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

Anjan Gudigar, Nahrizul Adib Kadri, U. Raghavendra, Jyothi Samanth, M. Maithri, Mahesh Anil Inamdar, Mukund A. Prabhu, Ajay Hegde, Massimo Salvi, Chai Hong Yeong, Prabal Datta Barua, Filippo Molinari, U. Rajendra Acharya

Research output: Contribution to journalReview articlepeer-review

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.

Original languageEnglish
Article number108207
JournalComputers in Biology and Medicine
Volume172
DOIs
Publication statusPublished - 04-2024

All Science Journal Classification (ASJC) codes

  • Health Informatics
  • Computer Science Applications

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