BACKGROUND AND OBJECTIVE

In the field of ophthalmology, diabetic retinopathy (DR) is a diabetes-related eye condition that damages the retina. DR is a serious issue for working-age people and it is important to solve it at an early stage to avoid complete vision loss. The review covers together technical and clinical perspectives of using the artificial intelligence (AI)-based systems in clinical institutes to help the ophthalmologists interpret and diagnose DR at the early stages.

METHODS

AI plays a significant role in assisting ophthalmologists with timely and effective treatment of patients by early and accurate detection and classification of DR. The study selection method followed specified searching criteria to complete the data collection task in the area of DR screening through AI.

KEY CONTENT AND FINDINGS

The review covers literature published in the nearest one decade or more for analyzing automated DR diagnostics through the identification of retinal lesions and evaluates the approaches of advanced AI-based models for the development of early and accurate DR diagnostic processes. The DR-related datasets and performance evaluation metrics used for segmentation and classification tasks of DR, are also included in this review. Moreover, the authors performed critical analysis and provided possible solutions against DR-based potential problems.

CONCLUSIONS

Hence, the study serves as a helping guide for the researchers to utilize their skills with advanced AI-based approaches in the technical and clinical perspectives of DR diagnostics. In conclusion, a freely available repository is created to facilitate the relevant researchers with up-to-date articles and open-source implementations for DR screening at https://github.com/muhammadmateen319/progress-of-diabetic-retinopathy-screening.