  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.
