Recent Developments in Detection of Central Serous Retinopathy through Imaging and Artificial Intelligence Techniques A Review

TL;DR

This review discusses recent advancements in detecting Central Serous Retinopathy using traditional imaging and AI-based deep learning techniques, highlighting improved accuracy and speed but emphasizing the need for larger datasets.

Contribution

It provides a comprehensive evaluation of 29 studies on CSR detection methods, comparing classical imaging and AI approaches, and identifies current limitations and future research directions.

Findings

Deep learning classifiers offer accurate and fast CSR detection.

Traditional imaging techniques have limitations in reliability.

More publicly available datasets are needed for improved AI performance.

Abstract

Central Serous Retinopathy (CSR) or Central Serous Chorioretinopathy (CSC) is a significant disease that causes blindness and vision loss among millions of people worldwide. It transpires as a result of accumulation of watery fluids behind the retina. Therefore, detection of CSR at early stages allows preventive measures to avert any impairment to the human eye. Traditionally, several manual methods for detecting CSR have been developed in the past; however, they have shown to be imprecise and unreliable. Consequently, Artificial Intelligence (AI) services in the medical field, including automated CSR detection, are now possible to detect and cure this disease. This review assessed a variety of innovative technologies and researches that contribute to the automatic detection of CSR. In this review, various CSR disease detection techniques, broadly classified into two categories: a) CSR detection based on classical imaging technologies, and b) CSR detection based on Machine/Deep Learning methods, have been reviewed after an elaborated evaluation of 29 different relevant articles. Additionally, it also goes over the advantages, drawbacks and limitations of a variety of traditional imaging techniques, such as Optical Coherence Tomography Angiography (OCTA), Fundus Imaging and more recent approaches that utilize Artificial Intelligence techniques. Finally, it is concluded that the most recent Deep Learning (DL) classifiers deliver accurate, fast, and reliable CSR detection. However, more research needs to be conducted on publicly available datasets to improve computation complexity for the reliable detection and diagnosis of CSR disease.