Year: 2026
Type: article
Abstract: Ophthalmic diseases establish a vital global health concern, affecting millions of persons worldwide and affect vision loss which increases impact on the quality of life and economic burden. Classifying diseases is a crucial aspect of the ophthalmology, as it helps in diagnosis, treatment and research. It encompasses a wide range of conditions affecting the eyes and its surrounding. The classification can be based on anatomy, clinical presentation, age-related factors, systemic associations and traumatic causes. Ophthalmologists use these classifications to provide targeted treatments and interventions. In this review paper, we converse the impact of diseases on individuals and society, and image modalities used to classify and detect disease with severity. Additionally, this study explores risk factors along with the need for dedicated research, intervention strategies, diagnosis challenges and proposed system. By understanding the various circumstances, this study helps to diagnose the eye disease with its severity. This study reveals that 28% of researchers used the Messidor dataset with 24% applied CNN techniques, along with 57% diseases focusing on diabetic retinopathy. Despite existing methods, there is scope for improvement in detecting multiple ophthalmic diseases, which the proposed system aims to address by enhancing detection and classification with severity within a single framework.
Authors: Shraddha S. Shinde, Swati A. Bhavsar
Institution: Savitribai Phule Pune University
Language: English
Topic: Retinal Imaging and Analysis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7129002801