Revolutionizing diabetic retinopathy screening and management: The role of artificial intelligence and machine learning

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

AI: Artificial intelligence; ML: Machine learning.