Ting et al.1212 Ting DSW, Cheung CY, Lim G, et al. Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes. JAMA. 2017;318(22):2211-23. PMID: 29234807; https://doi.org/10.1001/jama.2017.18152. https://doi.org/10.1001/jama.2017.18152...
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Diabetic retinopathy/76,370 images of retinal photographs |
Sensitivity of 90.5% and specificity of 91.6% |
Tufail et al.1010 Tufail A, Rudisill C, Egan C, et al. Automated Diabetic Retinopathy Image Assessment Software: Diagnostic Accuracy and Cost-Effectiveness Compared with Human Graders. Ophthalmology. 2017;124(3):343-351. PMID: 28024825; https://doi.org/10.1016/j.ophtha.2016.11.014. https://doi.org/10.1016/j.ophtha.2016.11...
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Diabetic retinopathy/20,258 images of retinal photographs |
EyeArt (sensitivity of 93.8%) and Retmaker (sensitivity of 97.9%) |
Abràmoff et al.1414 Abràmoff MD, Folk JC, Han DP, et al. Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmol. 2013;131(3):351-7. PMID: 23494039; https://doi.org/10.1001/jamaophthalmol.2013.1743. https://doi.org/10.1001/jamaophthalmol.2...
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Diabetic retinopathy/1,748 images of retinal photographs |
Sensitivity of 96.8% and specificity of 59.4% |
Gulshan et al.1515 Gulshan V, Peng L, Coram M, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016;316(22):2402-10. PMID: 27898976; https://doi.org/10.1001/jama.2016.17216. https://doi.org/10.1001/jama.2016.17216...
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Diabetic retinopathy/9,963 images of retinal photographs |
Sensitivity of 98.1% and specificity of 90.3% |
Gulshan et al.1616 Gulshan V, Rajan RP, Widner K, et al. Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in India. JAMA Ophthalmol. 2019;137(9):987–993. PMID: 31194246; https://doi.org/10.1001/jamaophthalmol.2019.2004. https://doi.org/10.1001/jamaophthalmol.2...
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Diabetic retinopathy/103,634 images of retinal photographs |
Sensitivity of 87.2% and specificity of 90.8% |
Ting et al.1212 Ting DSW, Cheung CY, Lim G, et al. Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes. JAMA. 2017;318(22):2211-23. PMID: 29234807; https://doi.org/10.1001/jama.2017.18152. https://doi.org/10.1001/jama.2017.18152...
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AMD/72,610 images of retinal photographs |
Sensitivity of 93.2% and specificity of 88.2% |
Burlina et al.2121 Burlina PM, Joshi N, Pekala M, et al. Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks. JAMA Ophthalmol. 2017;135(11):1170-6. PMID: 28973096; https://doi.org/10.1001/jamaophthalmol.2017.3782. https://doi.org/10.1001/jamaophthalmol.2...
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AMD/130,000 images of retinal photographs |
91.6% accuracy |
Grassmann et al.2222 Grassmann F, Mengelkamp J, Brandl C, et al. A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography. Ophthalmology. 2018;125(9):1410-20. PMID: 29653860; https://doi.org/10.1016/j.ophtha.2018.02.037. https://doi.org/10.1016/j.ophtha.2018.02...
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AMD/120,656 images of retinal photographs |
84.2% accuracy |
Venhuizen et al.2828 Venhuizen FG, van Ginneken B, van Asten F, et al. Automated Staging of Age-Related Macular Degeneration Using Optical Coherence Tomography. Invest Ophthalmol Vis Sci. 2017;58(4):2318-28. PMID: 28437528; https://doi.org/10.1167/iovs.16-20541. https://doi.org/10.1167/iovs.16-20541...
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AMD/3,265 images of OCT |
Sensitivity of 98.2% and specificity of 91.2% |
Peng et al.2323 Peng Y, Dharssi S, Chen Q, et al. DeepSeeNet: A Deep Learning Model for Automated Classification of Patient-based Age-related Macular Degeneration Severity from Color Fundus Photographs. Ophthalmology. 2019;126(4):565-75. PMID: 30471319; https://doi.org/10.1016/j.ophtha.2018.11.015. https://doi.org/10.1016/j.ophtha.2018.11...
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AMD/58,402 images of retinal photographs |
Accuracy of 97.0% |
Lee et al.2929 Lee CS, Baughman DM, Lee AY. Deep learning is effective for the classification of OCT images of normal versus age-related Macular Degeneration. Ophthalmol Retina. 2017;1(4):322-7. PMID: 30693348; https://doi.org/10.1016/j.oret.2016.12.009. https://doi.org/10.1016/j.oret.2016.12.0...
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AMD/48,312 images of OCT |
Sensitivity of 84.6% and specificity of 91.5% |
Ting et al.1212 Ting DSW, Cheung CY, Lim G, et al. Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes. JAMA. 2017;318(22):2211-23. PMID: 29234807; https://doi.org/10.1001/jama.2017.18152. https://doi.org/10.1001/jama.2017.18152...
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Glaucoma/125,189 images of retinal photographs |
96.4% sensitivity and 87.2% specificity |
Li et al.3030 Li Z, He Y, Keel S, et al. Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs. Ophthalmology. 2018;125(8):1199-1206. PMID: 29506863; https://doi.org/10.1016/j.ophtha.2018.01.023. https://doi.org/10.1016/j.ophtha.2018.01...
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Glaucoma/48,116 images of retinal photographs |
Sensitivity of 95.6% and specificity of 92% |
Kim et al.3232 Kim SJ, Cho KJ, Oh S. Development of machine learning models for diagnosis of glaucoma. PLoS One. 2017;12(5):e0177726. PMID: 28542342; https://doi.org/10.1371/journal.pone.0177726. https://doi.org/10.1371/journal.pone.017...
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Glaucoma/399 images of visual field |
Sensitivity of 98.3% and specificity of 97.5% |
Ahn et al.3333 Ahn JM, Kim S, Ahn KS, et al. A deep learning model for the detection of both advanced and early glaucoma using fundus photography. PLoS One. 2018;13(11):e0207982. PMID: 30481205; https://doi.org/10.1371/journal.pone.0207982. https://doi.org/10.1371/journal.pone.020...
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Glaucoma/1,542 images of retinal photographs |
92.2% accuracy |
Asaoka et al.3434 Asaoka R, Murata H, Iwase A, Araie M. Detecting Preperimetric Glaucoma with Standard Automated Perimetry Using a Deep Learning Classifier. Ophthalmology. 2016;123(9):1974-80. PMID: 27395766; https://doi.org/10.1016/j.ophtha.2016.05.029. https://doi.org/10.1016/j.ophtha.2016.05...
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Glaucoma/171 images of visual field |
92.6% accuracy |
Masumoto et al.3636 Masumoto H, Tabuchi H, Nakakura S, et al. Deep-learning Classifier With an Ultrawide-field Scanning Laser Ophthalmoscope Detects Glaucoma Visual Field Severity. J Glaucoma. 2018;27(7):647-52. PMID: 29781835; https://doi.org/10.1097/IJG.0000000000000988. https://doi.org/10.1097/IJG.000000000000...
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Glaucoma/982 images of visual field |
Sensitivity of 81.3% and specificity of 80.2% |
Brown et al.4646 Brown JM, Campbell JP, Beers A, et al. Imaging and Informatics in Retinopathy of Prematurity (i-ROP) Research Consortium. Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks. JAMA Ophthalmol. 2018;136(7):803-10. PMID: 29801159; https://doi.org/10.1001/jamaophthalmol.2018.1934. https://doi.org/10.1001/jamaophthalmol.2...
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ROP/5,511 images of retinal photographs |
93% sensitivity and 94% specificity |
Ataer-Cansizoglu et al.4949 Ataer-Cansizoglu E, Bolon-Canedo V, Campbell JP, et al. i-ROP Research Consortium. Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity: Performance of the “i-ROP” System and Image Features Associated With Expert Diagnosis. Transl Vis Sci Technol. 2015;4(6):5. PMID: 26644965; https://doi.org/10.1167/tvst.4.6.5. https://doi.org/10.1167/tvst.4.6.5...
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ROP/77 images of retinal photographs |
95% accuracy |
Campbell et al.5151 Campbell JP, Ataer-Cansizoglu E, Bolon-Canedo V, et al. Imaging and Informatics in ROP (i-ROP) Research Consortium. Expert Diagnosis of Plus Disease in Retinopathy of Prematurity From Computer-Based Image Analysis. JAMA Ophthalmol. 2016;134(6):651-7. PMID: 27077667; https://doi.org/10.1001/jamaophthalmol.2016.0611. https://doi.org/10.1001/jamaophthalmol.2...
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ROP/77 images of retinal photographs |
95% accuracy |