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.

Table 2 Various algorithms used in diabetic retinopathy as follows

Study conducted	Algorithms used	Identified and diagnosed
A retrospective study by Liu et al[46], 2022, using traditional fundus images	EfficientNet-B5	DME
A retrospective study by Dai et al[47], 2021	ResNet and Mask R-CNN	DR grading
A retrospective study by Lee et al[48], 2021	OpthAI, AirDoc, Eyenuk, Retina AI Health, Retmarker	Referable DR detection
A prospective study by Heydon et al[49], 2020	EyeArt v2.1	Referable DR detection
A prospective study by Gulshan et al[50], 2019	Inception-v3	Referable DR detection
Akram et al[51] proposed an automated module	MESSIDOR database used	Proposed an automated module for the grading of diabetic maculopathy

DME: Diabetic macular edema; DR: Diabetic retinopathy.

Table 3 Artificial intelligence use in non-ophthalmologic disease

Diseases	Artificial intelligence algorithm
Autism spectrum disorder[68]	DL model based on OCT images and automatic retinal image analysis
Chronic kidney disease[69]	Elevated urine albumin/creatinine ratio associated with reduction in retinal and choroid vasculature density in OCT or OCT angiography studies
Iron deficiency anemia[70]	Lower retinal vessel density and reduced vessel light reflectance observed in OCT images
Intracranial hypertension[71]	Brain and Optic nerve study (BONSAI) AI, U-Net and DenseNet networks used and Papilledema, optic atrophy and optic disc drusen observed, with 96.4% sensitivity and 84% specificity in detecting papilloedema and normal ONH
Alzheimer’s disease[62]	DL model using retinal images showed 83.6% accuracy, 93.2% sensitivity, 82.0% specificity, and an AUROC of 0.93 for detecting Alzheimer's disease-dementia

AI: Artificial intelligence; AUROC: Area under receiver operating characteristic curve; DL: Deep learning; OCT: Optical coherence tomography; ONH: Optic nerve head.