Computer-aided diagnostic system for hypertensive retinopathy: A review.

Autor: Suman S; Interdisciplinary Research Platform (IDRP): Smart Healthcare, Indian Institute of Technology, N.H. 62, Nagaur Road, Karwar, Jodhpur, Rajasthan 342030, India. Electronic address: suman.4@iitj.ac.in., Tiwari AK; Department of Electrical Engineering, Indian Institute of Technology, N.H. 62, Nagaur Road, Karwar, Jodhpur, Rajasthan 342030, India., Singh K; Department of Pediatrics, All India Institute of Medical Sciences, Basni Industrial Area Phase-2, Jodhpur, Rajasthan 342005, India.
Jazyk: angličtina
Zdroj: Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2023 Oct; Vol. 240, pp. 107627. Date of Electronic Publication: 2023 Jun 02.
DOI: 10.1016/j.cmpb.2023.107627
Abstrakt: Hypertensive Retinopathy (HR) is a retinal disease caused by elevated blood pressure for a prolonged period. There are no obvious signs in the early stages of high blood pressure, but it affects various body parts over time, including the eyes. HR is a biomarker for several illnesses, including retinal diseases, atherosclerosis, strokes, kidney disease, and cardiovascular risks. Early microcirculation abnormalities in chronic diseases can be diagnosed through retinal examination prior to the onset of major clinical consequences. Computer-aided diagnosis (CAD) plays a vital role in the early identification of HR with improved diagnostic accuracy, which is time-efficient and demands fewer resources. Recently, numerous studies have been reported on the automatic identification of HR. This paper provides a comprehensive review of the automated tasks of Artery-Vein (A/V) classification, Arteriovenous ratio (AVR) computation, HR detection (Binary classification), and HR severity grading. The review is conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. The paper discusses the clinical features of HR, the availability of datasets, existing methods used for A/V classification, AVR computation, HR detection, and severity grading, and performance evaluation metrics. The reviewed articles are summarized with classifiers details, adoption of different kinds of methodologies, performance comparisons, datasets details, their pros and cons, and computational platform. For each task, a summary and critical in-depth analysis are provided, as well as common research issues and challenges in the existing studies. Finally, the paper proposes future research directions to overcome challenges associated with data set availability, HR detection, and severity grading.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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