addition to conventional clinical and imaging data. A total of 287 features were analyzed using a clustering approach (k-means). The principal endpoint was VA. RESULTS 4 clusters were identified with a higher rhythmic risk for clusters 1 and 4 (VA rates of 26%(28/108), 13%(13/97), 12%(14/120), and 31%(34/109) for cluster 1,2,3 and 4 respectively). These 4 clusters differed mainly by LV-mechanics with a severe and homogeneous decrease of myocardial deformation for cluster 4, a small decrease for clusters 2 and 3 and a marked deformation delay and temporal dispersion for cluster 1 associated with a moderate decrease of the GLS (p < 0.0001 for GLS comparison between clusters). Patients from cluster 4 had the most severe phenotype (mean LV mass index 123 vs. 112 g/m2; p = 0.0003) with LV and left atrium (LA) remodeling (LA-volume index (LAVI) 46.6 vs. 41.5 ml/m2, p = 0.04 and LVEF 59.7 vs. 66.3%, p < 0.001) and impaired exercise capacity (% predicted peak VO2 58.6 vs. 69.5%; p = 0.025). CONCLUSION Processing LV-LS parameters in HCM patients 4 clusters with specific LV-strain patterns and different rhythmic risk levels are identified. Automatic extraction and analysis of LV strain parameters improves the risk stratification for VA in HCM patients.
Collapse
Affiliation(s)
- Adrien Al Wazzan
- Department of Cardiology, University of Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, Rennes, France.
| | - Marion Taconne
- Department of Cardiology, University of Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, Rennes, France.
| | - Virginie Le Rolle
- Department of Cardiology, University of Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, Rennes, France.
| | - Marianne Inngjerdingen Forsaa
- Department of Cardiology, University of Oslo, Oslo University Hospital, ProCardio Center for Innovation, Oslo, Norway
| | - Kristina Hermann Haugaa
- Department of Cardiology, University of Oslo, Oslo University Hospital, ProCardio Center for Innovation, Oslo, Norway.
| | - Elena Galli
- Department of Cardiology, University of Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, Rennes, France.
| | - Alfredo Hernandez
- Department of Cardiology, University of Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, Rennes, France.
| | - Thor Edvardsen
- Department of Cardiology, University of Oslo, Oslo University Hospital, ProCardio Center for Innovation, Oslo, Norway.
| | - Erwan Donal
- Department of Cardiology, University of Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, Rennes, France.
| |
Collapse
|
4
|
Johnson CL, Leeson P. Are We Ready to Let AI Replace the Human "Eye" When Looking for Wall Motion Abnormalities? J Am Soc Echocardiogr 2024:S0894-7317(24)00229-3. [PMID: 38761986 DOI: 10.1016/j.echo.2024.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 05/10/2024] [Indexed: 05/20/2024]
Affiliation(s)
- Casey L Johnson
- Oxford Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Paul Leeson
- Oxford Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom.
| |
Collapse
|
5
|
Barkas F, Sener YZ, Golforoush PA, Kheirkhah A, Rodriguez-Sanchez E, Novak J, Apellaniz-Ruiz M, Akyea RK, Bianconi V, Ceasovschih A, Chee YJ, Cherska M, Chora JR, D'Oria M, Demikhova N, Kocyigit Burunkaya D, Rimbert A, Macchi C, Rathod K, Roth L, Sukhorukov V, Stoica S, Scicali R, Storozhenko T, Uzokov J, Lupo MG, van der Vorst EPC, Porsch F. Advancements in risk stratification and management strategies in primary cardiovascular prevention. Atherosclerosis 2024; 395:117579. [PMID: 38824844 DOI: 10.1016/j.atherosclerosis.2024.117579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 04/29/2024] [Accepted: 05/14/2024] [Indexed: 06/04/2024]
Abstract
Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of morbidity and mortality worldwide, highlighting the urgent need for advancements in risk assessment and management strategies. Although significant progress has been made recently, identifying and managing apparently healthy individuals at a higher risk of developing atherosclerosis and those with subclinical atherosclerosis still poses significant challenges. Traditional risk assessment tools have limitations in accurately predicting future events and fail to encompass the complexity of the atherosclerosis trajectory. In this review, we describe novel approaches in biomarkers, genetics, advanced imaging techniques, and artificial intelligence that have emerged to address this gap. Moreover, polygenic risk scores and imaging modalities such as coronary artery calcium scoring, and coronary computed tomography angiography offer promising avenues for enhancing primary cardiovascular risk stratification and personalised intervention strategies. On the other hand, interventions aiming against atherosclerosis development or promoting plaque regression have gained attention in primary ASCVD prevention. Therefore, the potential role of drugs like statins, ezetimibe, proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, omega-3 fatty acids, antihypertensive agents, as well as glucose-lowering and anti-inflammatory drugs are also discussed. Since findings regarding the efficacy of these interventions vary, further research is still required to elucidate their mechanisms of action, optimize treatment regimens, and determine their long-term effects on ASCVD outcomes. In conclusion, advancements in strategies addressing atherosclerosis prevention and plaque regression present promising avenues for enhancing primary ASCVD prevention through personalised approaches tailored to individual risk profiles. Nevertheless, ongoing research efforts are imperative to refine these strategies further and maximise their effectiveness in safeguarding cardiovascular health.
Collapse
Affiliation(s)
- Fotios Barkas
- Department of Internal Medicine, Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina, Greece.
| | - Yusuf Ziya Sener
- Department of Internal Medicine, Faculty of Medicine, Hacettepe University, Ankara, Turkey
| | | | - Azin Kheirkhah
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Elena Rodriguez-Sanchez
- Division of Cardiology, Department of Medicine, Department of Physiology, and Molecular Biology Institute, UCLA, Los Angeles, CA, USA
| | - Jan Novak
- 2(nd) Department of Internal Medicine, St. Anne's University Hospital in Brno and Faculty of Medicine of Masaryk University, Brno, Czech Republic; Department of Physiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Maria Apellaniz-Ruiz
- Genomics Medicine Unit, Navarra Institute for Health Research - IdiSNA, Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Pamplona, Spain
| | - Ralph Kwame Akyea
- Centre for Academic Primary Care, School of Medicine, University of Nottingham, United Kingdom
| | - Vanessa Bianconi
- Department of Medicine and Surgery, University of Perugia, Italy
| | - Alexandr Ceasovschih
- Internal Medicine Department, Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania
| | - Ying Jie Chee
- Department of Endocrinology, Tan Tock Seng Hospital, Singapore
| | - Mariia Cherska
- Cardiology Department, Institute of Endocrinology and Metabolism, Kyiv, Ukraine
| | - Joana Rita Chora
- Unidade I&D, Grupo de Investigação Cardiovascular, Departamento de Promoção da Saúde e Doenças Não Transmissíveis, Instituto Nacional de Saúde Doutor Ricardo Jorge, Lisboa, Portugal; Universidade de Lisboa, Faculdade de Ciências, BioISI - Biosystems & Integrative Sciences Institute, Lisboa, Portugal
| | - Mario D'Oria
- Division of Vascular and Endovascular Surgery, Department of Medical Surgical and Health Sciences, University of Trieste, Trieste, Italy
| | - Nadiia Demikhova
- Sumy State University, Sumy, Ukraine; Tallinn University of Technology, Tallinn, Estonia
| | | | - Antoine Rimbert
- Nantes Université, CNRS, INSERM, l'institut du Thorax, Nantes, France
| | - Chiara Macchi
- Department of Pharmacological and Biomolecular Sciences "Rodolfo Paoletti", Università Degli Studi di Milano, Milan, Italy
| | - Krishnaraj Rathod
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom; Barts Interventional Group, Barts Heart Centre, St. Bartholomew's Hospital, London, United Kingdom
| | - Lynn Roth
- Laboratory of Physiopharmacology, University of Antwerp, Antwerp, Belgium
| | - Vasily Sukhorukov
- Laboratory of Cellular and Molecular Pathology of Cardiovascular System, Petrovsky National Research Centre of Surgery, Moscow, Russia
| | - Svetlana Stoica
- "Victor Babes" University of Medicine and Pharmacy, Timisoara, Romania; Institute of Cardiovascular Diseases Timisoara, Timisoara, Romania
| | - Roberto Scicali
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
| | - Tatyana Storozhenko
- Cardiovascular Center Aalst, OLV Clinic, Aalst, Belgium; Department of Prevention and Treatment of Emergency Conditions, L.T. Malaya Therapy National Institute NAMSU, Kharkiv, Ukraine
| | - Jamol Uzokov
- Republican Specialized Scientific Practical Medical Center of Therapy and Medical Rehabilitation, Tashkent, Uzbekistan
| | | | - Emiel P C van der Vorst
- Institute for Molecular Cardiovascular Research (IMCAR), RWTH Aachen University, 52074, Aachen, Germany; Aachen-Maastricht Institute for CardioRenal Disease (AMICARE), RWTH Aachen University, 52074, Aachen, Germany; Institute for Cardiovascular Prevention (IPEK), Ludwig-Maximilians-University Munich, 80336, Munich, Germany; Interdisciplinary Center for Clinical Research (IZKF), RWTH Aachen University, 52074, Aachen, Germany
| | - Florentina Porsch
- Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria
| |
Collapse
|
6
|
Zhang Y, Bos E, Clarkin O, Wilson T, Small GR, Wells RG, Lu L, Chow BJW. Interpretation of SPECT wall motion with deep learning. J Nucl Cardiol 2024:101881. [PMID: 38723886 DOI: 10.1016/j.nuclcard.2024.101881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 03/12/2024] [Accepted: 05/01/2024] [Indexed: 05/27/2024]
Abstract
OBJECTIVES We sought to develop a novel deep learning (DL) workflow to interpret single-photon emission computed tomography (SPECT) wall motion. BACKGROUND Wall motion assessment with SPECT is limited by image temporal and spatial resolution. Visual interpretation of wall motion can be subjective and prone to error. Artificial intelligence (AI) may improve accuracy of wall motion assessment. METHODS A total of 1038 patients undergoing rest electrocardiogram (ECG)-gated SPECT and echocardiography were included. Using echocardiography as truth, a DL-model (DL-model 1) was trained to predict the probability of abnormal wall motion. Of the 1038 patients, 317 were used to train a DL-model (DL-model 2) to assess regional wall motion. A 10-fold cross-validation was adopted. Diagnostic performance of DL was compared with human readers and quantitative parameters. RESULTS The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) of DL model (AUC: .82 [95% CI: .79-.85]; ACC: .88) were higher than human (AUC: .77 [95% CI: .73-.81]; ACC: .82; P < .001) and quantitative parameter (AUC: .74 [95% CI: .66-.81]; ACC: .78; P < .05). The net reclassification index (NRI) was 7.7%. The AUC and accuracy of DL model for per-segment and per-vessel territory diagnosis were also higher than human reader. The DL model generated results within 30 seconds with operable guided user interface (GUI) and therefore could provide preliminary interpretation. CONCLUSIONS DL can be used to improve interpretation of rest SPECT wall motion as compared with current human readers and quantitative parameter diagnosis.
Collapse
Affiliation(s)
- Yangmei Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Emma Bos
- Department of Physics, Engineering Physics & Astronomy, Queen's University, Canada
| | - Owen Clarkin
- Department of Medicine (Cardiology), University of Ottawa Heart Institute, Canada
| | - Tyler Wilson
- Department of Applied Science in Computer Engineering, Queen's University, Canada
| | - Gary R Small
- Department of Medicine (Cardiology), University of Ottawa Heart Institute, Canada
| | - R Glenn Wells
- Department of Medicine (Cardiology), University of Ottawa Heart Institute, Canada
| | - Lijun Lu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China; Pazhou Lab, Guangzhou, China
| | - Benjamin J W Chow
- Department of Medicine (Cardiology), University of Ottawa Heart Institute, Canada; Department of Radiology, University of Ottawa, Canada.
| |
Collapse
|
7
|
Zhang X, Zhang B, Zhang F. Stenosis Detection and Quantification of Coronary Artery Using Machine Learning and Deep Learning. Angiology 2024; 75:405-416. [PMID: 37399509 DOI: 10.1177/00033197231187063] [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] [Indexed: 07/05/2023]
Abstract
The aim of this review is to introduce some applications of artificial intelligence (AI) algorithms for the detection and quantification of coronary stenosis using computed tomography angiography (CTA). The realization of automatic/semi-automatic stenosis detection and quantification includes the following steps: vessel central axis extraction, vessel segmentation, stenosis detection, and quantification. Many new AI techniques, such as machine learning and deep learning, have been widely used in medical image segmentation and stenosis detection. This review also summarizes the recent progress regarding coronary stenosis detection and quantification, and discusses the development trends in this field. Through evaluation and comparison, researchers can better understand the research frontier in related fields, compare the advantages and disadvantages of various methods, and better optimize the new technologies. Machine learning and deep learning will promote the process of automatic detection and quantification of coronary artery stenosis. However, the machine learning and the deep learning methods need a large amount of data, so they also face some challenges because of the lack of professional image annotations (manually add labels by experts).
Collapse
Affiliation(s)
- Xinhong Zhang
- School of Software, Henan University, Kaifeng, China
| | - Boyan Zhang
- School of Software, Henan University, Kaifeng, China
| | - Fan Zhang
- Huaihe Hospital, Henan University, Kaifeng, China
| |
Collapse
|
8
|
Tamura Y, Nomura A, Kagiyama N, Mizuno A, Node K. Digitalomics, digital intervention, and designing future: The next frontier in cardiology. J Cardiol 2024; 83:318-322. [PMID: 38135148 DOI: 10.1016/j.jjcc.2023.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 12/10/2023] [Accepted: 12/15/2023] [Indexed: 12/24/2023]
Abstract
The discipline of cardiology stands at a transformative juncture, primarily influenced by the surge in digital health technologies. These innovations hold the promise to redefine the realms of cardiovascular research and patient care, ushering in an era of individualized and data-driven treatments. This review delves into the heart of this evolution, introducing a comprehensive design for the future of cardiology. Emphasizing the emerging domains of "digitalomics" and "digital intervention", it explores how the integration of patient data, artificial intelligence-enabled diagnostics, and telehealth can lead to more streamlined and personalized cardiovascular health. The "digital-twin" model, a highlight of this approach, encapsulates individual patient characteristics, allowing for targeted treatments. The role of interdisciplinary collaboration in cardiovascular medicine is also underlined, emphasizing the importance of merging traditional cardiology with technological advancements. The convergence of traditional cardiology methods and digital health technologies, facilitated by a transdisciplinary approach, is set to chart a new course in cardiovascular health, emphasizing individualized care and improved clinical outcomes.
Collapse
Affiliation(s)
- Yuichi Tamura
- Pulmonary Hypertension Center, International University of Health and Welfare Mita Hospital, Tokyo, Japan; Department of Cardiology International University of Health and Welfare School of Medicine Narita, Japan; Cardiointelligence Inc., Tokyo, Japan.
| | - Akihiro Nomura
- College of Transdisciplinary Sciences for Innovation, Kanazawa University, Kanazawa, Japan; Department of Cardiovascular Medicine, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan; Frontier Institute of Tourism Sciences, Kanazawa University, Kanazawa, Japan; Department of Biomedical Informatics, CureApp Institute, Karuizawa, Japan
| | - Nobuyuki Kagiyama
- Department of Digital Health and Telemedicine R&D, Juntendo University, Tokyo, Japan; Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Atsushi Mizuno
- Department of Cardiovascular Medicine, St. Luke's International Hospital, Tokyo, Japan; Leonard Davis Institute for Health Economics, University of Pennsylvania, PA, USA
| | - Koichi Node
- Department of Cardiovascular Medicine, Saga University, Saga, Japan
| |
Collapse
|
9
|
Medhi D, Kamidi SR, Mamatha Sree KP, Shaikh S, Rasheed S, Thengu Murichathil AH, Nazir Z. Artificial Intelligence and Its Role in Diagnosing Heart Failure: A Narrative Review. Cureus 2024; 16:e59661. [PMID: 38836155 PMCID: PMC11148729 DOI: 10.7759/cureus.59661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/04/2024] [Indexed: 06/06/2024] Open
Abstract
Heart failure (HF) is prevalent globally. It is a dynamic disease with varying definitions and classifications due to multiple pathophysiologies and etiologies. The diagnosis, clinical staging, and treatment of HF become complex and subjective, impacting patient prognosis and mortality. Technological advancements, like artificial intelligence (AI), have been significant roleplays in medicine and are increasingly used in cardiovascular medicine to transform drug discovery, clinical care, risk prediction, diagnosis, and treatment. Medical and surgical interventions specific to HF patients rely significantly on early identification of HF. Hospitalization and treatment costs for HF are high, with readmissions increasing the burden. AI can help improve diagnostic accuracy by recognizing patterns and using them in multiple areas of HF management. AI has shown promise in offering early detection and precise diagnoses with the help of ECG analysis, advanced cardiac imaging, leveraging biomarkers, and cardiopulmonary stress testing. However, its challenges include data access, model interpretability, ethical concerns, and generalizability across diverse populations. Despite these ongoing efforts to refine AI models, it suggests a promising future for HF diagnosis. After applying exclusion and inclusion criteria, we searched for data available on PubMed, Google Scholar, and the Cochrane Library and found 150 relevant papers. This review focuses on AI's significant contribution to HF diagnosis in recent years, drastically altering HF treatment and outcomes.
Collapse
Affiliation(s)
- Diptiman Medhi
- Internal Medicine, Gauhati Medical College and Hospital, Guwahati, Guwahati, IND
| | | | | | - Shifa Shaikh
- Cardiology, SMBT Institute of Medical Sciences and Research Centre, Igatpuri, IND
| | - Shanida Rasheed
- Emergency Medicine, East Sussex Healthcare NHS Trust, Eastbourne, GBR
| | | | - Zahra Nazir
- Internal Medicine, Combined Military Hospital, Quetta, Quetta, PAK
| |
Collapse
|
10
|
Parsa S, Saleh A, Raygor V, Hoeting N, Rao A, Navar AM, Rohatgi A, Kay F, Abbara S, Khera A, Joshi PH. Measurement and Application of Incidentally Detected Coronary Calcium: JACC Review Topic of the Week. J Am Coll Cardiol 2024; 83:1557-1567. [PMID: 38631775 DOI: 10.1016/j.jacc.2024.01.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 04/19/2024]
Abstract
Coronary artery calcium (CAC) scoring is a powerful tool for atherosclerotic cardiovascular disease risk stratification. The nongated, noncontrast chest computed tomography scan (NCCT) has emerged as a source of CAC characterization with tremendous potential due to the high volume of NCCT scans. Application of incidental CAC characterization from NCCT has raised questions around score accuracy, standardization of methodology including the possibility of deep learning to automate the process, and the risk stratification potential of an NCCT-derived score. In this review, the authors aim to summarize the role of NCCT-derived CAC in preventive cardiovascular health today as well as explore future avenues for eventual clinical applicability in specific patient populations and broader health systems.
Collapse
Affiliation(s)
- Shyon Parsa
- Department of Internal Medicine, Division of Cardiology, the UT Southwestern Medical Center, Dallas, Texas, USA; Department of Internal Medicine, Stanford University Hospital, Stanford, California, USA
| | - Adam Saleh
- Texas A&M University, Engineering Medicine, Houston, Texas, USA
| | - Viraj Raygor
- Sutter Health, Cardiovascular Health, Palo Alto, California, USA
| | - Natalie Hoeting
- Department of Internal Medicine, Division of Cardiology, the UT Southwestern Medical Center, Dallas, Texas, USA
| | - Anjali Rao
- Department of Internal Medicine, Division of Cardiology, the UT Southwestern Medical Center, Dallas, Texas, USA
| | - Ann Marie Navar
- Department of Internal Medicine, Division of Cardiology, the UT Southwestern Medical Center, Dallas, Texas, USA
| | - Anand Rohatgi
- Department of Internal Medicine, Division of Cardiology, the UT Southwestern Medical Center, Dallas, Texas, USA
| | - Fernando Kay
- Department of Radiology, Division of Cardiothoracic Imaging, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Suhny Abbara
- Department of Radiology, Division of Cardiothoracic Imaging, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Amit Khera
- Department of Internal Medicine, Division of Cardiology, the UT Southwestern Medical Center, Dallas, Texas, USA
| | - Parag H Joshi
- Department of Internal Medicine, Division of Cardiology, the UT Southwestern Medical Center, Dallas, Texas, USA.
| |
Collapse
|
11
|
Kapoor MC. Emerging Role of Artificial Intelligence in Echocardiography. Ann Card Anaesth 2024; 27:99-100. [PMID: 38607872 PMCID: PMC11095777 DOI: 10.4103/aca.aca_12_24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 01/12/2024] [Indexed: 04/14/2024] Open
Affiliation(s)
- Mukul Chandra Kapoor
- Department of Anesthesiology and Critical Care, Amrita School of Medicine and Amrita Institute of Medical Sciences, Faridabad, Haryana, India
| |
Collapse
|
12
|
VanDecker WA. The Integrative Sport of Cardiac Imaging and Clinical Cardiology: Machine Augmentation and an Evolving Odyssey. JACC Cardiovasc Imaging 2024:S1936-878X(24)00079-2. [PMID: 38613557 DOI: 10.1016/j.jcmg.2024.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 02/13/2024] [Indexed: 04/15/2024]
Affiliation(s)
- William A VanDecker
- Lewis Katz School of Medicine at Temple University, Philadelphia, Pennsylvania, USA.
| |
Collapse
|
13
|
Lekadir K. A deep learning solution to detect left ventricular structural abnormalities with chest X-rays: towards trustworthy AI in cardiology. Eur Heart J 2024:ehad775. [PMID: 38527415 DOI: 10.1093/eurheartj/ehad775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/27/2024] Open
Affiliation(s)
- Karim Lekadir
- University of Barcelona, Department of Mathematics and Computer Science, Artificial Intelligence in Medicine Lab (BCN-AIM), Barcelona, Spain
| |
Collapse
|
14
|
Miller RJH, Slomka PJ. Artificial Intelligence in Nuclear Cardiology: An Update and Future Trends. Semin Nucl Med 2024:S0001-2998(24)00015-1. [PMID: 38521708 DOI: 10.1053/j.semnuclmed.2024.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 02/19/2024] [Indexed: 03/25/2024]
Abstract
Myocardial perfusion imaging (MPI), using either single photon emission computed tomography (SPECT) or positron emission tomography (PET), is one of the most commonly ordered cardiac imaging tests, with prominent clinical roles for disease diagnosis and risk prediction. Artificial intelligence (AI) could potentially play a role in many steps along the typical MPI workflow, from image acquisition through to clinical reporting and risk estimation. AI can be utilized to improve image quality, reducing radiation exposure and image acquisition times. Once images are acquired, AI can help optimize motion correction and image registration during image reconstruction or provide direct image attenuation correction. Utilizing these image sets, AI can segment a number of anatomic features from associated computed tomographic imaging or even generate synthetic attenuation imaging. Lastly, AI may play an important role in disease diagnosis or risk prediction by combining the large number of potentially important clinical, stress, and imaging-related variables. This review will focus on the most recent developments in the field, providing clinicians and researchers with a timely update on the field. Additionally, it will discuss future trends including applications of AI during multiple points of the typical MPI workflow to maximize clinical utility and methods to maximize the information that can be obtained from hybrid imaging.
Collapse
Affiliation(s)
- Robert J H Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA; Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA.
| |
Collapse
|
15
|
Wang J, Zhang B, Wang Y, Zhou C, Vonsky MS, Mitrofanova LB, Zou D, Li Q. CrossU-Net: Dual-modality cross-attention U-Net for segmentation of precancerous lesions in gastric cancer. Comput Med Imaging Graph 2024; 112:102339. [PMID: 38262134 DOI: 10.1016/j.compmedimag.2024.102339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 10/20/2023] [Accepted: 01/15/2024] [Indexed: 01/25/2024]
Abstract
Gastric precancerous lesions (GPL) significantly elevate the risk of gastric cancer, and precise diagnosis and timely intervention are critical for patient survival. Due to the elusive pathological features of precancerous lesions, the early detection rate is less than 10%, which hinders lesion localization and diagnosis. In this paper, we provide a GPL pathological dataset and propose a novel method for improving the segmentation accuracy on a limited-scale dataset, namely RGB and Hyperspectral dual-modal pathological image Cross-attention U-Net (CrossU-Net). Specifically, we present a self-supervised pre-training model for hyperspectral images to serve downstream segmentation tasks. Secondly, we design a dual-stream U-Net-based network to extract features from different modal images. To promote information exchange between spatial information in RGB images and spectral information in hyperspectral images, we customize the cross-attention mechanism between the two networks. Furthermore, we use an intermediate agent in this mechanism to improve computational efficiency. Finally, we add a distillation loss to align predicted results for both branches, improving network generalization. Experimental results show that our CrossU-Net achieves accuracy and Dice of 96.53% and 91.62%, respectively, for GPL lesion segmentation, providing a promising spectral research approach for the localization and subsequent quantitative analysis of pathological features in early diagnosis.
Collapse
Affiliation(s)
- Jiansheng Wang
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China; Engineering Research Center of Nanophotonics & Advanced Instrument, Ministry of Education, East China Normal University, Shanghai, China
| | - Benyan Zhang
- Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yan Wang
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China
| | - Chunhua Zhou
- Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Maxim S Vonsky
- D.I. Mendeleev Institute for Metrology, Moskovsky Pr 19, St Petersburg, Russia; Almazov National Medical Research Centre, Saint-Petersburg, Russia
| | | | - Duowu Zou
- Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qingli Li
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China; Engineering Research Center of Nanophotonics & Advanced Instrument, Ministry of Education, East China Normal University, Shanghai, China; Engineering Center of SHMEC for Space Information and GNSS, Shanghai, China.
| |
Collapse
|
16
|
Çap M, Ramasamy A, Parasa R, Tanboga IH, Maung S, Morgan K, Yap NAL, Abou Gamrah M, Sokooti H, Kitslaar P, Reiber JHC, Dijkstra J, Torii R, Moon JC, Mathur A, Baumbach A, Pugliese F, Bourantas CV. Efficacy of human experts and an automated segmentation algorithm in quantifying disease pathology in coronary computed tomography angiography: A head-to-head comparison with intravascular ultrasound imaging. J Cardiovasc Comput Tomogr 2024; 18:142-153. [PMID: 38143234 DOI: 10.1016/j.jcct.2023.12.007] [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: 08/16/2023] [Revised: 11/26/2023] [Accepted: 12/18/2023] [Indexed: 12/26/2023]
Abstract
BACKGROUND Coronary computed tomography angiography (CCTA) analysis is currently performed by experts and is a laborious process. Fully automated edge-detection methods have been developed to expedite CCTA segmentation however their use is limited as there are concerns about their accuracy. This study aims to compare the performance of an automated CCTA analysis software and the experts using near-infrared spectroscopy-intravascular ultrasound imaging (NIRS-IVUS) as a reference standard. METHODS Fifty-one participants (150 vessels) with chronic coronary syndrome who underwent CCTA and 3-vessel NIRS-IVUS were included. CCTA analysis was performed by an expert and an automated edge detection method and their estimations were compared to NIRS-IVUS at a segment-, lesion-, and frame-level. RESULTS Segment-level analysis demonstrated a similar performance of the two CCTA analyses (conventional and automatic) with large biases and limits of agreement compared to NIRS-IVUS estimations for the total atheroma (ICC: 0.55 vs 0.25, mean difference:192 (-102-487) vs 243 (-132-617) and percent atheroma volume (ICC: 0.30 vs 0.12, mean difference: 12.8 (-5.91-31.6) vs 20.0 (0.79-39.2). Lesion-level analysis showed that the experts were able to detect more accurately lesions than the automated method (68.2 % and 60.7 %) however both analyses had poor reliability in assessing the minimal lumen area (ICC 0.44 vs 0.36) and the maximum plaque burden (ICC 0.33 vs 0.33) when NIRS-IVUS was used as the reference standard. CONCLUSIONS Conventional and automated CCTA analyses had similar performance in assessing coronary artery pathology using NIRS-IVUS as a reference standard. Therefore, automated segmentation can be used to expedite CCTA analysis and enhance its applications in clinical practice.
Collapse
Affiliation(s)
- Murat Çap
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University London, UK; Department of Cardiology, University of Health Sciences Diyarbakır Gazi Yaşargil Education and Research Hospital, Diyarbakır, Turkey.
| | - Anantharaman Ramasamy
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University London, UK
| | - Ramya Parasa
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University London, UK; Department of Cardiology, The Essex Cardiothoracic Centre, Basildon, UK
| | - Ibrahim H Tanboga
- Istanbul Nisantasi University Medical School, Department of Cardiology & Biostatistics, Istanbul, Turkey
| | - Soe Maung
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK
| | - Kimberley Morgan
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University London, UK
| | - Nathan A L Yap
- Barts and the London School of Medicine and Dentistry, London, UK
| | | | | | | | - Johan H C Reiber
- Medis Medical Imaging, Leiden, the Netherlands; Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Jouke Dijkstra
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Ryo Torii
- Department of Mechanical Engineering, University College London, London, UK
| | - James C Moon
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Institute of Cardiovascular Sciences, University College London, London, UK
| | - Anthony Mathur
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University London, UK
| | - Andreas Baumbach
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University London, UK
| | - Francesca Pugliese
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University London, UK
| | - Christos V Bourantas
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University London, UK; Institute of Cardiovascular Sciences, University College London, London, UK.
| |
Collapse
|
17
|
Gala D, Behl H, Shah M, Makaryus AN. The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature. Healthcare (Basel) 2024; 12:481. [PMID: 38391856 PMCID: PMC10887513 DOI: 10.3390/healthcare12040481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 02/13/2024] [Accepted: 02/14/2024] [Indexed: 02/24/2024] Open
Abstract
Cardiovascular diseases exert a significant burden on the healthcare system worldwide. This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology. AI has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding treatments, and monitoring patients, which can lead to improved patient outcomes and a more efficient healthcare system. Moreover, clinical decision support systems in cardiology have improved significantly over the past decade. The addition of AI to these clinical decision support systems can improve patient outcomes by processing large amounts of data, identifying subtle associations, and providing a timely, evidence-based recommendation to healthcare professionals. Lastly, the application of AI allows for personalized care by utilizing predictive models and generating patient-specific treatment plans. However, there are several challenges associated with the use of AI in healthcare. The application of AI in healthcare comes with significant cost and ethical considerations. Despite these challenges, AI will be an integral part of healthcare delivery in the near future, leading to personalized patient care, improved physician efficiency, and anticipated better outcomes.
Collapse
Affiliation(s)
- Dhir Gala
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands
| | - Haditya Behl
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands
| | - Mili Shah
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands
| | - Amgad N Makaryus
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hofstra University, 500 Hofstra Blvd., Hempstead, NY 11549, USA
- Department of Cardiology, Nassau University Medical Center, Hempstead, NY 11554, USA
| |
Collapse
|
18
|
Paciorek AM, von Schacky CE, Foreman SC, Gassert FG, Gassert FT, Kirschke JS, Laugwitz KL, Geith T, Hadamitzky M, Nadjiri J. Automated assessment of cardiac pathologies on cardiac MRI using T1-mapping and late gadolinium phase sensitive inversion recovery sequences with deep learning. BMC Med Imaging 2024; 24:43. [PMID: 38350900 PMCID: PMC10865672 DOI: 10.1186/s12880-024-01217-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 01/30/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND A deep learning (DL) model that automatically detects cardiac pathologies on cardiac MRI may help streamline the diagnostic workflow. To develop a DL model to detect cardiac pathologies on cardiac MRI T1-mapping and late gadolinium phase sensitive inversion recovery (PSIR) sequences were used. METHODS Subjects in this study were either diagnosed with cardiac pathology (n = 137) including acute and chronic myocardial infarction, myocarditis, dilated cardiomyopathy, and hypertrophic cardiomyopathy or classified as normal (n = 63). Cardiac MR imaging included T1-mapping and PSIR sequences. Subjects were split 65/15/20% for training, validation, and hold-out testing. The DL models were based on an ImageNet pretrained DenseNet-161 and implemented using PyTorch and fastai. Data augmentation with random rotation and mixup was applied. Categorical cross entropy was used as the loss function with a cyclic learning rate (1e-3). DL models for both sequences were developed separately using similar training parameters. The final model was chosen based on its performance on the validation set. Gradient-weighted class activation maps (Grad-CAMs) visualized the decision-making process of the DL model. RESULTS The DL model achieved a sensitivity, specificity, and accuracy of 100%, 38%, and 88% on PSIR images and 78%, 54%, and 70% on T1-mapping images. Grad-CAMs demonstrated that the DL model focused its attention on myocardium and cardiac pathology when evaluating MR images. CONCLUSIONS The developed DL models were able to reliably detect cardiac pathologies on cardiac MR images. The diagnostic performance of T1 mapping alone is particularly of note since it does not require a contrast agent and can be acquired quickly.
Collapse
Affiliation(s)
- Aleksandra M Paciorek
- Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany.
| | - Claudio E von Schacky
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Sarah C Foreman
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Felix G Gassert
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Florian T Gassert
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Jan S Kirschke
- TUM-Neuroimaging Center, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Karl-Ludwig Laugwitz
- Department of Medicine I, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Tobias Geith
- Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Martin Hadamitzky
- Department of Radiology, German Heart Center Munich, Technical University of Munich, Lazarettstraße 36, 80636, Munich, Germany
| | - Jonathan Nadjiri
- Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| |
Collapse
|
19
|
Hanneman K, Playford D, Dey D, van Assen M, Mastrodicasa D, Cook TS, Gichoya JW, Williamson EE, Rubin GD. Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association. Circulation 2024; 149:e296-e311. [PMID: 38193315 DOI: 10.1161/cir.0000000000001202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Abstract
Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved in implementing AI in cardiovascular imaging are highly diverse, varying by imaging modality, patient subtype, features to be extracted and analyzed, and clinical application. This article establishes a framework that defines value from an organizational perspective, followed by value chain analysis to identify the activities in which AI might produce the greatest incremental value creation. The various perspectives that should be considered are highlighted, including clinicians, imagers, hospitals, patients, and payers. Integrating the perspectives of all health care stakeholders is critical for creating value and ensuring the successful deployment of AI tools in a real-world setting. Different AI tools are summarized, along with the unique aspects of AI applications to various cardiac imaging modalities, including cardiac computed tomography, magnetic resonance imaging, and positron emission tomography. AI is applicable and has the potential to add value to cardiovascular imaging at every step along the patient journey, from selecting the more appropriate test to optimizing image acquisition and analysis, interpreting the results for classification and diagnosis, and predicting the risk for major adverse cardiac events.
Collapse
|
20
|
Vrudhula A, Kwan AC, Ouyang D, Cheng S. Machine Learning and Bias in Medical Imaging: Opportunities and Challenges. Circ Cardiovasc Imaging 2024; 17:e015495. [PMID: 38377237 PMCID: PMC10883605 DOI: 10.1161/circimaging.123.015495] [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] [Indexed: 02/22/2024]
Abstract
Bias in health care has been well documented and results in disparate and worsened outcomes for at-risk groups. Medical imaging plays a critical role in facilitating patient diagnoses but involves multiple sources of bias including factors related to access to imaging modalities, acquisition of images, and assessment (ie, interpretation) of imaging data. Machine learning (ML) applied to diagnostic imaging has demonstrated the potential to improve the quality of imaging-based diagnosis and the precision of measuring imaging-based traits. Algorithms can leverage subtle information not visible to the human eye to detect underdiagnosed conditions or derive new disease phenotypes by linking imaging features with clinical outcomes, all while mitigating cognitive bias in interpretation. Importantly, however, the application of ML to diagnostic imaging has the potential to either reduce or propagate bias. Understanding the potential gain as well as the potential risks requires an understanding of how and what ML models learn. Common risks of propagating bias can arise from unbalanced training, suboptimal architecture design or selection, and uneven application of models. Notwithstanding these risks, ML may yet be applied to improve gain from imaging across all 3A's (access, acquisition, and assessment) for all patients. In this review, we present a framework for understanding the balance of opportunities and challenges for minimizing bias in medical imaging, how ML may improve current approaches to imaging, and what specific design considerations should be made as part of efforts to maximize the quality of health care for all.
Collapse
Affiliation(s)
- Amey Vrudhula
- Icahn School of Medicine at Mount Sinai, New York
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center
| | - Alan C Kwan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center
| |
Collapse
|
21
|
Han PL, Jiang L, Cheng JL, Shi K, Huang S, Jiang Y, Jiang L, Xia Q, Li YY, Zhu M, Li K, Yang ZG. Artificial intelligence-assisted diagnosis of congenital heart disease and associated pulmonary arterial hypertension from chest radiographs: A multi-reader multi-case study. Eur J Radiol 2024; 171:111277. [PMID: 38160541 DOI: 10.1016/j.ejrad.2023.111277] [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: 09/08/2023] [Revised: 12/10/2023] [Accepted: 12/20/2023] [Indexed: 01/03/2024]
Abstract
OBJECTIVES To explore the possibility of automatic diagnosis of congenital heart disease (CHD) and pulmonary arterial hypertension associated with CHD (PAH-CHD) from chest radiographs using artificial intelligence (AI) technology and to evaluate whether AI assistance could improve clinical diagnostic accuracy. MATERIALS AND METHODS A total of 3255 frontal preoperative chest radiographs (1174 CHD of any type and 2081 non-CHD) were retrospectively obtained. In this study, we adopted ResNet18 pretrained with the ImageNet database to establish diagnostic models. Radiologists diagnosed CHD/PAH-CHD from 330/165 chest radiographs twice: the first time, 50% of the images were accompanied by AI-based classification; after a month, the remaining 50% were accompanied by AI-based classification. Diagnostic results were compared between the radiologists and AI models, and between radiologists with and without AI assistance. RESULTS The AI model achieved an average area under the receiver operating characteristic curve (AUC) of 0.948 (sensitivity: 0.970, specificity: 0.982) for CHD diagnoses and an AUC of 0.778 (sensitivity: 0.632, specificity: 0.925) for identifying PAH-CHD. In the 330 balanced (165 CHD and 165 non-CHD) testing set, AI achieved higher AUCs than all 5 radiologists in the identification of CHD (0.670-0.858) and PAH-CHD (0.610-0.688). With AI assistance, the mean ± standard error AUC of radiologists was significantly improved for CHD (ΔAUC + 0.096, 95 % CI: 0.001-0.190; P = 0.048) and PAH-CHD (ΔAUC + 0.066, 95 % CI: 0.010-0.122; P = 0.031) diagnosis. CONCLUSION Chest radiograph-based AI models can detect CHD and PAH-CHD automatically. AI assistance improved radiologists' diagnostic accuracy, which may facilitate a timely initial diagnosis of CHD and PAH-CHD.
Collapse
Affiliation(s)
- Pei-Lun Han
- Department of Radiology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Lei Jiang
- College of Computer Science, Sichuan University, Chengdu, China
| | - Jun-Long Cheng
- College of Computer Science, Sichuan University, Chengdu, China
| | - Ke Shi
- Department of Radiology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Shan Huang
- Department of Radiology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yu Jiang
- Department of Radiology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Li Jiang
- Department of Radiology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Qing Xia
- SenseTime Research, Beijing, China
| | - Yi-Yue Li
- Department of Radiology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Min Zhu
- College of Computer Science, Sichuan University, Chengdu, China
| | - Kang Li
- Department of Radiology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China; Med-X Center for Informatics, Sichuan University, Chengdu, China; Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Zhi-Gang Yang
- Department of Radiology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
| |
Collapse
|
22
|
Berman D, Hunter C, Hossain A, Yao J, Workman E, Guan S, Strickhart L, Beanlands R, Slater D, deKemp RA. Machine and deep learning models for accurate detection of ischemia and scar with myocardial blood flow positron emission tomography imaging. J Nucl Cardiol 2024; 32:101797. [PMID: 38185409 DOI: 10.1016/j.nuclcard.2024.101797] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
BACKGROUND Quantification of myocardial blood flow (MBF) is used for the noninvasive diagnosis of patients with coronary artery disease (CAD). This study compared traditional statistics, machine learning, and deep learning techniques in their ability to diagnose disease using only the rest and stress MBF values. METHODS This study included 3245 rest and stress rubidium-82 positron emission tomography (PET) studies and matching diagnostic labels from perfusion reports. Standard logistic regression, lasso logistic regression, support vector machine, random forest, multilayer perceptron, and dense U-Net were compared for per-patient detection and per-vessel localization of scars and ischemia. RESULTS Receiver-operator characteristic area under the curve (AUC) of machine learning models was significantly higher than those of traditional statistics models for per-patient detection of disease (0.92-0.95 vs. 0.87) but not for per-vessel localization of ischemia or scar. Random forest showed the highest AUC = 0.95 among the different models compared. On the final hold-out set for generalizability, random forest showed an AUC of 0.92 for detection and 0.89 for localization of perfusion abnormalities. CONCLUSIONS For per-vessel localization, simple models trained on segmental data performed similarly to a convolutional neural network trained on polar-map data, highlighting the need to justify the use of complex predictive algorithms through comparison with simpler methods.
Collapse
Affiliation(s)
- Daniel Berman
- The MITRE Corporation, 7515 Colshire Drive, McLean, VA 22102, USA
| | - Chad Hunter
- University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, K1Y 4W7, Canada
| | - Alomgir Hossain
- University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, K1Y 4W7, Canada; The Hospital for Sick Children, 555 University Avenue, Toronto, M5G 1X8, Canada
| | - Jason Yao
- University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, K1Y 4W7, Canada
| | - Emily Workman
- The MITRE Corporation, 7515 Colshire Drive, McLean, VA 22102, USA
| | - Steven Guan
- The MITRE Corporation, 7515 Colshire Drive, McLean, VA 22102, USA
| | - Laura Strickhart
- The MITRE Corporation, 7515 Colshire Drive, McLean, VA 22102, USA
| | - Rob Beanlands
- University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, K1Y 4W7, Canada
| | - David Slater
- The MITRE Corporation, 7515 Colshire Drive, McLean, VA 22102, USA
| | - Robert A deKemp
- University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, K1Y 4W7, Canada.
| |
Collapse
|
23
|
Pelter MN, Druz RS. Precision medicine: Hype or hope? Trends Cardiovasc Med 2024; 34:120-125. [PMID: 36375778 DOI: 10.1016/j.tcm.2022.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 11/05/2022] [Accepted: 11/06/2022] [Indexed: 11/13/2022]
Abstract
In recent years, precision medicine has steadily risen to the forefront of many aspects of medicine, including cardiology [1]. While this field has exponentially expanded and advanced in the last few years, a lot of questions remain regarding exact definition, usage, and clinical applications [2,3]. This review will provide a brief synopsis of the current state of precision medicine, its limitations, future directions, as well as analyze emerging clinical applications in cardiology.
Collapse
|
24
|
Alwan L, Benz DC, Cuddy SAM, Dobner S, Shiri I, Caobelli F, Bernhard B, Stämpfli SF, Eberli F, Reyes M, Kwong RY, Falk RH, Dorbala S, Gräni C. Current and Evolving Multimodality Cardiac Imaging in Managing Transthyretin Amyloid Cardiomyopathy. JACC Cardiovasc Imaging 2024; 17:195-211. [PMID: 38099914 DOI: 10.1016/j.jcmg.2023.10.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 10/06/2023] [Accepted: 10/18/2023] [Indexed: 01/29/2024]
Abstract
Amyloid transthyretin (ATTR) amyloidosis is a protein-misfolding disease characterized by fibril accumulation in the extracellular space that can result in local tissue disruption and organ dysfunction. Cardiac involvement drives morbidity and mortality, and the heart is the major organ affected by ATTR amyloidosis. Multimodality cardiac imaging (ie, echocardiography, scintigraphy, and cardiac magnetic resonance) allows accurate diagnosis of ATTR cardiomyopathy (ATTR-CM), and this is of particular importance because ATTR-targeting therapies have become available and probably exert their greatest benefit at earlier disease stages. Apart from establishing the diagnosis, multimodality cardiac imaging may help to better understand pathogenesis, predict prognosis, and monitor treatment response. The aim of this review is to give an update on contemporary and evolving cardiac imaging methods and their role in diagnosing and managing ATTR-CM. Further, an outlook is presented on how artificial intelligence in cardiac imaging may improve future clinical decision making and patient management in the setting of ATTR-CM.
Collapse
Affiliation(s)
- Louhai Alwan
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Dominik C Benz
- Amyloidosis Program, Division of Cardiology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA; Division of Nuclear Medicine, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA; CV Imaging Program, Cardiovascular Division, Department of Medicine and Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA; Cardiac Imaging, Department of Cardiology and Nuclear Medicine, Zurich University Hospital, Zurich, Switzerland
| | - Sarah A M Cuddy
- Amyloidosis Program, Division of Cardiology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA; Division of Nuclear Medicine, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA; CV Imaging Program, Cardiovascular Division, Department of Medicine and Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Stephan Dobner
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Isaac Shiri
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Federico Caobelli
- University Clinic of Nuclear Medicine, Inselspital, Bern University Hospital, Switzerland
| | - Benedikt Bernhard
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland; CV Imaging Program, Cardiovascular Division, Department of Medicine and Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Simon F Stämpfli
- Department of Cardiology, Heart Centre Lucerne, Luzerner Kantonsspital, Lucerne, Switzerland
| | - Franz Eberli
- Department of Cardiology, Triemli Hospital (Triemlispital), Zurich, Switzerland
| | - Mauricio Reyes
- Insel Data Science Center, Inselspital, Bern University Hospital, Bern, Switzerland; Artificial Intelligence in Medical Imaging, ARTORG Center for Biomedical Research, University of Bern, Bern, Switzerland
| | - Raymond Y Kwong
- CV Imaging Program, Cardiovascular Division, Department of Medicine and Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Rodney H Falk
- Amyloidosis Program, Division of Cardiology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Sharmila Dorbala
- Amyloidosis Program, Division of Cardiology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA; Division of Nuclear Medicine, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA; CV Imaging Program, Cardiovascular Division, Department of Medicine and Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Christoph Gräni
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
| |
Collapse
|
25
|
Bourazana A, Xanthopoulos A, Briasoulis A, Magouliotis D, Spiliopoulos K, Athanasiou T, Vassilopoulos G, Skoularigis J, Triposkiadis F. Artificial Intelligence in Heart Failure: Friend or Foe? Life (Basel) 2024; 14:145. [PMID: 38276274 PMCID: PMC10817517 DOI: 10.3390/life14010145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/08/2024] [Accepted: 01/17/2024] [Indexed: 01/27/2024] Open
Abstract
In recent times, there have been notable changes in cardiovascular medicine, propelled by the swift advancements in artificial intelligence (AI). The present work provides an overview of the current applications and challenges of AI in the field of heart failure. It emphasizes the "garbage in, garbage out" issue, where AI systems can produce inaccurate results with skewed data. The discussion covers issues in heart failure diagnostic algorithms, particularly discrepancies between existing models. Concerns about the reliance on the left ventricular ejection fraction (LVEF) for classification and treatment are highlighted, showcasing differences in current scientific perceptions. This review also delves into challenges in implementing AI, including variable considerations and biases in training data. It underscores the limitations of current AI models in real-world scenarios and the difficulty in interpreting their predictions, contributing to limited physician trust in AI-based models. The overarching suggestion is that AI can be a valuable tool in clinicians' hands for treating heart failure patients, as far as existing medical inaccuracies have been addressed before integrating AI into these frameworks.
Collapse
Affiliation(s)
- Angeliki Bourazana
- Department of Cardiology, University Hospital of Larissa, 41110 Larissa, Greece
| | - Andrew Xanthopoulos
- Department of Cardiology, University Hospital of Larissa, 41110 Larissa, Greece
| | - Alexandros Briasoulis
- Division of Cardiovascular Medicine, Section of Heart Failure and Transplantation, University of Iowa, Iowa City, IA 52242, USA
| | - Dimitrios Magouliotis
- Department of Cardiothoracic Surgery, University of Thessaly, 41110 Larissa, Greece; (D.M.); (K.S.)
| | - Kyriakos Spiliopoulos
- Department of Cardiothoracic Surgery, University of Thessaly, 41110 Larissa, Greece; (D.M.); (K.S.)
| | - Thanos Athanasiou
- Department of Surgery and Cancer, Imperial College London, St Mary’s Hospital, London W2 1NY, UK
| | - George Vassilopoulos
- Department of Hematology, University Hospital of Larissa, University of Thessaly Medical School, 41110 Larissa, Greece
| | - John Skoularigis
- Department of Cardiology, University Hospital of Larissa, 41110 Larissa, Greece
| | | |
Collapse
|
26
|
Sun Z, Silberstein J, Vaccarezza M. Cardiovascular Computed Tomography in the Diagnosis of Cardiovascular Disease: Beyond Lumen Assessment. J Cardiovasc Dev Dis 2024; 11:22. [PMID: 38248892 PMCID: PMC10816599 DOI: 10.3390/jcdd11010022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 01/10/2024] [Accepted: 01/11/2024] [Indexed: 01/23/2024] Open
Abstract
Cardiovascular CT is being widely used in the diagnosis of cardiovascular disease due to the rapid technological advancements in CT scanning techniques. These advancements include the development of multi-slice CT, from early generation to the latest models, which has the capability of acquiring images with high spatial and temporal resolution. The recent emergence of photon-counting CT has further enhanced CT performance in clinical applications, providing improved spatial and contrast resolution. CT-derived fractional flow reserve is superior to standard CT-based anatomical assessment for the detection of lesion-specific myocardial ischemia. CT-derived 3D-printed patient-specific models are also superior to standard CT, offering advantages in terms of educational value, surgical planning, and the simulation of cardiovascular disease treatment, as well as enhancing doctor-patient communication. Three-dimensional visualization tools including virtual reality, augmented reality, and mixed reality are further advancing the clinical value of cardiovascular CT in cardiovascular disease. With the widespread use of artificial intelligence, machine learning, and deep learning in cardiovascular disease, the diagnostic performance of cardiovascular CT has significantly improved, with promising results being presented in terms of both disease diagnosis and prediction. This review article provides an overview of the applications of cardiovascular CT, covering its performance from the perspective of its diagnostic value based on traditional lumen assessment to the identification of vulnerable lesions for the prediction of disease outcomes with the use of these advanced technologies. The limitations and future prospects of these technologies are also discussed.
Collapse
Affiliation(s)
- Zhonghua Sun
- Curtin Medical School, Curtin University, Perth, WA 6102, Australia; (J.S.); (M.V.)
- Curtin Health Innovation Research Institute (CHIRI), Curtin University, Perth, WA 6102, Australia
| | - Jenna Silberstein
- Curtin Medical School, Curtin University, Perth, WA 6102, Australia; (J.S.); (M.V.)
| | - Mauro Vaccarezza
- Curtin Medical School, Curtin University, Perth, WA 6102, Australia; (J.S.); (M.V.)
- Curtin Health Innovation Research Institute (CHIRI), Curtin University, Perth, WA 6102, Australia
| |
Collapse
|
27
|
Cau R, Pisu F, Suri JS, Montisci R, Gatti M, Mannelli L, Gong X, Saba L. Artificial Intelligence in the Differential Diagnosis of Cardiomyopathy Phenotypes. Diagnostics (Basel) 2024; 14:156. [PMID: 38248033 PMCID: PMC11154548 DOI: 10.3390/diagnostics14020156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/03/2024] [Accepted: 01/08/2024] [Indexed: 01/23/2024] Open
Abstract
Artificial intelligence (AI) is rapidly being applied to the medical field, especially in the cardiovascular domain. AI approaches have demonstrated their applicability in the detection, diagnosis, and management of several cardiovascular diseases, enhancing disease stratification and typing. Cardiomyopathies are a leading cause of heart failure and life-threatening ventricular arrhythmias. Identifying the etiologies is fundamental for the management and diagnostic pathway of these heart muscle diseases, requiring the integration of various data, including personal and family history, clinical examination, electrocardiography, and laboratory investigations, as well as multimodality imaging, making the clinical diagnosis challenging. In this scenario, AI has demonstrated its capability to capture subtle connections from a multitude of multiparametric datasets, enabling the discovery of hidden relationships in data and handling more complex tasks than traditional methods. This review aims to present a comprehensive overview of the main concepts related to AI and its subset. Additionally, we review the existing literature on AI-based models in the differential diagnosis of cardiomyopathy phenotypes, and we finally examine the advantages and limitations of these AI approaches.
Collapse
Affiliation(s)
- Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy; (R.C.); (F.P.)
| | - Francesco Pisu
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy; (R.C.); (F.P.)
| | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoin™, Roseville, CA 95661, USA;
| | - Roberta Montisci
- Department of Cardiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy;
| | - Marco Gatti
- Department of Radiology, Università degli Studi di Torino, 10129 Turin, Italy;
| | | | - Xiangyang Gong
- Radiology Department, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou 310014, China;
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy; (R.C.); (F.P.)
| |
Collapse
|
28
|
Willingham TB, Stowell J, Collier G, Backus D. Leveraging Emerging Technologies to Expand Accessibility and Improve Precision in Rehabilitation and Exercise for People with Disabilities. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2024; 21:79. [PMID: 38248542 PMCID: PMC10815484 DOI: 10.3390/ijerph21010079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/20/2023] [Accepted: 12/28/2023] [Indexed: 01/23/2024]
Abstract
Physical rehabilitation and exercise training have emerged as promising solutions for improving health, restoring function, and preserving quality of life in populations that face disparate health challenges related to disability. Despite the immense potential for rehabilitation and exercise to help people with disabilities live longer, healthier, and more independent lives, people with disabilities can experience physical, psychosocial, environmental, and economic barriers that limit their ability to participate in rehabilitation, exercise, and other physical activities. Together, these barriers contribute to health inequities in people with disabilities, by disproportionately limiting their ability to participate in health-promoting physical activities, relative to people without disabilities. Therefore, there is great need for research and innovation focusing on the development of strategies to expand accessibility and promote participation in rehabilitation and exercise programs for people with disabilities. Here, we discuss how cutting-edge technologies related to telecommunications, wearables, virtual and augmented reality, artificial intelligence, and cloud computing are providing new opportunities to improve accessibility in rehabilitation and exercise for people with disabilities. In addition, we highlight new frontiers in digital health technology and emerging lines of scientific research that will shape the future of precision care strategies for people with disabilities.
Collapse
Affiliation(s)
- T. Bradley Willingham
- Shepherd Center, Virginia C. Crawford Research Institute, Atlanta, GA 30309, USA (D.B.)
- Department of Physical Therapy, Georgia State University, Atlanta, GA 30302, USA
| | - Julie Stowell
- Shepherd Center, Virginia C. Crawford Research Institute, Atlanta, GA 30309, USA (D.B.)
- Department of Physical Therapy, Georgia State University, Atlanta, GA 30302, USA
| | - George Collier
- Shepherd Center, Virginia C. Crawford Research Institute, Atlanta, GA 30309, USA (D.B.)
| | - Deborah Backus
- Shepherd Center, Virginia C. Crawford Research Institute, Atlanta, GA 30309, USA (D.B.)
| |
Collapse
|
29
|
Abdelrahman K, Shiyovich A, Huck DM, Berman AN, Weber B, Gupta S, Cardoso R, Blankstein R. Artificial Intelligence in Coronary Artery Calcium Scoring Detection and Quantification. Diagnostics (Basel) 2024; 14:125. [PMID: 38248002 PMCID: PMC10814920 DOI: 10.3390/diagnostics14020125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 12/25/2023] [Accepted: 12/27/2023] [Indexed: 01/23/2024] Open
Abstract
Coronary artery calcium (CAC) is a marker of coronary atherosclerosis, and the presence and severity of CAC have been shown to be powerful predictors of future cardiovascular events. Due to its value in risk discrimination and reclassification beyond traditional risk factors, CAC has been supported by recent guidelines, particularly for the purposes of informing shared decision-making regarding the use of preventive therapies. In addition to dedicated ECG-gated CAC scans, the presence and severity of CAC can also be accurately estimated on non-contrast chest computed tomography scans performed for other clinical indications. However, the presence of such "incidental" CAC is rarely reported. Advances in artificial intelligence have now enabled automatic CAC scoring for both cardiac and non-cardiac CT scans. Various AI approaches, from rule-based models to machine learning algorithms and deep learning, have been applied to automate CAC scoring. Convolutional neural networks, a deep learning technique, have had the most successful approach, with high agreement with manual scoring demonstrated in multiple studies. Such automated CAC measurements may enable wider and more accurate detection of CAC from non-gated CT studies, thus improving the efficiency of healthcare systems to identify and treat previously undiagnosed coronary artery disease.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | - Ron Blankstein
- Departments of Medicine (Cardiovascular Division) and Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| |
Collapse
|
30
|
Föllmer B, Williams MC, Dey D, Arbab-Zadeh A, Maurovich-Horvat P, Volleberg RHJA, Rueckert D, Schnabel JA, Newby DE, Dweck MR, Guagliumi G, Falk V, Vázquez Mézquita AJ, Biavati F, Išgum I, Dewey M. Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteries. Nat Rev Cardiol 2024; 21:51-64. [PMID: 37464183 DOI: 10.1038/s41569-023-00900-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/07/2023] [Indexed: 07/20/2023]
Abstract
Artificial intelligence (AI) is likely to revolutionize the way medical images are analysed and has the potential to improve the identification and analysis of vulnerable or high-risk atherosclerotic plaques in coronary arteries, leading to advances in the treatment of coronary artery disease. However, coronary plaque analysis is challenging owing to cardiac and respiratory motion, as well as the small size of cardiovascular structures. Moreover, the analysis of coronary imaging data is time-consuming, can be performed only by clinicians with dedicated cardiovascular imaging training, and is subject to considerable interreader and intrareader variability. AI has the potential to improve the assessment of images of vulnerable plaque in coronary arteries, but requires robust development, testing and validation. Combining human expertise with AI might facilitate the reliable and valid interpretation of images obtained using CT, MRI, PET, intravascular ultrasonography and optical coherence tomography. In this Roadmap, we review existing evidence on the application of AI to the imaging of vulnerable plaque in coronary arteries and provide consensus recommendations developed by an interdisciplinary group of experts on AI and non-invasive and invasive coronary imaging. We also outline future requirements of AI technology to address bias, uncertainty, explainability and generalizability, which are all essential for the acceptance of AI and its clinical utility in handling the anticipated growing volume of coronary imaging procedures.
Collapse
Affiliation(s)
- Bernhard Föllmer
- Department of Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany.
| | | | - Damini Dey
- Biomedical Imaging Research Institute and Department of Imaging, Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Armin Arbab-Zadeh
- Division of Cardiology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Pál Maurovich-Horvat
- Department of Radiology, Medical Imaging Center, Semmelweis University, Budapest, Hungary
| | - Rick H J A Volleberg
- Department of Cardiology, Radboud University Medical Center, Nijmegen, Netherlands
| | - Daniel Rueckert
- Artificial Intelligence in Medicine and Healthcare, Technical University of Munich, Munich, Germany
- Department of Computing, Imperial College London, London, UK
| | - Julia A Schnabel
- School of Biomedical Imaging and Imaging Sciences, King's College London, London, UK
- Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Neuherberg, Germany
- School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - David E Newby
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Marc R Dweck
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Giulio Guagliumi
- Division of Cardiology, IRCCS Galeazzi Sant'Ambrogio Hospital, Milan, Italy
| | - Volkmar Falk
- Department of Cardiothoracic and Vascular Surgery, Deutsches Herzzentrum der Charité, Charité Universitätsmedizin, Berlin, Germany
- Department of Health Science and Technology, ETH Zurich, Zurich, Switzerland
- Berlin Institute of Health at Charité and DZHK (German Centre for Cardiovascular Research), Partner Site, Berlin, Germany
| | | | - Federico Biavati
- Department of Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
- Informatics Institute, Faculty of Science, University of Amsterdam, Amsterdam, Netherlands
| | - Marc Dewey
- Department of Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany.
- Berlin Institute of Health, Campus Charité Mitte, Berlin, Germany.
- DZHK (German Centre for Cardiovascular Research), Partner Site Berlin and Deutsches Herzzentrum der Charité (DHZC), Charité - Universitätsmedizin Berlin, Berlin, Germany.
| |
Collapse
|
31
|
Fletcher AJ, Johnson CL, Leeson P. Artificial intelligence and innovation of clinical care: the need for evidence in the real world. Eur Heart J 2024; 45:42-44. [PMID: 37670406 DOI: 10.1093/eurheartj/ehad553] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/07/2023] Open
Affiliation(s)
- Andrew J Fletcher
- Oxford Cardiovascular Clinical Research Facility, RDM Division of Cardiovascular Medicine, University of Oxford, Oxford OX3 9DU, UK
- Department of Cardiac Physiology, Royal Papworth Hospital NHS Foundation Trust, Cambridge, UK
| | - Casey L Johnson
- Oxford Cardiovascular Clinical Research Facility, RDM Division of Cardiovascular Medicine, University of Oxford, Oxford OX3 9DU, UK
| | - Paul Leeson
- Oxford Cardiovascular Clinical Research Facility, RDM Division of Cardiovascular Medicine, University of Oxford, Oxford OX3 9DU, UK
| |
Collapse
|
32
|
Teng X, Wang Z. Online COVID-19 diagnosis prediction using complete blood count: an innovative tool for public health. BMC Public Health 2023; 23:2536. [PMID: 38114942 PMCID: PMC10729447 DOI: 10.1186/s12889-023-17477-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 12/13/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND COVID-19, caused by SARS-CoV-2, presents distinct diagnostic challenges due to its wide range of clinical manifestations and the overlapping symptoms with other common respiratory diseases. This study focuses on addressing these difficulties by employing machine learning (ML) methodologies, particularly the XGBoost algorithm, to utilize Complete Blood Count (CBC) parameters for predictive analysis. METHODS We performed a retrospective study involving 2114 COVID-19 patients treated between December 2022 and January 2023 at our healthcare facility. These patients were classified into fever (1057 patients) and pneumonia groups (1057 patients), based on their clinical symptoms. The CBC data were utilized to create predictive models, with model performance evaluated through metrics like Area Under the Receiver Operating Characteristics Curve (AUC), accuracy, sensitivity, specificity, and precision. We selected the top 10 predictive variables based on their significance in disease prediction. The data were then split into a training set (70% of patients) and a validation set (30% of patients) for model validation. RESULTS We identified 31 indicators with significant disparities. The XGBoost model outperformed others, with an AUC of 0.920 and high precision, sensitivity, specificity, and accuracy. The top 10 features (Age, Monocyte%, Mean Platelet Volume, Lymphocyte%, SIRI, Eosinophil count, Platelet count, Hemoglobin, Platelet Distribution Width, and Neutrophil count.) were crucial in constructing a more precise predictive model. The model demonstrated strong performance on both training (AUC = 0.977) and validation (AUC = 0.912) datasets, validated by decision curve analysis and calibration curve. CONCLUSION ML models that incorporate CBC parameters offer an innovative and effective tool for data analysis in COVID-19. They potentially enhance diagnostic accuracy and the efficacy of therapeutic interventions, ultimately contributing to a reduction in the mortality rate of this infectious disease.
Collapse
Affiliation(s)
- Xiaojing Teng
- Department of Clinical Laboratory, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, Zhejiang, 310000, China
| | - Zhiyi Wang
- Department of Clinical Laboratory, Hangzhou Women's Hospital (Hangzhou Maternity and Child Health Care Hospital), No. 369, Kunpeng Road, Shangcheng District Hangzhou, Hangzhou, Zhejiang, 310008, China.
| |
Collapse
|
33
|
Motwani M. 2022 Artificial intelligence primer for the nuclear cardiologist. J Nucl Cardiol 2023; 30:2441-2453. [PMID: 35854041 DOI: 10.1007/s12350-022-03049-7] [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: 05/15/2022] [Accepted: 06/14/2022] [Indexed: 10/17/2022]
Abstract
Driven by advances in computing power, the past decade has seen rapid developments in artificial intelligence (AI) which now offers potential enhancements to every aspect of nuclear cardiology workflow including acquisition, reconstruction, segmentation, direct image analysis, and interpretation; as well as facilitating clinical and imaging big-data integration for superior personalized risk stratification. To understand the relevance and potential of AI in their field, this review provides a primer for nuclear cardiologists in 2022. The aim is to explain terminology and provide a summary of key current implementations, challenges, and future aspirations of AI-based enhancements to nuclear cardiology.
Collapse
Affiliation(s)
- Manish Motwani
- Department of Cardiology, Manchester Heart Institute, Manchester Royal Infirmary, Manchester Heart Centre, Manchester University NHS Foundation Trust, Oxford Road, Manchester, UK.
- Institute of Cardiovascular Science, University of Manchester, Manchester, UK.
| |
Collapse
|
34
|
Li LS, Yang L, Zhuang L, Ye ZY, Zhao WG, Gong WP. From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning. Mil Med Res 2023; 10:58. [PMID: 38017571 PMCID: PMC10685516 DOI: 10.1186/s40779-023-00490-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 11/06/2023] [Indexed: 11/30/2023] Open
Abstract
Latent tuberculosis infection (LTBI) has become a major source of active tuberculosis (ATB). Although the tuberculin skin test and interferon-gamma release assay can be used to diagnose LTBI, these methods can only differentiate infected individuals from healthy ones but cannot discriminate between LTBI and ATB. Thus, the diagnosis of LTBI faces many challenges, such as the lack of effective biomarkers from Mycobacterium tuberculosis (MTB) for distinguishing LTBI, the low diagnostic efficacy of biomarkers derived from the human host, and the absence of a gold standard to differentiate between LTBI and ATB. Sputum culture, as the gold standard for diagnosing tuberculosis, is time-consuming and cannot distinguish between ATB and LTBI. In this article, we review the pathogenesis of MTB and the immune mechanisms of the host in LTBI, including the innate and adaptive immune responses, multiple immune evasion mechanisms of MTB, and epigenetic regulation. Based on this knowledge, we summarize the current status and challenges in diagnosing LTBI and present the application of machine learning (ML) in LTBI diagnosis, as well as the advantages and limitations of ML in this context. Finally, we discuss the future development directions of ML applied to LTBI diagnosis.
Collapse
Affiliation(s)
- Lin-Sheng Li
- Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, the Eighth Medical Center of PLA General Hospital, Beijing, 100091, China
- Hebei North University, Zhangjiakou, 075000, Hebei, China
- Senior Department of Respiratory and Critical Care Medicine, the Eighth Medical Center of PLA General Hospital, Beijing, 100091, China
| | - Ling Yang
- Hebei North University, Zhangjiakou, 075000, Hebei, China
| | - Li Zhuang
- Hebei North University, Zhangjiakou, 075000, Hebei, China
| | - Zhao-Yang Ye
- Hebei North University, Zhangjiakou, 075000, Hebei, China
| | - Wei-Guo Zhao
- Senior Department of Respiratory and Critical Care Medicine, the Eighth Medical Center of PLA General Hospital, Beijing, 100091, China.
| | - Wen-Ping Gong
- Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, the Eighth Medical Center of PLA General Hospital, Beijing, 100091, China.
| |
Collapse
|
35
|
Ohashi H, Bouisset F, Buytaert D, Seki R, Sonck J, Sakai K, Belmonte M, Kitslaar P, Updegrove A, Amano T, Andreini D, De Bruyne B, Collet C. Coronary CT Angiography in the Cath Lab: Leveraging Artificial Intelligence to Plan and Guide Percutaneous Coronary Intervention. Interv Cardiol 2023; 18:e26. [PMID: 38125928 PMCID: PMC10731535 DOI: 10.15420/icr.2023.12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 08/15/2023] [Indexed: 12/23/2023] Open
Abstract
The role of coronary CT angiography for the diagnosis and risk stratification of coronary artery disease is well established. However, its potential beyond the diagnostic phase remains to be determined. The current review focuses on the insights that coronary CT angiography can provide when planning and performing percutaneous coronary interventions. We describe a novel approach incorporating anatomical and functional pre-procedural planning enhanced by artificial intelligence, computational physiology and online 3D CT guidance for percutaneous coronary interventions. This strategy allows the individualisation of patient selection, optimisation of the revascularisation strategy and effective use of resources.
Collapse
Affiliation(s)
- Hirofumi Ohashi
- Cardiovascular Center OLVAalst, Belgium
- Department of Cardiology, Aichi Medical UniversityAichi, Japan
| | - Frédéric Bouisset
- Cardiovascular Center OLVAalst, Belgium
- Department of Cardiology, Toulouse University HospitalToulouse, France
| | | | | | | | - Koshiro Sakai
- Cardiovascular Center OLVAalst, Belgium
- Department of Cardiology, Showa University HospitalTokyo, Japan
| | - Marta Belmonte
- Cardiovascular Center OLVAalst, Belgium
- Department of Advanced Biomedical Sciences, University Federico IINaples, Italy
| | | | | | - Tetsuya Amano
- Department of Cardiology, Aichi Medical UniversityAichi, Japan
| | - Daniele Andreini
- Division of Cardiology and Cardiac Imaging, IRCCS Ospedale Galeazzi – Sant’AmbrogioMilan, Italy
- Department of Biomedical and Clinical Sciences, University of MilanMilan, Italy
| | - Bernard De Bruyne
- Cardiovascular Center OLVAalst, Belgium
- Department of Cardiology, University Hospital of LausanneLausanne, Switzerland
| | | |
Collapse
|
36
|
Alahdab F, El Shawi R, Ahmed AI, Han Y, Al-Mallah M. Patient-level explainable machine learning to predict major adverse cardiovascular events from SPECT MPI and CCTA imaging. PLoS One 2023; 18:e0291451. [PMID: 37967112 PMCID: PMC10651041 DOI: 10.1371/journal.pone.0291451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 08/30/2023] [Indexed: 11/17/2023] Open
Abstract
BACKGROUND Machine learning (ML) has shown promise in improving the risk prediction in non-invasive cardiovascular imaging, including SPECT MPI and coronary CT angiography. However, most algorithms used remain black boxes to clinicians in how they compute their predictions. Furthermore, objective consideration of the multitude of available clinical data, along with the visual and quantitative assessments from CCTA and SPECT, are critical for optimal patient risk stratification. We aim to provide an explainable ML approach to predict MACE using clinical, CCTA, and SPECT data. METHODS Consecutive patients who underwent clinically indicated CCTA and SPECT myocardial imaging for suspected CAD were included and followed up for MACEs. A MACE was defined as a composite outcome that included all-cause mortality, myocardial infarction, or late revascularization. We employed an Automated Machine Learning (AutoML) approach to predict MACE using clinical, CCTA, and SPECT data. Various mainstream models with different sets of hyperparameters have been explored, and critical predictors of risk are obtained using explainable techniques on the global and patient levels. Ten-fold cross-validation was used in training and evaluating the AutoML model. RESULTS A total of 956 patients were included (mean age 61.1 ±14.2 years, 54% men, 89% hypertension, 81% diabetes, 84% dyslipidemia). Obstructive CAD on CCTA and ischemia on SPECT were observed in 14% of patients, and 11% experienced MACE. ML prediction's sensitivity, specificity, and accuracy in predicting a MACE were 69.61%, 99.77%, and 96.54%, respectively. The top 10 global predictive features included 8 CCTA attributes (segment involvement score, number of vessels with severe plaque ≥70, ≥50% stenosis in the left marginal coronary artery, calcified plaque, ≥50% stenosis in the left circumflex coronary artery, plaque type in the left marginal coronary artery, stenosis degree in the second obtuse marginal of the left circumflex artery, and stenosis category in the marginals of the left circumflex artery) and 2 clinical features (past medical history of MI or left bundle branch block, being an ever smoker). CONCLUSION ML can accurately predict risk of developing a MACE in patients suspected of CAD undergoing SPECT MPI and CCTA. ML feature-ranking can also show, at a sample- as well as at a patient-level, which features are key in making such a prediction.
Collapse
Affiliation(s)
- Fares Alahdab
- Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, United States of America
| | - Radwa El Shawi
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Ahmed Ibrahim Ahmed
- Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, United States of America
| | - Yushui Han
- Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, United States of America
| | - Mouaz Al-Mallah
- Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, United States of America
| |
Collapse
|
37
|
Holste G, Oikonomou EK, Mortazavi BJ, Coppi A, Faridi KF, Miller EJ, Forrest JK, McNamara RL, Ohno-Machado L, Yuan N, Gupta A, Ouyang D, Krumholz HM, Wang Z, Khera R. Severe aortic stenosis detection by deep learning applied to echocardiography. Eur Heart J 2023; 44:4592-4604. [PMID: 37611002 PMCID: PMC11004929 DOI: 10.1093/eurheartj/ehad456] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 06/21/2023] [Accepted: 07/11/2023] [Indexed: 08/25/2023] Open
Abstract
BACKGROUND AND AIMS Early diagnosis of aortic stenosis (AS) is critical to prevent morbidity and mortality but requires skilled examination with Doppler imaging. This study reports the development and validation of a novel deep learning model that relies on two-dimensional (2D) parasternal long axis videos from transthoracic echocardiography without Doppler imaging to identify severe AS, suitable for point-of-care ultrasonography. METHODS AND RESULTS In a training set of 5257 studies (17 570 videos) from 2016 to 2020 [Yale-New Haven Hospital (YNHH), Connecticut], an ensemble of three-dimensional convolutional neural networks was developed to detect severe AS, leveraging self-supervised contrastive pretraining for label-efficient model development. This deep learning model was validated in a temporally distinct set of 2040 consecutive studies from 2021 from YNHH as well as two geographically distinct cohorts of 4226 and 3072 studies, from California and other hospitals in New England, respectively. The deep learning model achieved an area under the receiver operating characteristic curve (AUROC) of 0.978 (95% CI: 0.966, 0.988) for detecting severe AS in the temporally distinct test set, maintaining its diagnostic performance in geographically distinct cohorts [0.952 AUROC (95% CI: 0.941, 0.963) in California and 0.942 AUROC (95% CI: 0.909, 0.966) in New England]. The model was interpretable with saliency maps identifying the aortic valve, mitral annulus, and left atrium as the predictive regions. Among non-severe AS cases, predicted probabilities were associated with worse quantitative metrics of AS suggesting an association with various stages of AS severity. CONCLUSION This study developed and externally validated an automated approach for severe AS detection using single-view 2D echocardiography, with potential utility for point-of-care screening.
Collapse
Affiliation(s)
- Gregory Holste
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA
| | - Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA
| | - Bobak J Mortazavi
- Department of Computer Science & Engineering, Texas A&M University, College Station, TX, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, 195 Church St 5th Floor, New Haven, CT, USA
| | - Andreas Coppi
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, 195 Church St 5th Floor, New Haven, CT, USA
| | - Kamil F Faridi
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA
| | - John K Forrest
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA
| | - Robert L McNamara
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA
| | - Lucila Ohno-Machado
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA
| | - Neal Yuan
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
- Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Aakriti Gupta
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Harlan M Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, 195 Church St 5th Floor, New Haven, CT, USA
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
| | - Zhangyang Wang
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, 195 Church St 5th Floor, New Haven, CT, USA
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, 60 College St, New Haven, CT, USA
| |
Collapse
|
38
|
Chen J, Zhu Q, Mo Y, Ling H, Wang Y, Xie H, Li L. Exploring the action mechanism of Jinxin oral liquid on asthma by network pharmacology, molecular docking, and microRNA recognition. Medicine (Baltimore) 2023; 102:e35438. [PMID: 37904411 PMCID: PMC10615469 DOI: 10.1097/md.0000000000035438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 09/08/2023] [Indexed: 11/01/2023] Open
Abstract
Using network pharmacology, molecular docking, and microRNA recognition, we have elucidated the mechanisms underlying the treatment of asthma by Jinxin oral liquid (JXOL). We began by identifying and normalizing the active compounds in JXOL through searches in the traditional Chinese medicine systems pharmacology database, SwissADME database, encyclopedia of traditional Chinese medicine database, HERB database, and PubChem. Subsequently, we gathered and standardized the targets of these active compounds from sources including the encyclopedia of traditional Chinese medicine database, similarity ensemble approach dataset, UniProt, and other databases. Disease targets were extracted from GeneCards, PharmGKB, OMIM, comparative toxicogenomics database, and DisGeNET. The intersection of targets between JXOL and asthma was determined using a Venn diagram. We visualized a Formula-Herb-Compound-Target-Disease network and a protein-protein interaction network using Cytoscape 3.9.0. Molecular docking studies were performed using Schrodinger software. To identify pathways related to asthma, we conducted gene ontology functional analysis and Kyoto encyclopedia of genes and genomes pathway enrichment analysis using Metascape. MicroRNAs regulating the hub genes were obtained from the miRTarBase database, and a network linking these targets and miRNAs was constructed. Finally, we found 88 bioactive components in JXOL and 218 common targets with asthma. Molecular docking showed JXOL key compounds strongly bind to HUB targets. According to gene ontology biological process analysis and Kyoto encyclopedia of genes and genomes pathway enrichment analysis, the PI3K-Akt signaling pathway, the MAPK signaling pathway, or the cAMP signaling pathway play a key role in treating of asthma by JXOL. The HUB target-miRNA network showed that 6 miRNAs were recognized. In our study, we have revealed for the first time the unique components, multiple targets, and diverse pathways in JXOL that underlie its mechanism of action in treating asthma through miRNAs.
Collapse
Affiliation(s)
- Jing Chen
- Shanghai municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China
| | - Qiaozhen Zhu
- Clinical Medical School, Henan University, Kaifeng, People’s Republic of China
| | - Yanling Mo
- Department of Pediatrics, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People’s Republic of China
| | - Hao Ling
- Department of Pediatrics, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People’s Republic of China
| | - Yan Wang
- Department of Pediatrics, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People’s Republic of China
| | - Huihui Xie
- Department of Pediatrics, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People’s Republic of China
| | - Lan Li
- Department of Pediatrics, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People’s Republic of China
| |
Collapse
|
39
|
Hulten EA, Weinberg RL, Keating FK. Multiparametric Nuclear Stress Imaging: The Whole Is Greater Than the Sum of its Parts. J Am Coll Cardiol 2023; 82:1673-1675. [PMID: 37852697 DOI: 10.1016/j.jacc.2023.08.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 08/17/2023] [Indexed: 10/20/2023]
Affiliation(s)
- Edward A Hulten
- F. Edward Hebert School of Medicine, Uniformed Services University of Health Sciences, Bethesda, Maryland, USA; Lifespan Cardiovascular Institute and the Warren Alpert School of Medicine at Brown University, Providence, Rhode Island, USA.
| | - Richard L Weinberg
- Division of Cardiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA. https://twitter.com/rlweinberg
| | - Friederike K Keating
- Division of Cardiology, University of Vermont Larner College of Medicine, Burlington, Vermont, USA. https://twitter.com/FKeatingMD
| |
Collapse
|
40
|
Samant S, Bakhos JJ, Wu W, Zhao S, Kassab GS, Khan B, Panagopoulos A, Makadia J, Oguz UM, Banga A, Fayaz M, Glass W, Chiastra C, Burzotta F, LaDisa JF, Iaizzo P, Murasato Y, Dubini G, Migliavacca F, Mickley T, Bicek A, Fontana J, West NEJ, Mortier P, Boyers PJ, Gold JP, Anderson DR, Tcheng JE, Windle JR, Samady H, Jaffer FA, Desai NR, Lansky A, Mena-Hurtado C, Abbott D, Brilakis ES, Lassen JF, Louvard Y, Stankovic G, Serruys PW, Velazquez E, Elias P, Bhatt DL, Dangas G, Chatzizisis YS. Artificial Intelligence, Computational Simulations, and Extended Reality in Cardiovascular Interventions. JACC Cardiovasc Interv 2023; 16:2479-2497. [PMID: 37879802 DOI: 10.1016/j.jcin.2023.07.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 07/11/2023] [Accepted: 07/13/2023] [Indexed: 10/27/2023]
Abstract
Artificial intelligence, computational simulations, and extended reality, among other 21st century computational technologies, are changing the health care system. To collectively highlight the most recent advances and benefits of artificial intelligence, computational simulations, and extended reality in cardiovascular therapies, we coined the abbreviation AISER. The review particularly focuses on the following applications of AISER: 1) preprocedural planning and clinical decision making; 2) virtual clinical trials, and cardiovascular device research, development, and regulatory approval; and 3) education and training of interventional health care professionals and medical technology innovators. We also discuss the obstacles and constraints associated with the application of AISER technologies, as well as the proposed solutions. Interventional health care professionals, computer scientists, biomedical engineers, experts in bioinformatics and visualization, the device industry, ethics committees, and regulatory agencies are expected to streamline the use of AISER technologies in cardiovascular interventions and medicine in general.
Collapse
Affiliation(s)
- Saurabhi Samant
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Jules Joel Bakhos
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Wei Wu
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Shijia Zhao
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Ghassan S Kassab
- California Medical Innovations Institute, San Diego, California, USA
| | - Behram Khan
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Anastasios Panagopoulos
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Janaki Makadia
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Usama M Oguz
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Akshat Banga
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Muhammad Fayaz
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - William Glass
- Interprofessional Experiential Center for Enduring Learning, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Claudio Chiastra
- PoliTo(BIO)Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy
| | - Francesco Burzotta
- Department of Cardiovascular Sciences, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - John F LaDisa
- Departments of Biomedical Engineering and Pediatrics - Division of Cardiology, Herma Heart Institute, Children's Wisconsin and the Medical College of Wisconsin, and the MARquette Visualization Lab, Marquette University, Milwaukee, Wisconsin, USA
| | - Paul Iaizzo
- Visible Heart Laboratories, Department of Surgery, University of Minnesota, Minnesota, USA
| | - Yoshinobu Murasato
- Department of Cardiology, National Hospital Organization Kyushu Medical Center, Fukuoka, Japan
| | - Gabriele Dubini
- Department of Chemistry, Materials and Chemical Engineering 'Giulio Natta', Politecnico di Milano, Milan, Italy
| | - Francesco Migliavacca
- Department of Chemistry, Materials and Chemical Engineering 'Giulio Natta', Politecnico di Milano, Milan, Italy
| | | | - Andrew Bicek
- Boston Scientific Inc, Marlborough, Massachusetts, USA
| | | | | | | | - Pamela J Boyers
- Interprofessional Experiential Center for Enduring Learning, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Jeffrey P Gold
- Interprofessional Experiential Center for Enduring Learning, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Daniel R Anderson
- Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - James E Tcheng
- Cardiovascular Division, Duke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina, USA
| | - John R Windle
- Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Habib Samady
- Georgia Heart Institute, Gainesville, Georgia, USA
| | - Farouc A Jaffer
- Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Nihar R Desai
- Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Alexandra Lansky
- Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Carlos Mena-Hurtado
- Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Dawn Abbott
- Cardiovascular Institute, Warren Alpert Medical School at Brown University, Providence, Rhode Island, USA
| | - Emmanouil S Brilakis
- Center for Advanced Coronary Interventions, Minneapolis Heart Institute, Minneapolis, Minnesota, USA
| | - Jens Flensted Lassen
- Department of Cardiology B, Odense University Hospital, Odense, Syddanmark, Denmark
| | - Yves Louvard
- Institut Cardiovasculaire Paris Sud, Massy, France
| | - Goran Stankovic
- Department of Cardiology, Clinical Center of Serbia, Belgrade, Serbia
| | - Patrick W Serruys
- Department of Cardiology, National University of Ireland, Galway, Galway, Ireland
| | - Eric Velazquez
- Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Pierre Elias
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, New York, New York, USA
| | - Deepak L Bhatt
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - George Dangas
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yiannis S Chatzizisis
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA.
| |
Collapse
|
41
|
Li Y, Gu W, Yue H, Lei G, Guo W, Wen Y, Tang H, Luo X, Tu W, Ye J, Hong R, Cai Q, Gu Q, Liu T, Miao B, Wang R, Ren J, Lei W. Real-time detection of laryngopharyngeal cancer using an artificial intelligence-assisted system with multimodal data. J Transl Med 2023; 21:698. [PMID: 37805551 PMCID: PMC10559609 DOI: 10.1186/s12967-023-04572-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 09/23/2023] [Indexed: 10/09/2023] Open
Abstract
BACKGROUND Laryngopharyngeal cancer (LPC) includes laryngeal and hypopharyngeal cancer, whose early diagnosis can significantly improve the prognosis and quality of life of patients. Pathological biopsy of suspicious cancerous tissue under the guidance of laryngoscopy is the gold standard for diagnosing LPC. However, this subjective examination largely depends on the skills and experience of laryngologists, which increases the possibility of missed diagnoses and repeated unnecessary biopsies. We aimed to develop and validate a deep convolutional neural network-based Laryngopharyngeal Artificial Intelligence Diagnostic System (LPAIDS) for real-time automatically identifying LPC in both laryngoscopy white-light imaging (WLI) and narrow-band imaging (NBI) images to improve the diagnostic accuracy of LPC by reducing diagnostic variation among on-expert laryngologists. METHODS All 31,543 laryngoscopic images from 2382 patients were categorised into training, verification, and test sets to develop, validate, and internal test LPAIDS. Another 25,063 images from five other hospitals were used as external tests. Overall, 551 videos were used to evaluate the real-time performance of the system, and 200 randomly selected videos were used to compare the diagnostic performance of the LPAIDS with that of laryngologists. Two deep-learning models using either WLI (model W) or NBI (model N) images were constructed to compare with LPAIDS. RESULTS LPAIDS had a higher diagnostic performance than models W and N, with accuracies of 0·956 and 0·949 in the internal image and video tests, respectively. The robustness and stability of LPAIDS were validated in external sets with the area under the receiver operating characteristic curve values of 0·965-0·987. In the laryngologist-machine competition, LPAIDS achieved an accuracy of 0·940, which was comparable to expert laryngologists and outperformed other laryngologists with varying qualifications. CONCLUSIONS LPAIDS provided high accuracy and stability in detecting LPC in real-time, which showed great potential for using LPAIDS to improve the diagnostic accuracy of LPC by reducing diagnostic variation among on-expert laryngologists.
Collapse
Affiliation(s)
- Yun Li
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510080, Guangdong, China
| | - Wenxin Gu
- School of Computer Science and Engineering, Guangdong Province Key Lab of Computational Science, Sun Yat-Sen University, Guangzhou, 510006, Guangdong, China
| | - Huijun Yue
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510080, Guangdong, China
| | - Guoqing Lei
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510080, Guangdong, China
| | - Wenbin Guo
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510080, Guangdong, China
| | - Yihui Wen
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510080, Guangdong, China
| | - Haocheng Tang
- Department of Otolaryngology-Head and Neck Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Xin Luo
- Department of Otolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Wenjuan Tu
- Department of Otolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Jin Ye
- Department of Otolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Ruomei Hong
- Department of Otolaryngology-Head and Neck, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Qian Cai
- Department of Otolaryngology-Head and Neck, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Qingyu Gu
- Department of Otorhinolaryngology-Head and Neck Surgery, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Tianrun Liu
- Department of Otorhinolaryngology-Head and Neck Surgery, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Beiping Miao
- Department of Otolaryngology-Head and Neck Surgery, Shenzhen Secondary Hospital and First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China
| | - Ruxin Wang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Jiangtao Ren
- School of Computer Science and Engineering, Guangdong Province Key Lab of Computational Science, Sun Yat-Sen University, Guangzhou, 510006, Guangdong, China.
| | - Wenbin Lei
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510080, Guangdong, China.
| |
Collapse
|
42
|
Vasile CM, Iriart X. Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve. Diagnostics (Basel) 2023; 13:3137. [PMID: 37835880 PMCID: PMC10572870 DOI: 10.3390/diagnostics13193137] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 09/26/2023] [Accepted: 10/03/2023] [Indexed: 10/15/2023] Open
Abstract
Advancements in artificial intelligence (AI) have rapidly transformed various sectors, and the field of echocardiography is no exception. AI-driven technologies hold immense potential to revolutionize echo labs' diagnostic capabilities and improve patient care. This paper explores the importance for echo labs to embrace AI and stay ahead of the curve in harnessing its power. Our manuscript provides an overview of the growing impact of AI on medical imaging, specifically echocardiography. It highlights how AI-driven algorithms can enhance image quality, automate measurements, and accurately diagnose cardiovascular diseases. Additionally, we emphasize the importance of training echo lab professionals in AI implementation to optimize its integration into routine clinical practice. By embracing AI, echo labs can overcome challenges such as workload burden and diagnostic accuracy variability, improving efficiency and patient outcomes. This paper highlights the need for collaboration between echocardiography laboratory experts, AI researchers, and industry stakeholders to drive innovation and establish standardized protocols for implementing AI in echocardiography. In conclusion, this article emphasizes the importance of AI adoption in echocardiography labs, urging practitioners to proactively integrate AI technologies into their workflow and take advantage of their present opportunities. Embracing AI is not just a choice but an imperative for echo labs to maintain their leadership and excel in delivering state-of-the-art cardiac care in the era of advanced medical technologies.
Collapse
Affiliation(s)
- Corina Maria Vasile
- Department of Pediatric and Adult Congenital Cardiology, Bordeaux University Hospital, 33600 Pessac, France
| | - Xavier Iriart
- Department of Pediatric and Adult Congenital Cardiology, Bordeaux University Hospital, 33600 Pessac, France
- IHU Liryc—Electrophysiology and Heart Modelling Institute, Bordeaux University Foundation, 33600 Pessac, France
| |
Collapse
|
43
|
Kusunose K. Echocardiographic Phenotyping of Mitral Regurgitation for Clinical Decision Making. JACC Cardiovasc Imaging 2023; 16:1268-1270. [PMID: 37178076 DOI: 10.1016/j.jcmg.2023.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 03/02/2023] [Indexed: 05/15/2023]
Affiliation(s)
- Kenya Kusunose
- Department of Cardiovascular Medicine, Tokushima University Hospital, Tokushima, Japan.
| |
Collapse
|
44
|
Meng Q, Yu P, Yin S, Li X, Chang Y, Xu W, Wu C, Xu N, Zhang H, Wang Y, Shen H, Zhang R, Zhang Q. Coronary computed tomography angiography analysis using artificial intelligence for stenosis quantification and stent segmentation: a multicenter study. Quant Imaging Med Surg 2023; 13:6876-6886. [PMID: 37869330 PMCID: PMC10585569 DOI: 10.21037/qims-23-423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 08/30/2023] [Indexed: 10/24/2023]
Abstract
Background Accurate interpretation of coronary computed tomography angiography (CCTA) is a labor-intensive and expertise-driven endeavor, as inexperienced readers may inadvertently overestimate stenosis severity. Recent artificial intelligence (AI) advances in medical imaging present compelling prospects for auxiliary diagnostic tools in CCTA. This study aimed to externally validate an AI-assisted analysis system capable of rapidly evaluating stenosis severity, exploring its potential integration into routine clinical workflows. Methods This multicenter study consisted of an internal and external cohort of patients who underwent CCTA scans between April 2017 and February 2023. CCTA scans were evaluated using Coronary Artery Disease Reporting and Data System (CAD-RADS) scores to determine stenosis severity, while ground-truth stents were manually annotated by expert readers. The InferRead CT Heart (version 1.6; Infervision Medical Technology Co., Ltd., Beijing, China), which incorporates AI-assisted coronary artery stenosis quantification and automatic stent segmentation, was employed for CCTA scan analysis. AI-based stenosis assessment performance was determined using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), while the AI-based stent segmentation overlap was assessed using the Dice similarity coefficient (DSC). Results For ≥50% stenosis diagnoses, the AI system attained per-patient sensitivity, specificity, PPV, and NPV surpassing 90.0% for the internal dataset; for the external dataset, the per-patient values were 88.0% [95% confidence interval (CI): 81.0-94.4%], 94.5% (95% CI: 90.7-97.6%), 90.0% (95% CI: 83.3-95.6%), and 93.4% (95% CI: 89.2-96.8%), respectively. For ≥70% stenosis diagnoses, the per-patient values on the internal dataset were 94.2% (95% CI: 89.2-98.1%), 95.8% (95% CI: 94.1-97.4%), 80.8% (95% CI: 73.5-87.7%), and 98.9% (95% CI: 97.9-99.6%), respectively; for the external dataset, the per-patient values were 91.9% (95% CI: 82.6-100.0%), 97.3% (95% CI: 94.9-99.1%), 85.0% (95% CI: 72.5-94.6%), and 98.6% (95% CI: 96.8-100.0%), respectively. Regarding CAD-RADS categorization, the Cohen kappa was 0.75 and 0.81 for the internal per-patient and per-vessel basis, respectively, and 0.72 and 0.76 for the external per-patient and per-vessel basis, respectively. The DSC for stent segmentation was 0.96±0.06. Conclusions The AI-assisted analysis system for CCTA interpretation exhibited exceptional proficiency in stenosis quantification and stent segmentation, indicating that AI holds considerable potential in advancing CCTA postprocessing techniques.
Collapse
Affiliation(s)
- Qingtao Meng
- Department of Radiology, The Affiliated Chuzhou Hospital of Anhui Medical University, Chuzhou, China
| | - Pengxin Yu
- Infervision Medical Technology Co., Ltd., Beijing, China
| | - Siyuan Yin
- Infervision Medical Technology Co., Ltd., Beijing, China
| | - Xiaofeng Li
- Infervision Medical Technology Co., Ltd., Beijing, China
| | - Yitong Chang
- Department of Radiology, The Affiliated Chuzhou Hospital of Anhui Medical University, Chuzhou, China
| | - Wei Xu
- Department of Radiology, The Affiliated Chuzhou Hospital of Anhui Medical University, Chuzhou, China
| | - Chunmao Wu
- Department of Radiology, The Affiliated Chuzhou Hospital of Anhui Medical University, Chuzhou, China
| | - Na Xu
- Department of Radiology, The Affiliated Chuzhou Hospital of Anhui Medical University, Chuzhou, China
| | - Huan Zhang
- Infervision Medical Technology Co., Ltd., Beijing, China
| | - Yu Wang
- Infervision Medical Technology Co., Ltd., Beijing, China
| | - Hong Shen
- Infervision Medical Technology Co., Ltd., Beijing, China
| | - Rongguo Zhang
- Infervision Medical Technology Co., Ltd., Beijing, China
| | - Qingyue Zhang
- Infervision Medical Technology Co., Ltd., Beijing, China
| |
Collapse
|
45
|
Valiyaveettil D, Joseph D, Malik M. Cardiotoxicity in breast cancer treatment: Causes and mitigation. Cancer Treat Res Commun 2023; 37:100760. [PMID: 37714054 DOI: 10.1016/j.ctarc.2023.100760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 08/25/2023] [Accepted: 09/06/2023] [Indexed: 09/17/2023]
Abstract
Survivorship issues and treatment related toxicities have considerably increased in breast cancer patients following improved therapeutic options. Cardiotoxicity has been a major treatment related side effects in these patients. Despite this being a well-known entity, the real magnitude of the problem remains an enigma. The amount of research in mitigation of cardiotoxicity or its management in breast cancer survivors is limited and there is an urgent need for finding solutions for the problem. In this article, we are reviewing the agents that cause cardiotoxicity and suggesting a proposal for follow up of breast cancer survivors in an attempt to reduce the magnitude of impact on their quality of life.
Collapse
Affiliation(s)
| | - Deepa Joseph
- Department of Radiation Oncology, All India Institute of Medical sciences, Rishikesh, India.
| | - Monica Malik
- Nizam's Institute of Medical sciences, Hyderabad, India
| |
Collapse
|
46
|
Woodward G, Bajre M, Bhattacharyya S, Breen M, Chiocchia V, Dawes H, Dehbi HM, Descamps T, Frangou E, Fazakarley CA, Harris V, Hawkes W, Hewer O, Johnson CL, Krasner S, Laidlaw L, Lau J, Marwick T, Petersen SE, Piotrowska H, Ridgeway G, Ripley DP, Sanderson E, Savage N, Sarwar R, Tetlow L, Thompson B, Thulborn S, Williamson V, Woodward W, Upton R, Leeson P. PROTEUS Study: A Prospective Randomized Controlled Trial Evaluating the Use of Artificial Intelligence in Stress Echocardiography. Am Heart J 2023; 263:123-132. [PMID: 37192698 DOI: 10.1016/j.ahj.2023.05.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 05/03/2023] [Accepted: 05/04/2023] [Indexed: 05/18/2023]
Abstract
BACKGROUND Stress echocardiography (SE) is one of the most commonly used diagnostic imaging tests for coronary artery disease (CAD) but requires clinicians to visually assess scans to identify patients who may benefit from invasive investigation and treatment. EchoGo Pro provides an automated interpretation of SE based on artificial intelligence (AI) image analysis. In reader studies, use of EchoGo Pro when making clinical decisions improves diagnostic accuracy and confidence. Prospective evaluation in real world practice is now important to understand the impact of EchoGo Pro on the patient pathway and outcome. METHODS PROTEUS is a randomized, multicenter, 2-armed, noninferiority study aiming to recruit 2,500 participants from National Health Service (NHS) hospitals in the UK referred to SE clinics for investigation of suspected CAD. All participants will undergo a stress echocardiogram protocol as per local hospital policy. Participants will be randomized 1:1 to a control group, representing current practice, or an intervention group, in which clinicians will receive an AI image analysis report (EchoGo Pro, Ultromics Ltd, Oxford, UK) to use during image interpretation, indicating the likelihood of severe CAD. The primary outcome will be appropriateness of clinician decision to refer for coronary angiography. Secondary outcomes will assess other health impacts including appropriate use of other clinical management approaches, impact on variability in decision making, patient and clinician qualitative experience and a health economic analysis. DISCUSSION This will be the first study to assess the impact of introducing an AI medical diagnostic aid into the standard care pathway of patients with suspected CAD being investigated with SE. TRIAL REGISTRATION Clinicaltrials.gov registration number NCT05028179, registered on 31 August 2021; ISRCTN: ISRCTN15113915; IRAS ref: 293515; REC ref: 21/NW/0199.
Collapse
Affiliation(s)
| | - Mamta Bajre
- Oxford Academic Health Science Network, Magdalen Centre-Whitehead Building, Oxford, UK
| | - Sanjeev Bhattacharyya
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK
| | | | - Virginia Chiocchia
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Helen Dawes
- Oxford Clinical Allied Technology and Trial Services Unit, Oxford Brookes University, Oxford, UK
| | - Hakim-Moulay Dehbi
- UCL Comprehensive Clinical Trials Unit, University College London, London, UK
| | | | | | | | - Victoria Harris
- Department of Primary Health Care Sciences, University of Oxford, Oxford, UK
| | | | | | - Casey L Johnson
- Cardiovascular Clinical Research Facility, RDM Division of Cardiovascular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Samuel Krasner
- Cardiovascular Clinical Research Facility, RDM Division of Cardiovascular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Lynn Laidlaw
- British Society of Echocardiography. (BSE) Wavelength PPI group member, NIHR and BMJ lay reviewer
| | | | - Tom Marwick
- Baker Heart and Diabetes Institute, Melbourne Victoria, Australia
| | - Steffen E Petersen
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK; William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London, UK
| | | | - Ged Ridgeway
- Oxford Brain Diagnostics Ltd, Oxford Centre for Innovation, Oxford, UK
| | - David P Ripley
- School of Medicine, Faculty of Health Sciences and Wellbeing, University of Sunderland, London, UK
| | | | - Natalie Savage
- Cardiovascular Clinical Research Facility, RDM Division of Cardiovascular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | | | | | | | | | | | - William Woodward
- Cardiovascular Clinical Research Facility, RDM Division of Cardiovascular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | | | - Paul Leeson
- Cardiovascular Clinical Research Facility, RDM Division of Cardiovascular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK.
| |
Collapse
|
47
|
Khan MS, Arshad MS, Greene SJ, Van Spall HGC, Pandey A, Vemulapalli S, Perakslis E, Butler J. Artificial intelligence and heart failure: A state-of-the-art review. Eur J Heart Fail 2023; 25:1507-1525. [PMID: 37560778 DOI: 10.1002/ejhf.2994] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 08/06/2023] [Accepted: 08/08/2023] [Indexed: 08/11/2023] Open
Abstract
Heart failure (HF) is a heterogeneous syndrome affecting more than 60 million individuals globally. Despite recent advancements in understanding of the pathophysiology of HF, many issues remain including residual risk despite therapy, understanding the pathophysiology and phenotypes of patients with HF and preserved ejection fraction, and the challenges related to integrating a large amount of disparate information available for risk stratification and management of these patients. Risk prediction algorithms based on artificial intelligence (AI) may have superior predictive ability compared to traditional methods in certain instances. AI algorithms can play a pivotal role in the evolution of HF care by facilitating clinical decision making to overcome various challenges such as allocation of treatment to patients who are at highest risk or are more likely to benefit from therapies, prediction of adverse outcomes, and early identification of patients with subclinical disease or worsening HF. With the ability to integrate and synthesize large amounts of data with multidimensional interactions, AI algorithms can supply information with which physicians can improve their ability to make timely and better decisions. In this review, we provide an overview of the AI algorithms that have been developed for establishing early diagnosis of HF, phenotyping HF with preserved ejection fraction, and stratifying HF disease severity. This review also discusses the challenges in clinical deployment of AI algorithms in HF, and the potential path forward for developing future novel learning-based algorithms to improve HF care.
Collapse
Affiliation(s)
| | | | - Stephen J Greene
- Division of Cardiology, Duke University School of Medicine, Durham, NC, USA
- Duke Clinical Research Institute, Durham, NC, USA
| | - Harriette G C Van Spall
- Department of Medicine and Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Ambarish Pandey
- Canada Population Health Research Institute, Hamilton, ON, Canada
- Division of Cardiology, Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, USA
| | - Sreekanth Vemulapalli
- Division of Cardiology, Duke University School of Medicine, Durham, NC, USA
- Duke Clinical Research Institute, Durham, NC, USA
| | | | - Javed Butler
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
- Baylor Scott and White Research Institute, Dallas, TX, USA
| |
Collapse
|
48
|
Xu Y, Zheng X, Li Y, Ye X, Cheng H, Wang H, Lyu J. Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review. J Evid Based Med 2023; 16:342-375. [PMID: 37718729 DOI: 10.1111/jebm.12548] [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: 07/04/2023] [Accepted: 08/30/2023] [Indexed: 09/19/2023]
Abstract
BACKGROUND Increasingly, patient medication adherence data are being consolidated from claims databases and electronic health records (EHRs). Such databases offer an indirect avenue to gauge medication adherence in our data-rich healthcare milieu. The surge in data accessibility, coupled with the pressing need for its conversion to actionable insights, has spotlighted data mining, with machine learning (ML) emerging as a pivotal technique. Nonadherence poses heightened health risks and escalates medical costs. This paper elucidates the synergistic interaction between medical database mining for medication adherence and the role of ML in fostering knowledge discovery. METHODS We conducted a comprehensive review of EHR applications in the realm of medication adherence, leveraging ML techniques. We expounded on the evolution and structure of medical databases pertinent to medication adherence and harnessed both supervised and unsupervised ML paradigms to delve into adherence and its ramifications. RESULTS Our study underscores the applications of medical databases and ML, encompassing both supervised and unsupervised learning, for medication adherence in clinical big data. Databases like SEER and NHANES, often underutilized due to their intricacies, have gained prominence. Employing ML to excavate patient medication logs from these databases facilitates adherence analysis. Such findings are pivotal for clinical decision-making, risk stratification, and scholarly pursuits, aiming to elevate healthcare quality. CONCLUSION Advanced data mining in the era of big data has revolutionized medication adherence research, thereby enhancing patient care. Emphasizing bespoke interventions and research could herald transformative shifts in therapeutic modalities.
Collapse
Affiliation(s)
- Yixian Xu
- Department of Anesthesiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Xinkai Zheng
- Department of Dermatology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Yuanjie Li
- Planning & Discipline Construction Office, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Xinmiao Ye
- Department of Anesthesiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Hongtao Cheng
- School of Nursing, Jinan University, Guangzhou, China
| | - Hao Wang
- Department of Anesthesiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jun Lyu
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Guangzhou, China
| |
Collapse
|
49
|
Hurvitz N, Ilan Y. The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from "Nice to Have" to Mandatory Systems. Clin Pract 2023; 13:994-1014. [PMID: 37623270 PMCID: PMC10453547 DOI: 10.3390/clinpract13040089] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 08/16/2023] [Accepted: 08/18/2023] [Indexed: 08/26/2023] Open
Abstract
The success of artificial intelligence depends on whether it can penetrate the boundaries of evidence-based medicine, the lack of policies, and the resistance of medical professionals to its use. The failure of digital health to meet expectations requires rethinking some of the challenges faced. We discuss some of the most significant challenges faced by patients, physicians, payers, pharmaceutical companies, and health systems in the digital world. The goal of healthcare systems is to improve outcomes. Assisting in diagnosing, collecting data, and simplifying processes is a "nice to have" tool, but it is not essential. Many of these systems have yet to be shown to improve outcomes. Current outcome-based expectations and economic constraints make "nice to have," "assists," and "ease processes" insufficient. Complex biological systems are defined by their inherent disorder, bounded by dynamic boundaries, as described by the constrained disorder principle (CDP). It provides a platform for correcting systems' malfunctions by regulating their degree of variability. A CDP-based second-generation artificial intelligence system provides solutions to some challenges digital health faces. Therapeutic interventions are held to improve outcomes with these systems. In addition to improving clinically meaningful endpoints, CDP-based second-generation algorithms ensure patient and physician engagement and reduce the health system's costs.
Collapse
Affiliation(s)
| | - Yaron Ilan
- Hadassah Medical Center, Department of Medicine, Faculty of Medicine, Hebrew University, POB 1200, Jerusalem IL91120, Israel;
| |
Collapse
|
50
|
Bienstock S, Lin F, Blankstein R, Leipsic J, Cardoso R, Ahmadi A, Gelijns A, Patel K, Baldassarre LA, Hadley M, LaRocca G, Sanz J, Narula J, Chandrashekhar YS, Shaw LJ, Fuster V. Advances in Coronary Computed Tomographic Angiographic Imaging of Atherosclerosis for Risk Stratification and Preventive Care. JACC Cardiovasc Imaging 2023; 16:1099-1115. [PMID: 37178070 DOI: 10.1016/j.jcmg.2023.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 01/04/2023] [Accepted: 02/01/2023] [Indexed: 05/15/2023]
Abstract
The diagnostic evaluation of coronary artery disease is undergoing a dramatic transformation with a new focus on atherosclerotic plaque. This review details the evidence needed for effective risk stratification and targeted preventive care based on recent advances in automated measurement of atherosclerosis from coronary computed tomography angiography (CTA). To date, research findings support that automated stenosis measurement is reasonably accurate, but evidence on variability by location, artery size, or image quality is unknown. The evidence for quantification of atherosclerotic plaque is unfolding, with strong concordance reported between coronary CTA and intravascular ultrasound measurement of total plaque volume (r >0.90). Statistical variance is higher for smaller plaque volumes. Limited data are available on how technical or patient-specific factors result in measurement variability by compositional subgroups. Coronary artery dimensions vary by age, sex, heart size, coronary dominance, and race and ethnicity. Accordingly, quantification programs excluding smaller arteries affect accuracy for women, patients with diabetes, and other patient subsets. Evidence is unfolding that quantification of atherosclerotic plaque is useful to enhance risk prediction, yet more evidence is required to define high-risk patients across varied populations and to determine whether such information is incremental to risk factors or currently used coronary computed tomography techniques (eg, coronary artery calcium scoring or visual assessment of plaque burden or stenosis). In summary, there is promise for the utility of coronary CTA quantification of atherosclerosis, especially if it can lead to targeted and more intensive cardiovascular prevention, notably for those patients with nonobstructive coronary artery disease and high-risk plaque features. The new quantification techniques available to imagers must not only provide sufficient added value to improve patient care, but also add minimal and reasonable cost to alleviate the financial burden on our patients and the health care system.
Collapse
Affiliation(s)
- Solomon Bienstock
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Fay Lin
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ron Blankstein
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jonathon Leipsic
- University of British Columbia, Vancouver, British Columbia, Canada
| | - Rhanderson Cardoso
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Amir Ahmadi
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Annetine Gelijns
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Krishna Patel
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Lauren A Baldassarre
- Department of Cardiovascular Medicine and Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Michael Hadley
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Gina LaRocca
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Javier Sanz
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jagat Narula
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Leslee J Shaw
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
| | - Valentin Fuster
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| |
Collapse
|