Machine learning and artificial intelligence in cardiac transplantation: A systematic review
| العنوان: | Machine learning and artificial intelligence in cardiac transplantation: A systematic review |
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| المؤلفون: | Vinci Naruka, Arian Arjomandi Rad, Hariharan Subbiah Ponniah, Jeevan Francis, Robert Vardanyan, Panagiotis Tasoudis, Dimitrios E. Magouliotis, George L. Lazopoulos, Mohammad Yousuf Salmasi, Thanos Athanasiou |
| المصدر: | Artificial Organs. 46:1741-1753 |
| بيانات النشر: | Wiley, 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Databases, Factual, Biomedical Engineering, Medicine (miscellaneous), Bioengineering, General Medicine, Length of Stay, Machine Learning, Biomaterials, Databases, Artificial Intelligence, Heart Transplantation, Humans, Heart Transplantation/adverse effects, Factual |
| الوصف: | BACKGROUND: This review aims to systematically evaluate the currently available evidence investigating the use of artificial intelligence (AI) and machine learning (ML) in the field of cardiac transplantation. Furthermore, based on the challenges identified we aim to provide a series of recommendations and a knowledge base for future research in the field of ML and heart transplantation. METHODS: A systematic database search was conducted of original articles that explored the use of ML and/or AI in heart transplantation in EMBASE, MEDLINE, Cochrane database, and Google Scholar, from inception to November 2021. RESULTS: Our search yielded 237 articles, of which 13 studies were included in this review, featuring 463 850 patients. Three main areas of application were identified: (1) ML for predictive modeling of heart transplantation mortality outcomes; (2) ML in graft failure outcomes; (3) ML to aid imaging in heart transplantation. The results of the included studies suggest that AI and ML are more accurate in predicting graft failure and mortality than traditional scoring systems and conventional regression analysis. Major predictors of graft failure and mortality identified in ML models were: length of hospital stay, immunosuppressive regimen, recipient's age, congenital heart disease, and organ ischemia time. Other potential benefits include analyzing initial lab investigations and imaging, assisting a patient with medication adherence, and creating positive behavioral changes to minimize further cardiovascular risk. CONCLUSION: ML demonstrated promising applications for improving heart transplantation outcomes and patient-centered care, nevertheless, there remain important limitations relating to implementing AI into everyday surgical practices. |
| تدمد: | 1525-1594 0160-564X |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::42bd421e2347d386ad12b53485184c81 https://doi.org/10.1111/aor.14334 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....42bd421e2347d386ad12b53485184c81 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15251594 0160564X |
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