Artificial intelligence-based apps for screening and diagnosing diabetic retinopathy and common ocular disorders

Table 1 Various trials on artificial intelligence in ophthalmology

Ref.	Data included	Results
Kanagasingam et al[43], 2018	A total of 216 patients, out of which 193 agreed to undergo eye screening. 386 images were evaluated by an AI-based system and by an ophthalmologist	Specificity: 92%; Positive predictive value 12%; Detection of false positive cases attributed to poor image quality
Arenas-Cavalli et al[44], 2022	Examination of 1123 diabetic eyes, utilizing a well-designed protocol endorsed by the Chilean Ministry of Health Personnel and Retina Specialists	Sensitivity: 94.6%; Specificity: 74.3%
Peeters et al[10], 2023	Analysis of a dataset comprising 16,772 cases of DR and 16833 cases of DME unique patient visits	Specificity for DR: 94.24%; Sensitivity for DME: 90.91%; Sensitivity in patients aged over 65 years: 82.51%
Brown et al[45], 2018	Utilization of 100 test images in Retinopathy of Prematurity using Inception–V1 and U-Net CNN	Predicted sensitivity: 100%; Predicted specificity: 94%
Ting et al[9], 2019	Incorporation of ten external datasets from various countries (Japan, United States, Hong Kong, Mexico, and Australia) employing the Deep Learning Algorithm VGG-19	Sensitivity in referable DR: 90.5%; Specificity in referable DR: 91.6%
Morya et al[52], 2021; Morya et al[53], 2021	World's first Smartphone based AI Annotation tool for grading multiple retinal images in a shortest span – quantitative and qualitative analysis	DR; AMD; Glaucoma; Retinitis Pigmentosa; CSR etc.

AI: Artificial intelligence; AMD: Age-related macular degeneration; CSR: Central serous retinopathy; DR: Diabetic retinopathy; DME: Diabetic macular edema.