Everything real about unreal artificial intelligence in diabetic retinopathy and in ocular pathologies

Table 1 Comparative analysis of various studies done on artificial intelligence in diabetic retinopathy[19]

Ref.	Sensitivity, specificity or accuracy of the study	Total fundus images examined	Types of AI used	Main objective
Wong et al[20]	Area under the curve were 0.97 and 0.92 for microaneurysm and hemorrhages respectively	143 images	A three-layer feed forward neural network	Deals with detecting the microaneurysm and hemorrhages. Frangi filter used
Imani et al[57]	Sensitivity of 75.02%-75.24%; Specificity of 97.45%-97.53%	60 images	MCA	Detected the exudation and blood vessel
Yazid et al[58]	97.8% in sensitivity, 99% in specificity and 83.3% in predictivity for STARE database. 90.7% in sensitivity, 99.4% in specificity and 74% in predictivity for the custom database	30 images	Inverse surface thresholding	Detected both hard and soft exudates
Akyol et al[59]	Percentage accuracy of disc detection ranged from 90%-94.38% using different data set	239 images	Key point detection, texture analysis, and visual dictionary techniques	Detected the optic disc of fundus images
Niemeijer et al[13]	Accuracy in 99.9% cases in finding the disc	1000 images	Combined k-nearest neighbor and cues	Fast detection of the optic disc
Rajalakshmi et al[60], Smart phone based study	95.8% sensitivity and 80.2% specificity for detecting any DR. 99.1% sensitivity and 80.4% specificity in detecting STDR	Retinal images of 296 patients	Eye Art AI Dr screening software used	Retinal photography with Remidio ‘Fundus on Phone’
Eye Nuk study	Sensitivity was 91.7%; Specificity was 91.5%	40542 images	Eye PAC Stelescreening system	Retinal images taken with traditional desktop fundus cameras
Ting et al[61]	Sensitivity and specificity for RDR was 90.5% and 91.6%; For STDR the sensitivity was 100% and the specificity was 91.1%	494661 retinal images	Deep learning system	Multiple Retinal images taken with conventional fundus cameras
IRIS	Sensitivity of the IRIS algorithm in detecting STDR was 66.4% with false-negative rate of 2% and the specificity was 72.8%. Positive Predictive value of 10.8% and negative predictive value 97.8%	15015 patients	Intelligent Retinal Imaging System (IRIS)	Retinal screening examination and nonmydriatic fundus photography

This table has been reproduced from the article by Padhy et al[19]. Citation: Padhy SK, Takkar B, Chawla R, Kumar A. Artificial intelligence in diabetic retinopathy: A natural step to the future. Indian J Ophthalmol 2019; 67: 1004-1009. Copyright© The Authors 2019. Published by Indian Journal of Ophthalmology. The authors have obtained the permission for table using from the Indian Journal of Ophthalmology Publishing Group (Supplementary material). AI: Artificial intelligence; MCA: Morphological component analysis.