Bioscience Methods 2024, Vol.15, No.3, 124-138 http://bioscipublisher.com/index.php/bm 124 Feature Review Open Access Development of AI-Based Diagnostic Systems for Hypertensive Heart Disease Jianli Zhong Hainan Institute of Biotechnology, Haikou, 570206, Hainan, China Corresponding email: jianli.zhong@hibio.org Bioscience Methods, 2024, Vol.15, No.3 doi: 10.5376/bm.2024.15.0014 Received: 09 Apr., 2024 Accepted: 24 May, 2024 Published: 13 Jun., 2024 Copyright © 2024 Zhong, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Zhong J.L., 2024, Development of ai-based diagnostic systems for hypertensive heart disease, Bioscience Methods, 15(3): 124-138 (doi: 10.5376/bm.2024.15.0014) Abstract The development of AI-based diagnostic systems for hypertensive heart disease represents a significant advancement in cardiovascular medicine. This study explores the integration of artificial intelligence (AI) and machine learning (ML) technologies in the diagnosis, prediction, and management of hypertensive heart disease. AI applications, particularly deep learning (DL) and machine learning algorithms, have shown promise in enhancing diagnostic accuracy, personalizing treatment plans, and predicting disease progression. Wearable devices and mobile technologies equipped with AI capabilities enable continuous monitoring and early detection of hypertension-related complications. Despite the transformative potential, challenges such as data privacy, algorithm transparency, and the need for high-quality data remain. This study synthesizes recent research findings, highlighting the benefits and limitations of AI in hypertensive heart disease management, and underscores the importance of ongoing methodological advancements to fully realize the potential of AI in clinical practice. Keywords Artificial intelligence; Hypertensive heart disease; Machine learning; Diagnostic systems; Cardiovascular medicine 1 Introduction Hypertensive Heart Disease (HHD) is a significant cardiovascular condition resulting from prolonged high blood pressure, leading to various structural and functional changes in the heart. It encompasses a range of cardiac complications, including left ventricular hypertrophy (LVH), heart failure, and arrhythmias, which collectively contribute to increased cardiovascular morbidity and mortality (Díez and Frohlich, 2010; Nwabuo andVasan, 2020). The pathophysiology of HHD involves complex mechanisms such as myocardial fibrosis, cardiac remodeling, and neurohumoral alterations, which can affect both the left and right ventricles as well as th atria (Díez and Frohlich, 2010; Shenasa and Shenasa, 2017; Nwabuo and Vasan, 2020). Diagnosing HHD presents several challenges due to its heterogeneous nature and the overlap of its manifestations with other cardiovascular conditions. Traditional diagnostic methods, such as electrocardiography and echocardiography, primarily focus on detecting LVH but may not fully capture the extent of myocardial remodeling and fibrosis (Díez and Frohlich, 2010; Tadic et al., 2022). Advanced imaging techniques, including cardiac magnetic resonance and computed tomography, offer more detailed insights but are not always accessible or cost-effective (Tadic et al., 2022; Díez and Butler, 2022). Additionally, the early detection of subclinical HHD remains difficult, complicating timely intervention and management (Santos and Shah, 2014; Tadic et al., 2022). Artificial Intelligence (AI) has emerged as a transformative tool in modern diagnostics, offering the potential to enhance the accuracy and efficiency of HHD detection. AI algorithms can analyze large datasets from various imaging modalities, identify subtle patterns indicative of early HHD, and predict disease progression (Tadic et al., 2022; Díez and Butler, 2022). Machine learning models, in particular, can integrate clinical, imaging, and biomarker data to provide comprehensive diagnostic insights and personalized treatment recommendations (Tadic et al., 2022; Ismail et al., 2023). The application of AI in HHD diagnostics promises to overcome current limitations by enabling earlier detection, reducing diagnostic errors, and optimizing patient outcomes. This study explores the development and application of AI-based diagnostic systems for Hypertensive Heart Disease. It will provide an in-depth analysis of the current state of HHD diagnostics, highlight the challenges faced in clinical practice, and discuss the potential of AI technologies to address these challenges. By examining
RkJQdWJsaXNoZXIy MjQ4ODY0NQ==