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| Groups | Aim | Data sets | Deep learning techniques | Performance | Conclusions |
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| Gulshan et al. [6] (Google Inc.) | DR detection | Public:
EyePACS, Messidor 128175 fundus images | DCNN | AUC
0.991 for EyePACS
0.990 for Messidor | The DCNN had high sensitivity and specificity for detecting referable DR (moderate and worse DR, referable diabetic macular edema, or both) |
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| Gargeya and Leng [46] (Byers Eye Institute at Stanford) | DR detection | Public:
EyePACS, Messidor, E-ophtha 77348 fundus images | DCNN | AUC
0.94 for Messidor data set
0.95 for E-ophtha data set | The DCNN can be used to screen fundus images to identify DR with high reliability |
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| Quellec et al. [7] (Brest University) | DR detection heatmaps creation | Public:
Kaggle, DiaretDB, E-ophtha 196590 fundus images | CNN | AUC = 0.954 in Kaggle’s data set
AUC = 0.949 in E-ophtha data set | The proposed method is a promising image mining tool to discover new biomarkers in images. The model trained to detect referable DR can detect some lesions and outperforms recent algorithms trained to detect those lesions specifically |
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| Ardiyanto et al. [63] (Universitas Gadjah Mada) | DR grading | Public:
FINDeRS 315 fundus images | CNN | Detection
Accuracy: 95.71%
Sensitivity: 76.92%
Specificity: 100%
Grading
Accuracy: 60.28%
Sensitivity: 65.40%
Specificity: 73.37% | The network needs more data to train. And, this work opens up the future possibility to establish an integrated DR system to grade the severity at a low cost |
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| ElTanboly et al. [66] (Mansoura University) | DR detection | Local:
52 SD-OCT scans | DFCN | AUC: 0.98
Accuracy: 92%
Sensitivity: 83%
Specificity: 100% | The proposed CAD system for early DR detection using the OCT retinal images has good outcome and outperforms than other 4 conventional classifiers |
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| Prahs et al. [100] (Department of Ophthalmology, University of Regensburg) | Give an indication of the treatment of anti-VEGF injection | Local:
183,402 OCT B-scans | DCNN (GoogLeNet) | AUC: 0.968
Accuracy: 95.5%
Sensitivity: 90.1%
Specificity: 96.2% | The DCNN neural networks are effective in assessing OCT scans with regard to treatment indication with anti-VEGF medications |
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| Abràmoff et al. [62] (University of Iowa Hospitals and Clinics) | DR detection | Public:
Messidor 1748 fundus images | CNN | Referable DR:
AUC: 0.980
Sensitivity: 96.8%
Specificity: 87%
Vision threatening DR:
AUC: 0.989
Sensitivity: 100%
Specificity: 90.8% | The DL enhanced algorithms have the potential to improve the efficiency of DR screening |
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| Takahashi et al. [65] (Department of Ophthalmology, Jichi Medical University) | DR grading | Local:
9939 fundus images | DCNN (GoogLeNet) | Accuracy: 0.64∼0.82 | The proposed novel AI disease-staging system have the ability to grade DR involving retinal areas not typically visualized on fundoscopy |
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| Abbas et al. [64] (Surgery Department and Glaucoma Unity, University Hospital Puerta del Mar, Cádiz) | DR grading | Public:
Messidor, DiaretDB, FAZ
500 fundus images
Local: 250 fundus images | DNN | AUC: 0.924
Sensitivity: 92.18%
Specificity: 94.50% | The system is appropriate for early detection of DR and provides an effective treatment for prediction type of diabetes |
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| Chen et al. [73] (Institute for Infocomm Research, Agency for Science, Technology and Research; Singapore National Eye Centre) | Glaucoma detection | Public:
Origa, Sces 2326 fundus images | DCNN | AUC:
0.831 for Origa
0.887 for Sces | Present a DL framework for glaucoma detection based on DCNN |
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| Li et al. [89] (Institute for Infocomm Research, Agency for Science, Technology and Research) | Glaucoma detection | Public:
Origa 650 fundus images | DCNN (AlexNet, VGG-19, VGG-16, GoogLeNet) | Best AUC: 0.8384
AlexNet > VGG-19 ≈ VG-16 > GoogLeNet | The proposed method that integrates both local and holistic features of optic disc to detect glaucoma is reliable |
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| Asaoka et al. [91] (Department of Ophthalmology, The University of Tokyo) | Preperimetric OAG detection | Local:
279 VFs | DFNN | AUC: 92.6% | Using a deep FNN can distinguish preperimetric glaucoma VFs from healthy VFs with very high accuracy, which is better than the outcome obtained from ML techniques |
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| Muhammad et al. [92] (Department of Physiology, Weill Cornell Medicine) | Glaucoma detection | Local:
612 single wide-field OCT images | DCNN (AlexNet) | Accuracy: 65.7%∼92.4% | The proposed protocol outperforms standard OCT and VF in distinguishing healthy suspect eyes from eyes with early glaucoma |
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| Li et al. [90] (Zhongshan Ophthalmic Center, Sun Yat-sen University) | Glaucoma detection | Local:
8000 fundus images | DCNN (GoogleNet) | AUC: 0.986
Sensitivity: 95.6%
Specificity: 92% | DL can be applied to detect referable glaucomatous optic neuropathy with high sensitivity and specificity |
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| Burlina et al. [104] (Retina Division, Wilmer Eye Institute, Johns Hopkins University School of Medicine) | AMD Grading | Public:
AREDS 5664 fundus images | DCNN | Accuracy
79.4% (4-class)
81.5% (3-class)
93.4% (2-class) | Demonstrates comparable performance between computer and physician grading |
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| Burlina et al. [103] (Retina Division, Wilmer Eye Institute, Johns Hopkins University School of Medicine) | AMD detection | Public:
AREDS 130000 fundus images | DCNN (AlexNet) | AUC: 0.94∼0.96
Accuracy: 88.4%∼91.6% | Applying a DL-based automated assessment of AMD from fundus images can produce results that are similar to human performance levels |
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| Treder et al. [105] (Department of Ophthalmology, University of Münster Medical Center) | AMD detection | Local:
1112 SD-OCT images | DCNN | Sensitivity: 100%
Specificity: 92%
Accuracy: 96% | With the DL-based approach, it is possible to detect AMD in SD-OCT with good outcome. With more image data, the model can get more practical value in clinical decisions |
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| Gao et al. [111] (Microsoft Research Asia and Singapore Eye Research Institute) | Nuclear cataracts grading | Public:
ACHIKO-NC 5378 slit-lamp images | CNN and SVM | Accuracy: 70.7% | The proposed method is useful for assisting and improving diagnosis of the disease in the background of large-population screening and has the potential to be applied to other eye diseases |
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| Long et al. [114] (Zhongshan Ophthalmic Centre, Sun Yat-sen University) | Pediatric cataracts detection | Local:
CCPMOH 886 slit-lamp images | DCNN | Accuracy
98.87% (detection)
97.56% (treatment suggestion) | The AI agent using DL have the ability to accurately diagnose and provide treatment decisions for congenital cataracts. And the AI agent and individual ophthalmologists perform equally well. A cloud-based platform integrated with the AI agent for multihospital collaboration was built to improve disease management |
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| Choi et al. [120] (Department of Ophthalmology, Yonsei University College of Medicine) | Multiple retinal diseases detection | Public:
STARE 397 fundus images | DCNN (VGG-19) | Accuracy
30.5% (all categories were included)
36.7% (using ensemble classifiers)
72.8% (considering only normal, DR and AMD) | As the number of categories increased, the performance of the DL model has declined. Several ensemble classifiers enhanced the multicategorical classification performance. Large data sets should be applied to confirm the effectiveness of the proposed model |
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