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. 2022 Jul;36(7):1433-1441.
doi: 10.1038/s41433-021-01552-8. Epub 2021 Jul 1.

Deep learning-based automated detection for diabetic retinopathy and diabetic macular oedema in retinal fundus photographs

Feng Li #  1 , Yuguang Wang #  1 , Tianyi Xu #  2 , Lin Dong  1 , Lei Yan  1 , Minshan Jiang  3 , Xuedian Zhang  4   5 , Hong Jiang  6 , Zhizheng Wu  7 , Haidong Zou  8
Affiliations

Deep learning-based automated detection for diabetic retinopathy and diabetic macular oedema in retinal fundus photographs

Feng Li et al. Eye (Lond). 2022 Jul.

Abstract

Objectives: To present and validate a deep ensemble algorithm to detect diabetic retinopathy (DR) and diabetic macular oedema (DMO) using retinal fundus images.

Methods: A total of 8739 retinal fundus images were collected from a retrospective cohort of 3285 patients. For detecting DR and DMO, a multiple improved Inception-v4 ensembling approach was developed. We measured the algorithm's performance and made a comparison with that of human experts on our primary dataset, while its generalization was assessed on the publicly available Messidor-2 dataset. Also, we investigated systematically the impact of the size and number of input images used in training on model's performance, respectively. Further, the time budget of training/inference versus model performance was analyzed.

Results: On our primary test dataset, the model achieved an 0.992 (95% CI, 0.989-0.995) AUC corresponding to 0.925 (95% CI, 0.916-0.936) sensitivity and 0.961 (95% CI, 0.950-0.972) specificity for referable DR, while the sensitivity and specificity for ophthalmologists ranged from 0.845 to 0.936, and from 0.912 to 0.971, respectively. For referable DMO, our model generated an AUC of 0.994 (95% CI, 0.992-0.996) with a 0.930 (95% CI, 0.919-0.941) sensitivity and 0.971 (95% CI, 0.965-0.978) specificity, whereas ophthalmologists obtained sensitivities ranging between 0.852 and 0.946, and specificities ranging between 0.926 and 0.985.

Conclusion: This study showed that the deep ensemble model exhibited excellent performance in detecting DR and DMO, and had good robustness and generalization, which could potentially help support and expand DR/DMO screening programs.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The architecture of the improved Inception-v4 (leftmost side) and the ensemble of five classification model (rightmost side).
Fig. 2
Fig. 2. Performance of the model and ophthalmologists for classifying NRDR/RDR and NRDMO/RDMO on our primary test dataset.
a NRDR/RDR (b) NRDMO/RDMO.
Fig. 3
Fig. 3. The impact of the input image size on model performance.
a NRDR/RDR classification on our primary test set. b NRDMO/RDMO classification on our primary test set. c NRDR/RDR classification on the second publicly available Messidor-2 set. d NRDMO/RDMO classification on the second publicly available Messidor-2 set.
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