Artificial intelligence in ophthalmology and visual sciences: Current implications and future directions

Table 1 Summary of artificial intelligence applications in detection of suspected diabetic retinopathy, age related macular degeneration, and glaucoma

Ref.	Imaging modality	AI algorithm	Dataset for training	Dataset for validation	AUC	Sensitivity (%)	Specificity (%)
Diabetic retinopathy
Abràmoff et al[9], 2016	CFP	AlexNet and VGGNet	10000 to 1250000 images	Messidor-2: 1748	0.980	96.8	87
Gulshan et al[29], 2016	CFP	Inception-V3	128175 images	EyePACS-1: 8788 Messidor-2: 1745	0.9910.990	97.596.1	93.493.9
Ting et al[6], 2017	CFP	VGG -19	76370 images	SiDRP: 71896 images	0.936	90.5	91.6
				Guangdong: 15798	0.949	98.7	81.6
				SIMES: 3052	0.889	97.1	82
				SINDI: 4512	0.917	99.3	73.3
				SCES: 1936	0.919	100	76.3
				BES: 1052	0.929	94.4	88.5
				AFEDS: 1968	0.98	98.8	86.5
				RVEEH: 2302	0.983	98.9	92.2
				MEXICAN: 1172	0.95	91.8	84.8
				CUHK: 1254	0.948	99.3	83.1
				HKU: 7706	0.964	100	81.3
Abràmoff et al[30], 2018	CFP	AlexNet and VGGNet	10000 to 1250000 images	819 patients	N/A	87.2	90.7
Li et al[10], 2018	CFP	Inception V3	58790 images	8000 images for referable DR	0.989	97	91.4
Ruamviboonsuk et al[31], 2019	CFP	Inception V4	1665151 images	25326 images	0.987	96.8	95.6
Son et al[11], 2020	CFP	Custom CNN	95350 images	Two data sets: IDRiD: 144 images &	0.957 to 0.980	88.9-92.6	94.0- 100
				e-ophtha: 434 images	0.947 to 0.965	89.2-93.6	91.4 - 97.1
Age related macular degeneration
Ting et al[6], 2017	CFP	VGG-19	72610 images	35948 images	0.932	93.20	88.70
Lee et al[13], 2017	OCT scans - Spectralis	Modified VGG 16	80839 images	20163 images	0.974	92.64	93.69
Zapata et al[14], 2020	CFP	CNN 1 image type selection	53396	20% of training datasets	0.979	97.7	92.4
		CNN 1 CFP quality selection	150075		0.989	98.3	96.6
		CNN 1 OD/OS	30119		0.947	96.9	81.8
		AMDNET	8832		0.936	90.2	82.5
		Modified RESNET 50 (23) Referable GON	3776		0.863	76.8	83.8
Glaucoma suspect
Ting et al[6], 2017	CFP	VGG-19	125189 images	71896 images	0.942	96.40	93.20
Li et al[18], 2018	CFP		31745 images	8000 images	0.986	95.6	92

CFP: Color fundus photographs; AUC: Area under curve; GON: Glaucomatous optic neuropathy; OCT: Optical coherence tomography; DR: Diabetic retinopathy; SiDRP: Singapore integrated diabetic retinopathy screening program; CNN: Convolutional neural network; VGG-19: Visual geometry group; CUHK: Chinese University Hong Kong; HKU: Hong Kong University; N/A: Not available; RVEEH: Royal Victorian Eye and Ear Hospital; SCES: Singapore Chinese Eye Study; SIMES: Singapore Malay Eye Study; SINDI: Singapore Indian Eye Study; BES: Beijing Eye Study; IDRiD: Indian Diabetic Retinopathy Image Dataset; AFEDS: African American Eye Disease Study.