The value of deep learning in the diagnosis of diabetic retinopathy based on color fundus photographs: a meta-analysis_Chinese Journal of Evidence-Based Medicine

Authors：

HE Hongyue ¹ , LIU Jingxian ¹ , CHEN Jiao ¹ , LIU Lixin ¹ , YOU Wenli ¹ , LUO Wen ¹ ,  WANG Fang ²

1. Chengdu University of Traditional Chinese Medicine, Chengdu 610000, P. R. China;
2. Guang'an Hospital of Chinese Medicine, Guang'an 638400, P. R. China;

Corresponding author：

WANG Fang, Email: 1697070757@qq.com

Keywords：

Deep learning; Diabetic retinopathy; Diagnostic test accuracy study; Meta-analysis

DOI：

10.7507/1672-2531.202504076

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Objective To systematically review the value of deep learning (DL) on the diagnosis of diabetic retinopathy (DR) based on color fundus photographs. Methods The PubMed, Embase, Web of Science, Cochrane Library, IEEE, CNKI, VIP, WanFang Data databases were systematically searched to collect the studies on the use of DL in the diagnosis of DR from January 2019 to November 2024. Two reviewers independently screened literature, extracted data and assessed the risk of bias of the included studies. Meta-analysis was then performed by using RevMan 5.4.1, Meta-Disc 1.4 and Stata 16.0 software. Results A total of 16 studies were included, involving 215 560 images. Meta-analysis results showed that the combined sensitivity of DL in diagnosing DR was 0.97 (95%CI 0.94 to 0.98), the specificity was 0.97 (95%CI 0.94 to 0.98), the AUC was 0.99 (95%CI 0.94 to 0.98), and the DOR was 852 (95%CI 403 to 1 803). Conclusion DL has a high diagnostic value for DR. However, there is a high degree of heterogeneity among different studies. In the future, more large-sample, high-quality studies can be included to confirm its clinical applicability.

Citation： HE Hongyue, LIU Jingxian, CHEN Jiao, LIU Lixin, YOU Wenli, LUO Wen, WANG Fang. The value of deep learning in the diagnosis of diabetic retinopathy based on color fundus photographs: a meta-analysis. Chinese Journal of Evidence-Based Medicine, 2026, 26(2): 153-157. doi: 10.7507/1672-2531.202504076 Copy

1.	American Diabetes Association Professional Practice Committee. 2. Diagnosis and classification of diabetes: standards of care in diabetes-2025. Diabetes Care, 2025, 48(1 Suppl 1): S27-S49.
2.	Tan TE, Wong TY. Diabetic retinopathy: looking forward to 2030. Front Endocrinol (Lausanne), 2023, 13: 1077669.
3.	Cheung N, Mitchell P, Wong TY. Diabetic retinopathy. Lancet, 2010, 376(9735): 124-136.
4.	Diabetic retinopathy. Nature Reviews Disease Primers, 2016, 2(1): 16013.
5.	邓宇轩, 叶雯青, 孙艳婷, 等. 中国糖尿病视网膜病变患病率的荟萃分析. 中华医学杂志, 2020, 100(48): 3846-3852.
6.	张佳慧, 陈晓隆. 糖尿病视网膜病变的诊断和治疗: 2016-2018年最新研究进展. 眼科新进展, 2018, 38(12): 1185-1190.
7.	Flaxel CJ, Adelman RA, Bailey ST, et al. Diabetic retinopathy preferred practice Pattern®. Ophthalmology, 2020, 127(1): P66-P145.
8.	Cole JB, Florez JC. Genetics of diabetes mellitus and diabetes complications. Nat Rev Nephrol, 2020, 16(7): 377-390.
9.	Dinpajhouh M, Seyyedsalehi SA. Automated detecting and severity grading of diabetic retinopathy using transfer learning and attention mechanism. Neural Comput Appl, 2023, 35(33): 23959-23971.
10.	孙石磊, 李明, 刘静, 等. 深度学习在糖尿病视网膜病变分类领域的研究进展. 计算机工程与应用, 2024, 60(8): 16-30.
11.	Tsiknakis N, Theodoropoulos D, Manikis G, et al. Deep learning for diabetic retinopathy detection and classification based on fundus images: a review. Comput Biol Med, 2021, 135: 104599.
12.	Grauslund J. Diabetic retinopathy screening in the emerging era of artificial intelligence. Diabetologia, 2022, 65(9): 1415-1423.
13.	LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-444.
14.	Jang HJ, Cho KO. Applications of deep learning for the analysis of medical data. Arch Pharm Res, 2019, 42(6): 492-504.
15.	康林, 刘喜, 程浩, 等. 深度学习在内镜中识别萎缩性胃炎和肠上皮化生的诊断效能: 系统综述和Meta分析. 海南医学院学报, 2024, 30(17): 1335-1345.
16.	张倩, 曹云太, 王志洁, 等. 深度学习在早期胃癌内镜图像诊断中的研究进展. 实用医学杂志, 2025, 41(14): 2160-2166.
17.	罗文斌, 郑晔, 刘欣, 等. 基于DCE-MRI图像深度学习模型鉴别诊断乳腺良恶性肿瘤的价值分析. 磁共振成像, 2024, 15(10): 22-29.
18.	宫阿娟, 潘天荣. 多病种眼底疾病诊断的深度学习策略讨论. 计算机工程, 2024, 50(5): 363-372.
19.	Dong L, He W, Zhang R, et al. Artificial intelligence for screening of multiple retinal and optic nerve diseases. JAMA Netw Open, 2022, 5(5): e229960.
20.	McInnes MDF, Moher D, Thombs BD, et al. Preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies: the PRISMA-DTA statement. JAMA, 2018, 319(4): 388-396.
21.	Whiting PF, Rutjes AW, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med, 2011, 155(8): 529-536.
22.	Chetoui M, Akhloufi MA. Explainable end-to-end deep learning for diabetic retinopathy detection across multiple datasets. J Med Imaging (Bellingham), 2020, 7(4): 044503.
23.	Bodapati JD, Shaik NS, Naralasetti V. Composite deep neural network with gated-attention mechanism for diabetic retinopathy severity classification. J Ambient Intell Humaniz Comput, 2021, 12(10): 9825-9839.
24.	Mohammedhasan M, Uğuz H. A new early stage diabetic retinopathy diagnosis model using deep convolutional neural networks and principal component analysis. Traitement du Signal, 2020, 37(5): 711-722.
25.	Sanjana S, Shadin NS, Farzana M. Automated diabetic retinopathy detection using transfer learning models. 2021 5th International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), 2021.
26.	Shakibania H, Raoufi S, Pourafkham B, et al. Dual branch deep learning network for detection and stage grading of diabetic retinopathy. Biomed Signal Process Control, 2024, 93: 106061.
27.	Sridhar S, Sanagavarapu S. Detection and prognosis evaluation of diabetic retinopathy using ensemble deep convolutional neural networks. Proceedings of the 2020 International Electronics Symposium (IES), 2020.
28.	Vives-Boix V, Ruiz-Fernández D. Diabetic retinopathy detection through convolutional neural networks with synaptic metaplasticity. Comput Methods Programs Biomed, 2021, 206: 106094.
29.	Zhang W, Zhong J, Yang S, et al. Automated identification and grading system of diabetic retinopathy using deep neural networks. Knowl Bas Sys, 2019, 175: 12-25.
30.	Kumar G, Chatterjee S, Chattopadhyay C. DRISTI: a hybrid deep neural network for diabetic retinopathy diagnosis. Signal Image Video Process, 2021, 15(8): 1679-1686.
31.	Nur-A-Alam M, Nasir MMK, Ahsan M, et al. A faster Rcnn-based diabetic retinopathy detection method using fused features from retina images. IEEE Access, 2023, 11: 124331-124349.
32.	Hu J, Wang H, Wang L, et al. Graph adversarial transfer learning for diabetic retinopathy classification. IEEE Access, 2022, 10: 119071-119083.
33.	Zhu D, Ge A, Chen X, et al. Supervised contrastive learning with angular margin for the detection and grading of diabetic retinopathy. Diagnostics (Basel), 2023, 13(14): 2389.
34.	朱小红, 张云, 刘美玲, 等. GhostNet轻量级网络在糖尿病视网膜病变诊断中的应用价值. 首都医科大学学报, 2024, 45(4): 678-685.
35.	张子振, 刘明, 朱德江. 融合注意力机制和高效网络的糖尿病视网膜病变识别与分类. 中国图象图形学报, 2020, 25(8): 1708-1718.
36.	高韶晖, 金学民, 赵朝霞, 等. 糖尿病视网膜病变人工智能机器人辅助诊断系统的建立及应用. 中华实验眼科杂志, 2019, 37(8): 669-673.
37.	Teo ZL, Tham YC, Yu M, et al. Global prevalence of diabetic retinopathy and projection of burden through 2045: systematic review and meta-analysis. Ophthalmology, 2021, 128(11): 1580-1591.
38.	Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol, 2018, 14(2): 88-98.
39.	倪家远. 基于决策树的糖尿病诊断专家系统的研究与设计. 合肥: 安徽工业大学, 2016.
40.	Shan Y, Xu Y, Lin X, et al. Burden of vision loss due to diabetic retinopathy in China from 1990 to 2017: findings from the global burden of disease study. Acta Ophthalmol, 2021, 99(2): e267-e273.
41.	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): 2402-2410.
42.	Raumviboonsuk P, Krause J, Chotcomwongse P, et al. Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program. NPJ Digit Med, 2019, 2: 25.
43.	陈明惠, 王子昂, 李家昱, 等. 基于人工智能辅助糖尿病视网膜病变分类研究. 激光与光电子学进展, 2025, (12): 1-21.
44.	杨雨帆, 袁立明, 王珂, 等. 基于图卷积网络的糖尿病视网膜病变分级模型. 计算机科学, 2024, 51(S2): 461-465.
45.	郭莹, 李绍杰. 基于小尺度跨层融合模型的糖尿病视网膜病变分类方法. 生物医学工程学杂志, 2024, 41(5): 861-868.
46.	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): 2211-2223.
47.	Dai L, Wu L, Li H, et al. A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nat Commun, 2021, 12(1): 3242.
48.	Arcadu F, Benmansour F, Maunz A, et al. Deep learning algorithm predicts diabetic retinopathy progression in individual patients. NPJ Digit Med, 2019, 2: 92.
49.	Dai L, Sheng B, Chen T, et al. A deep learning system for predicting time to progression of diabetic retinopathy. Nat Med, 2024, 30(2): 584-594.

1. American Diabetes Association Professional Practice Committee. 2. Diagnosis and classification of diabetes: standards of care in diabetes-2025. Diabetes Care, 2025, 48(1 Suppl 1): S27-S49.
2. Tan TE, Wong TY. Diabetic retinopathy: looking forward to 2030. Front Endocrinol (Lausanne), 2023, 13: 1077669.
3. Cheung N, Mitchell P, Wong TY. Diabetic retinopathy. Lancet, 2010, 376(9735): 124-136.
4. Diabetic retinopathy. Nature Reviews Disease Primers, 2016, 2(1): 16013.
5. 邓宇轩, 叶雯青, 孙艳婷, 等. 中国糖尿病视网膜病变患病率的荟萃分析. 中华医学杂志, 2020, 100(48): 3846-3852.
6. 张佳慧, 陈晓隆. 糖尿病视网膜病变的诊断和治疗: 2016-2018年最新研究进展. 眼科新进展, 2018, 38(12): 1185-1190.
7. Flaxel CJ, Adelman RA, Bailey ST, et al. Diabetic retinopathy preferred practice Pattern®. Ophthalmology, 2020, 127(1): P66-P145.
8. Cole JB, Florez JC. Genetics of diabetes mellitus and diabetes complications. Nat Rev Nephrol, 2020, 16(7): 377-390.
9. Dinpajhouh M, Seyyedsalehi SA. Automated detecting and severity grading of diabetic retinopathy using transfer learning and attention mechanism. Neural Comput Appl, 2023, 35(33): 23959-23971.
10. 孙石磊, 李明, 刘静, 等. 深度学习在糖尿病视网膜病变分类领域的研究进展. 计算机工程与应用, 2024, 60(8): 16-30.
11. Tsiknakis N, Theodoropoulos D, Manikis G, et al. Deep learning for diabetic retinopathy detection and classification based on fundus images: a review. Comput Biol Med, 2021, 135: 104599.
12. Grauslund J. Diabetic retinopathy screening in the emerging era of artificial intelligence. Diabetologia, 2022, 65(9): 1415-1423.
13. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-444.
14. Jang HJ, Cho KO. Applications of deep learning for the analysis of medical data. Arch Pharm Res, 2019, 42(6): 492-504.
15. 康林, 刘喜, 程浩, 等. 深度学习在内镜中识别萎缩性胃炎和肠上皮化生的诊断效能: 系统综述和Meta分析. 海南医学院学报, 2024, 30(17): 1335-1345.
16. 张倩, 曹云太, 王志洁, 等. 深度学习在早期胃癌内镜图像诊断中的研究进展. 实用医学杂志, 2025, 41(14): 2160-2166.
17. 罗文斌, 郑晔, 刘欣, 等. 基于DCE-MRI图像深度学习模型鉴别诊断乳腺良恶性肿瘤的价值分析. 磁共振成像, 2024, 15(10): 22-29.
18. 宫阿娟, 潘天荣. 多病种眼底疾病诊断的深度学习策略讨论. 计算机工程, 2024, 50(5): 363-372.
19. Dong L, He W, Zhang R, et al. Artificial intelligence for screening of multiple retinal and optic nerve diseases. JAMA Netw Open, 2022, 5(5): e229960.
20. McInnes MDF, Moher D, Thombs BD, et al. Preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies: the PRISMA-DTA statement. JAMA, 2018, 319(4): 388-396.
21. Whiting PF, Rutjes AW, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med, 2011, 155(8): 529-536.
22. Chetoui M, Akhloufi MA. Explainable end-to-end deep learning for diabetic retinopathy detection across multiple datasets. J Med Imaging (Bellingham), 2020, 7(4): 044503.
23. Bodapati JD, Shaik NS, Naralasetti V. Composite deep neural network with gated-attention mechanism for diabetic retinopathy severity classification. J Ambient Intell Humaniz Comput, 2021, 12(10): 9825-9839.
24. Mohammedhasan M, Uğuz H. A new early stage diabetic retinopathy diagnosis model using deep convolutional neural networks and principal component analysis. Traitement du Signal, 2020, 37(5): 711-722.
25. Sanjana S, Shadin NS, Farzana M. Automated diabetic retinopathy detection using transfer learning models. 2021 5th International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), 2021.
26. Shakibania H, Raoufi S, Pourafkham B, et al. Dual branch deep learning network for detection and stage grading of diabetic retinopathy. Biomed Signal Process Control, 2024, 93: 106061.
27. Sridhar S, Sanagavarapu S. Detection and prognosis evaluation of diabetic retinopathy using ensemble deep convolutional neural networks. Proceedings of the 2020 International Electronics Symposium (IES), 2020.
28. Vives-Boix V, Ruiz-Fernández D. Diabetic retinopathy detection through convolutional neural networks with synaptic metaplasticity. Comput Methods Programs Biomed, 2021, 206: 106094.
29. Zhang W, Zhong J, Yang S, et al. Automated identification and grading system of diabetic retinopathy using deep neural networks. Knowl Bas Sys, 2019, 175: 12-25.
30. Kumar G, Chatterjee S, Chattopadhyay C. DRISTI: a hybrid deep neural network for diabetic retinopathy diagnosis. Signal Image Video Process, 2021, 15(8): 1679-1686.
31. Nur-A-Alam M, Nasir MMK, Ahsan M, et al. A faster Rcnn-based diabetic retinopathy detection method using fused features from retina images. IEEE Access, 2023, 11: 124331-124349.
32. Hu J, Wang H, Wang L, et al. Graph adversarial transfer learning for diabetic retinopathy classification. IEEE Access, 2022, 10: 119071-119083.
33. Zhu D, Ge A, Chen X, et al. Supervised contrastive learning with angular margin for the detection and grading of diabetic retinopathy. Diagnostics (Basel), 2023, 13(14): 2389.
34. 朱小红, 张云, 刘美玲, 等. GhostNet轻量级网络在糖尿病视网膜病变诊断中的应用价值. 首都医科大学学报, 2024, 45(4): 678-685.
35. 张子振, 刘明, 朱德江. 融合注意力机制和高效网络的糖尿病视网膜病变识别与分类. 中国图象图形学报, 2020, 25(8): 1708-1718.
36. 高韶晖, 金学民, 赵朝霞, 等. 糖尿病视网膜病变人工智能机器人辅助诊断系统的建立及应用. 中华实验眼科杂志, 2019, 37(8): 669-673.
37. Teo ZL, Tham YC, Yu M, et al. Global prevalence of diabetic retinopathy and projection of burden through 2045: systematic review and meta-analysis. Ophthalmology, 2021, 128(11): 1580-1591.
38. Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol, 2018, 14(2): 88-98.
39. 倪家远. 基于决策树的糖尿病诊断专家系统的研究与设计. 合肥: 安徽工业大学, 2016.
40. Shan Y, Xu Y, Lin X, et al. Burden of vision loss due to diabetic retinopathy in China from 1990 to 2017: findings from the global burden of disease study. Acta Ophthalmol, 2021, 99(2): e267-e273.
41. 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): 2402-2410.
42. Raumviboonsuk P, Krause J, Chotcomwongse P, et al. Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program. NPJ Digit Med, 2019, 2: 25.
43. 陈明惠, 王子昂, 李家昱, 等. 基于人工智能辅助糖尿病视网膜病变分类研究. 激光与光电子学进展, 2025, (12): 1-21.
44. 杨雨帆, 袁立明, 王珂, 等. 基于图卷积网络的糖尿病视网膜病变分级模型. 计算机科学, 2024, 51(S2): 461-465.
45. 郭莹, 李绍杰. 基于小尺度跨层融合模型的糖尿病视网膜病变分类方法. 生物医学工程学杂志, 2024, 41(5): 861-868.
46. 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): 2211-2223.
47. Dai L, Wu L, Li H, et al. A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nat Commun, 2021, 12(1): 3242.
48. Arcadu F, Benmansour F, Maunz A, et al. Deep learning algorithm predicts diabetic retinopathy progression in individual patients. NPJ Digit Med, 2019, 2: 92.
49. Dai L, Sheng B, Chen T, et al. A deep learning system for predicting time to progression of diabetic retinopathy. Nat Med, 2024, 30(2): 584-594.

Chinese Journal of Evidence-Based Medicine

The value of deep learning in the diagnosis of diabetic retinopathy based on color fundus photographs: a meta-analysis

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content