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

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.