Computer-aided diagnostic system for hypertensive retinopathy: A review

• Published in: Computer methods and programs in biomedicine Vol. 240; p. 107627
• Main Authors: Suman, Supriya, Tiwari, Anil Kumar, Singh, Kuldeep
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.10.2023
• Subjects: Computer-aided diagnosis system (CAD); Deep learning; Fundus image; Hypertension; Hypertensive Retinopathy; Machine learning; Medical imaging; Computer-aided diagnosis system (CAD); Hypertension; Medical imaging; CAD; A/V; DL; HR; Fundus image; Deep learning; OD; HAM; Machine learning; Hypertensive Retinopathy; AVR; CWS; ML
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2023.107627

•The review presents the comprehensive evolution of Computer Aided Diagnosis (CAD) system for automatic diagnosis of Hypertensive Retinopathy (HR).•Detailed analysis of the current research methodologies on conventional, machine learning, and deep learning techniques for HR diagnosis.•It provides the detailed summaries and analysis for each task and overall.•It discusses the pathophysiology, brief details of datasets, and, performance evaluation metrics for HR.•This study also highlights the challenges in the existing studies and proposes the future research directions. 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.