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Central Serous Retinopathy (CSR) or Central Serous Chorioretinopathy (CSC) isa significant disease that causes blindness and vision loss among millions ofpeople worldwide. It transpires as a result of accumulation of watery fluidsbehind the retina. Therefore, detection of CSR at early stages allowspreventive 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 automatedCSR detection, are now possible to detect and cure this disease. This reviewassessed a variety of innovative technologies and researches that contribute tothe automatic detection of CSR. In this review, various CSR disease detectiontechniques, broadly classified into two categories: a) CSR detection based onclassical imaging technologies, and b) CSR detection based on Machine/DeepLearning methods, have been reviewed after an elaborated evaluation of 29different relevant articles. Additionally, it also goes over the advantages,drawbacks and limitations of a variety of traditional imaging techniques, suchas Optical Coherence Tomography Angiography (OCTA), Fundus Imaging and morerecent approaches that utilize Artificial Intelligence techniques. Finally, itis concluded that the most recent Deep Learning (DL) classifiers deliveraccurate, fast, and reliable CSR detection. However, more research needs to beconducted on publicly available datasets to improve computation complexity forthe reliable detection and diagnosis of CSR disease.

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