Table 5.

Summary of DL methods using FP and OCT to detect eye disease

Authors	Year	Imaging Modalities	Aim	Data sets	DL techniques	Performance
Arcadu F et al. [111]	2019	FP	Diabetic macular thickening detection	Local:17,997 FPs	Inception-v3	AUC:0.97 (central subfield thickness ≥ 250 μm)0.91 (central foveal thickness ≥ 250 μm)0.94 (central subfield thickness ≥ 400 μm)0.96 (central foveal thickness ≥ 400 μm)
Nagasawa T et al. [112]	2019	FP	Treatment-naïve proliferative diabetic retinopathy detection	Local:132 FPs	VGG-16	Sensitivity: 94.7%Specificity: 97.2%AUC: 0.969
Phan S et al. [113]	2019	FP	Glaucoma detection	Local:3312 FPs	VGG-19ResNet-152DenseNet-201	AUCs of 0.9 or more (3 DCNNs)
Nagasato D et al. [114]	2019	FP	Branch retinal vein occlusion detection	Local:466 FPs	VGG-16SVM	Sensitivity: 94.0%Specificity: 97.0%positive predictive value (PPV): 96.5%negative predictive value (NPV): 93.2%AUC: 97.6%
Burlina PM et al. [115]	2019	FP	To develop DL techniques for synthesizing high-resolution realistic fundus images	Local:133,821 FPs	GAN	AUC:0.9706 (model trained on real data) 0.9235 (model trained on synthetic data)
Girard F et al. [116]	2019	FP	Joint segmentation and classification of retinal arteries and veins	Public:DRIVE, 40 FPsMESSIDOR, 1200 FPs	CNN	Accuracy: 94.8% Sensitivity: 93.7% Specificity: 92.9%
Coyner AS et al. [117]	2018	FP	Image quality assessment of fundus images in ROP	Local: 6043 FPs	VGG-19 DCNN	Accuracy: 89.1% AUC: 0.964
Keel S et al. [118]	2018	FP	Detection of referable diabetic retinopathy and glaucoma	Public:LabelMe, 114,906 FPs (referable DR)		Sensitivity:90% (glaucomatous optic neuropathy) 96% (referable DR)
Sayres R et al. [119]	2018	FP	Assist grading for DR	Public: EyePACS, 1796 FPs	Inception v-4	Sensitivity:79.4% (unassisted) 87.5% (grades only) 88.7% (grades plus heatmap)
Peng Y et al. [120]	2018	FP	Automated classification of AMD severity	Public: AREDS, 59302 FPs	DeepSeeNet (Inception v-3)	Accuracy: 0.671 AUC: 0.94 (large drusen) 0.93 (pigmentary abnormalities) 0.97 (late AMD)
Guo Y et al. [121]	2018	FP	Retinal vessel detection	Public: DRIVE, 20 FPs STARE, 20 FPs	Multiple DCNNs	Accuracy: 95.97% (DRIVE training dataset) 96.13% (DRIVE testing dataset) 95.39% (STARE dataset) AUC: 0,9726 (DRIVE training dataset) 0.9737 (DRIVE testing dataset) 0.9539 (STARE dataset)
Khojasteh P et al. [122]	2018	FP	Detection of exudates, microaneurysms and hemorrhages	Public: DIARETDB1, 75 FPs e-Ophtha, 209 FPs	CNN	Accuracy: 97.3% (DIARETDB1 dataset) 86.6% (e-Ophtha) Sensitivity: 0.96 (exudates) 0.84 (hemorrhages) 0.85 (microaneurysms)
Gargeya R et al. [123]	2017	FP	Automated identification of DR	Public: EyePACS, 75,137 FPs MESSIDOR 2, 1748 E-Ophtha, 463 FPs	DCNN	Sensitivity: 94% Specificity: 98% AUC: 0.97
Burlina PM et al. [63]	2017	FP	Automated grading of AMD	Public: AREDS, more than 130,000 FPs	DCNN	Accuracy: 88.4% (SD, 0.5%)-91.6% (SD, 0.1%) AUC: 0.94 (SD, 0.5%)-0.96 (SD, 0.1%)
Ordóñez PF et al. [124]	2017	FP	To improve the accuracy of microaneurysms detection	Public: Kaggle, 88,702 FPs Messidor, 1200 FPs DiaRerDB1, 89 FPs	Standard CNNVGG CNN	Sensitivity > 91% Specificity > 93% AUC > 93%
Takahashi H et al. [58]	2017	FP	Improving staging of DR	Local: 9939 FPs	GoogleNet DCNN	Prevalence and bias-adjusted Fleiss’kappa (PABAK): 0.64 (modified Davis grading) 0.37 (real prognosis grading)
Abbas Q et al. [125]	2017	FP	Automatic recognition of severity level of DR	Local: 750 FPs	DCNN	Sensitivity: 92.18% Specificity: 94.50% AUC: 0.924
Pfister M et al. [126]	2019	OCT	Automated segmentation of dermal fillers in OCT images	Local: 100 OCT volume data sets	CNN (U-net-like architecture)	Accuracy: 0.9938
Fu H et al. [127]	2019	OCT	Automated angle-closure detection	Local: 4135 anterior segment OCT images	CNN	Sensitivity: 0.79 ± 0.037 Specificity: 0.87 ± 0.009 AUC: 0.90
Masood S et al. [128]	2019	OCT	Automatic choroid layer segmentation from OCT images	Local: 525 OCT images	CNN (Cifar-10 model)	Accuracy: 97%
Dos Santos VA et al. [129]	2019	OCT	Segmentation of cornea OCT scans	Local: 20,160 OCT images	CNN	Accuracy: 99.56%
Asaoka R et al. [130]	2019	OCT	Diagnosis early-onset glaucoma from OCT images	Local: 4316 OCT images	CNN	AUC: 93.7%
Lu W et al. [131]	2018	OCT	Classification of multi-categorical abnormalities from OCT images	Local: 60,407 OCT images	ResNet	Accuracy: 0.959 AUC: 0.984
Schlegl T et al. [132]	2018	OCT	Detection of macular fluid in OCT images	Local: 1200 OCT scans	CNN	Intraretinal cystoid fluid detection: Accuracy: 0.91 AUC: 0.94 Subretinal fluid detection: Accuracy: 0.61 AUC: 0.92
Prahs P et al. [133]	2018	OCT	Evaluation of treatment indication with anti-vascular endothelial growth factor medications	Local: 183,402 OCT scans	GoogleNet inception DCNN	Accuracy: 95.5% Sensitivity: 90.1% Specificity: 96.2% AUC: 0.968
Shah A et al. [134]	2018	OCT	Retinal layer segmentation in OCT images	Local: 3000 OCT scans	CNN	Average computation time: 12.3 s
Chan GCY et al. [135]	2018	OCT	Automated diabetic macular edema classification	Public: Singapore Eye Research Institute, 14,720 OCT scans	AlexNet, VGG, GoogleNet	Accuracy: 93.75%
Muhammad H et al. [136]	2017	OCT	Classification of glaucoma suspects	Local:102 OCT scans	CNN, Random forest	Accuracy: 93.1% (retinal nerve fiber layer)
Lee CS et al. [81]	2017	OCT	Segmentation of macular edema in OCT	Local:1289 OCT images	U-Net CNN	cross-validated Dice coefficient: 0.911
Lee CS et al. [137]	2017	OCT	Classification of normal and AMD OCT images	Public:Electronic medical records, 101,002 OCT images	VGG-16	Accuracy: 87.63% AUC: 92.78%

DL = deep learning; FP = fundus photography; OCT = optical coherence tomography; CNN = convolution neural network; DCNN = deep convolution neural network; DR = diabetic retinopathy; AMD = age-related macular degeneration; AUC = area under the curve