Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013–2023)
Article
Gudigar, Anjan, Kadri, Nahrizul Adib, Raghavendra, U., Samanth, Jyothi, Maithri, M., Inamdar, Mahesh Anil, Prabhu, Mukund A., Hegde, Ajay, Salvi, Massimo, Yeong, Chai Hong, Barua, Prabal Datta, Molinari, Filippo and Acharya, U. Rajendra. 2024. "Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013–2023)." Computers in Biology and Medicine. 172. https://doi.org/10.1016/j.compbiomed.2024.108207
Article Title | Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013–2023) |
---|---|
ERA Journal ID | 5040 |
Article Category | Article |
Authors | Gudigar, Anjan, Kadri, Nahrizul Adib, Raghavendra, U., Samanth, Jyothi, Maithri, M., Inamdar, Mahesh Anil, Prabhu, Mukund A., Hegde, Ajay, Salvi, Massimo, Yeong, Chai Hong, Barua, Prabal Datta, Molinari, Filippo and Acharya, U. Rajendra |
Journal Title | Computers in Biology and Medicine |
Journal Citation | 172 |
Article Number | 108207 |
Number of Pages | 26 |
Year | 2024 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0010-4825 |
1879-0534 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.compbiomed.2024.108207 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0010482524002919 |
Abstract | 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. |
Keywords | Artificial intelligence; Clinical data ; Deep learning ; Hypertension; Physiological signals ; Imaging modalities ; Machine learning |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460299. Artificial intelligence not elsewhere classified |
Byline Affiliations | Manipal Academy of Higher Education, India |
University of Malaya, Malaysia | |
Manipal Hospitals, India | |
PolitoBIOMed Lab, Italy | |
Taylor's University, Malaysia | |
School of Business | |
University of Technology Sydney | |
Cogninet Australia, Australia | |
School of Mathematics, Physics and Computing | |
Centre for Health Research |
Permalink -
https://research.usq.edu.au/item/z84x5/automatic-identification-of-hypertension-and-assessment-of-its-secondary-effects-using-artificial-intelligence-a-systematic-review-2013-2023
Download files
32
total views37
total downloads0
views this month4
downloads this month