Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013–2023)

被引：0
作者：
Gudigar A. [1 ]
Kadri N.A. [2 ]
Raghavendra U. [1 ]
Samanth J. [3 ]
Maithri M. [4 ]
Inamdar M.A. [4 ]
Prabhu M.A. [5 ]
Hegde A. [6 ]
Salvi M. [7 ]
Yeong C.H. [8 ]
Barua P.D. [9 ,10 ,11 ]
Molinari F. [7 ]
Acharya U.R. [12 ,13 ]
机构：
[1] Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal
[2] Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur
[3] Department of Cardiovascular Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal
[4] Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal
[5] Department of Cardiology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal
[6] Manipal Hospitals, Karnataka, Bengaluru
[7] Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnicodi Torino, Turin
[8] School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, Subang Jaya
[9] Cogninet Brain Team, Cogninet Australia, Sydney, 2010, NSW
[10] School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, 4350, QLD
[11] Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, 2007, NSW
[12] School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, 4300, QLD
[13] Centre for Health Research, University of Southern Queensland, Toowoomba, 4350, QLD
来源：
Computers in Biology and Medicine | 2024年 / 172卷
关键词：
Artificial intelligence; Clinical data; Deep learning; Hypertension; Imaging modalities; Machine learning; Physiological signals;
D O I：
10.1016/j.compbiomed.2024.108207
中图分类号：
学科分类号：
摘要：
Artificial Intelligence (AI) techniques are increasingly used in computer-aided diagnostic tools in medicine. These techniques can also help to identify Hypertension (HTN) in its early stage, as it is a global health issue. Automated HTN detection uses socio-demographic, clinical data, and physiological signals. Additionally, signs of secondary HTN can also be identified using various imaging modalities. This systematic review examines related work on automated HTN detection. We identify datasets, techniques, and classifiers used to develop AI models from clinical data, physiological signals, and fused data (a combination of both). Image-based models for assessing secondary HTN are also reviewed. The majority of the studies have primarily utilized single-modality approaches, such as biological signals (e.g., electrocardiography, photoplethysmography), and medical imaging (e.g., magnetic resonance angiography, ultrasound). Surprisingly, only a small portion of the studies (22 out of 122) utilized a multi-modal fusion approach combining data from different sources. Even fewer investigated integrating clinical data, physiological signals, and medical imaging to understand the intricate relationships between these factors. Future research directions are discussed that could build better healthcare systems for early HTN detection through more integrated modeling of multi-modal data sources. © 2024 The Authors
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