American Diabetes Association
3 files

A Multitask Deep-Learning System to Classify Diabetic Macular Edema for Different Optical Coherence Tomography Devices: A Multicenter Analysis

posted on 2021-07-28, 18:00 authored by Fangyao Tang, Xi Wang, An-ran Ran, Carmen KM Chan, Mary Ho, Wilson Yip, Alvin L. Young, Jerry Lok, Simon Szeto, Jason Chan, Fanny Yip, Raymond Wong, Ziqi Tang, Dawei Yang, Danny S Ng, Li Jia Chen, Marten Brelén, Victor Chu, Kenneth Li, Tracy HT Lai, Gavin S Tan, Daniel SW Ting, Haifan Huang, Haoyu Chen, Jacey Hongjie Ma, Shibo Tang, Theodore Leng, Schahrouz Kakavand, Suria S Mannil, Robert T Chang, Gerald Liew, Bamini Gopinath, Timothy YY Lai, Chi Pui Pang, Peter H Scanlon, Tien Yin Wong, Clement C Tham, Hao Chen, Pheng-Ann Heng, Carol Y Cheung
Objective: Diabetic macular edema (DME) is the primary cause of vision loss among individuals with diabetes mellitus (DM). We developed, validated, and tested a deep-learning (DL) system for classifying DME using images from three common commercially available optical coherence tomography (OCT) devices.

Research Design and Methods: We trained and validated two versions of a multi-task convolution neural network (CNN) to classify DME (center-involved DME [CI-DME], non-CI-DME, or absence of DME) using three-dimensional (3D) volume-scans and two-dimensional (2D) B-scans respectively. For both 3D and 2D CNNs, we employed the residual network (ResNet) as the backbone. For the 3D CNN, we used a 3D version of ResNet-34 with the last fully connected layer removed as the feature extraction module. A total of 73,746 OCT images were used for training and primary validation. External testing was performed using 26,981 images across seven independent datasets from Singapore, Hong Kong, the US, China, and Australia.

Results: In classifying the presence or absence of DME, the DL system achieved area under the receiver operating characteristic curves (AUROCs) of 0.937 (95% CI 0.920–0.954), 0.958 (0.930–0.977), and 0.965 (0.948–0.977) for primary dataset obtained from Cirrus, Spectralis, and Triton OCTs respectively, in addition to AUROCs greater than 0.906 for the external datasets. For the further classification of the CI-DME and non-CI-DME subgroups, the AUROCs were 0.968 (0.940–0.995), 0.951 (0.898–0.982), and 0.975 (0.947–0.991) for the primary dataset and greater than 0.894 for the external datasets.

Conclusion: We demonstrated excellent performance with a DL system for the automated classification of DME, highlighting its potential as a promising second-line screening tool for patients with DM, which may potentially create a more effective triaging mechanism to eye clinics.


This study was funded by the Research Grants Council General Research Fund (GRF), Hong Kong (ref no.: 14102418) and Innovation and Technology Fund (ITF), Hong Kong (ref no: MRP/056/20X); Research to Prevent Blindness; and NIH grant no. P30-EY026877. The funder had no role in study design, data collection, data analysis, data interpretation, or report writing.


Usage metrics

    Diabetes Care


    Ref. manager