Diagnosis of chronic stage of hypertensive retinopathy based on spectral domain optical coherence tomography

Artificial intelligence in retinal image analysis for hypertensive retinopathy diagnosis: a comprehensive review and perspective

Hypertensive retinopathy (HR) occurs when the choroidal vessels, which form the photosensitive layer at the back of the eye, are injured owing to high blood pressure. Artificial intelligence (AI) in retinal image analysis (RIA) for HR diagnosis involves the use of advanced computational algorithms and machine learning (ML) strategies to recognize and evaluate signs of HR in retinal images automatically. This review aims to advance the field of HR diagnosis by investigating the latest ML and deep learning techniques, and highlighting their efficacy and capability for early diagnosis and intervention. By analyzing recent advancements and emerging trends, this study seeks to inspire further innovation in automated RIA. In this context, AI shows significant potential for enhancing the accuracy, effectiveness, and consistency of HR diagnoses. This will eventually lead to better clinical results by enabling earlier intervention and precise management of the condition. Overall, the integration of AI into RIA represents a considerable step forward in the early identification and treatment of HR, offering substantial benefits to both healthcare providers and patients.

Computer-aided diagnostic system for hypertensive retinopathy: A review

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.

Diagnosis of chronic stage of hypertensive retinopathy based on spectral domain optical coherence tomography

Hypertensive retinopathy refers to the retinal vascular changes associated with systemic arterial hypertension. Hypertensive retinopathy can be divided into chronic and acute phases. A cross-sectional study was performed to explore a method of measurement in the diameters of retinal vessels for diagnosis of chronic hypertensive retinopathy based on spectral domain optical coherence tomography (SD-OCT). The central retinal artery diameter (CRAD), the central retinal vein diameter (CRVD), and the artery-to-vein ratio (AVR) were measured. A total of 119 subjects with 119 eyes were included in this study, in which 56 subjects with 56 eyes were included in hypertensive group and 63 subjects with 63 eyes were included in normotensive group. There were significant differences between the two groups in the CRAD (t = -2.14, P = .04) and the AVR (t = -2.59, P = .01). The cutoff point of 0.75 was determined by receiver operating characteristic (ROC) curve (area under the curve, AUC 0.786; 95% confidence interval, 95% CI 0.70-0.87). Multivariate logistic regression analysis showed the probability of AVR below to 0.75 was more in patients with high systolic blood pressure (odds ratio OR 4.39; P = .048), more in male (OR 4.15; P = .004) and more in smokers (OR 5.80; P = .01). Bland-Altman plots showed small mean bias between the measurements of the two technicians in the CRAD, the CRVD, and the AVR. In summary, application of SD-OCT is an accurate, reproducible, convenient method for measuring the diameters of retinal vessels. It is valuable for the diagnosis of chronic stage of hypertensive retinopathy.