Revolutionizing diabetic retinopathy screening and management: The role of artificial intelligence and machine learning (original) (raw)

Editorial

Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.

World J Clin Cases. Feb 16, 2025; 13(5): 101306
Published online Feb 16, 2025. doi: 10.12998/wjcc.v13.i5.101306

Table 1 Overview of diabetic retinopathy diagnostic tools

Tool Year introduced Country of origin Ref. Advantages Disadvantages AI/ML-based
Fundus photography Mid-20th century Germany Srinivasan __et al_[13], 2023 Established method for capturing detailed retinal images Resource-intensive requires specialized personnel, expensive, and not scalable in low-resource settings No
Optical coherence tomography 1991 United States Huang __et al_[24], 1991 High-resolution cross-sectional images; effective in detecting diabetic macular edema. High cost, requires specialized training, limited availability in low-resource settings No
Fluorescein angiography 1961 United States Norton and Gutman[27], 1965 Gold standard for visualizing retinal vasculature; highly precise. Invasive, requires dye injection, potential side effects, limited use in rural and low-income areas. No
Ultrawide-field imaging Early 2000s Canada, United Kingdom Nagiel __et al_[32], 2016 Captures up to 200 degrees of the retina; detects peripheral lesions often missed by standard imaging High cost, requires specialized training, limited adoption in low-resource settings No
Confocal scanning laser ophthalmoscopy Late 1980s Germany Webb __et al_[35], 1987 Provides high-resolution, high-contrast images; improves diagnostic accuracy for subtle abnormalities High cost, requires specialized training, limited adoption, particularly in low-resource settings No
Multispectral Imaging 2012 Canada Ma __et al_[36], 2023 Enhances contrast and detail in retinal images by capturing muliple wavelengths of light High cost, limited availability, not widely adopted in low-resource settings No
Smartphone-based retinal imaging Early 2010s United Kingdom Kim __et al_[37], 2018 Cost-effective, portable, accessible; useful in remote and low-resource settings Variable image quality depending on lighting and operator skill; requires adequate training No
Hyperspectral imaging Early 2010s Canada Akbari and Kosugi[39], 2009 Captures detailed biochemical information; high accuracy in tissue composition analysis; valuable for early detection Complex, expensive, not widely available, limited adoption in clinical practice No
Photoacoustic imaging Early 2010s United States Hu and Wang[43], 2010 Combines laser-induced ultrasound with optical imaging; provides functional assessment of the retina Still in research phase, high cost, complex, limited clinical application No
Teleophthalmology Early 2000s United States Whited[44], 2006 Expands access to DR screening, particularly in underserved areas; allows remote retinal imaging and analysis Dependent on internet connectivity, requires high-quality imaging devices and trained personnel, lack of direct patient interaction No
AI and ML algorithms 2018, 2020 United States Esmaeilzadeh[48], 2024 High sensitivity and specificity; automates retinal image analysis; provides immediate diagnostic feedback High initial investment, requires continuous algorithm updates, data privacy concerns, integration challenges in clinical workflows Yes