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Review Papers | Cardiology Science | India | Volume 12 Issue 11, November 2023
Efficacy of AI and Machine Learning in Diagnosing Heart Valve Disease: A Five - Year Review
Vansh Marwaha
Abstract: Background: Valvular heart diseases pose significant diagnostic challenges, prompting the exploration of artificial intelligence (AI) and machine learning (ML) to improve the accuracy and patient outcomes. This systematic review examines the recent five - year advancements in AI/ML applications for valvular heart disease diagnostics and their impact compared to traditional methods. Methods: A comprehensive search was conducted in PubMed and Google Scholar from October 2018 to October 2023 which yielded 326 articles. After title and abstract screening, 73 articles underwent full - text review, resulting in the exclusion of 70 articles due to reasons such as other heart related/diverse diseases, absence of AI/ML use, and lack of clinical validation. Finally, 03 articles met the rigorous inclusion criteria and were included in this systematic review. Results: The selected studies applied artificial intelligence (AI) and machine learning (ML) techniques in valvular heart disease diagnosis. These studies revealed significant insights, associating specific biomarkers with adverse outcomes in aortic stenosis, also developing ECG - based models effectively identifying high - risk patients with multiple heart conditions, and introducing precision algorithms that accurately predicted severe valvular stenosis, potentially reducing missed diagnoses and aiding clinical prioritization. Conclusions: Despite promising advancements in AI - driven valvular heart disease diagnostics, challenges include limited validation, echocardiographic accuracy issues, and diversity gaps. Future efforts must focus on refining algorithms, integrating diverse data, broader validation, and addressing demographic diversity to ensure accurate and clinically relevant diagnostic outcomes. Additionally, this systematic review evaluates the advancements in AI and ML for diagnosing valvular heart disease over the past five years. It analyzes three key studies, focusing on improved diagnostic accuracy, methodological approaches, and the impact on patient outcomes compared to traditional methods. The review highlights significant findings, underscores potential challenges, and suggests future directions for research in this domain.
Keywords: Artificial Intelligence (AI), Machine Learning (ML)
Edition: Volume 12 Issue 11, November 2023,
Pages: 1589 - 1594