Development and validation of a 3-D deep learning system for diabetic macular oedema classification on optical coherence tomography images
Objectives To develop and validate an automated diabetic macular oedema (DME) classification system based on the images from different three-dimensional optical coherence tomography (3-D OCT) devices.Design A multicentre, platform-based development study using retrospective and cross-sectional data....
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BMJ Publishing Group
2025-05-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/15/5/e099167.full |
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| author | Mingzhi Zhang Tsz Kin Ng Yi Zheng Guihua Zhang Jian-Wei Lin Ji Wang Jie Ji Peiwen Xie Yongqun Xiong Hanfu Wu Cui Liu Huishan Zhu Jinqu Huang Leixian Lin |
| author_facet | Mingzhi Zhang Tsz Kin Ng Yi Zheng Guihua Zhang Jian-Wei Lin Ji Wang Jie Ji Peiwen Xie Yongqun Xiong Hanfu Wu Cui Liu Huishan Zhu Jinqu Huang Leixian Lin |
| author_sort | Mingzhi Zhang |
| collection | DOAJ |
| description | Objectives To develop and validate an automated diabetic macular oedema (DME) classification system based on the images from different three-dimensional optical coherence tomography (3-D OCT) devices.Design A multicentre, platform-based development study using retrospective and cross-sectional data. Data were subjected to a two-level grading system by trained graders and a retina specialist, and categorised into three types: no DME, non-centre-involved DME and centre-involved DME (CI-DME). The 3-D convolutional neural networks algorithm was used for DME classification system development. The deep learning (DL) performance was compared with the diabetic retinopathy experts.Setting Data were collected from Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Chaozhou People’s Hospital and The Second Affiliated Hospital of Shantou University Medical College from January 2010 to December 2023.Participants 7790 volumes of 7146 eyes from 4254 patients were annotated, of which 6281 images were used as the development set and 1509 images were used as the external validation set, split based on the centres.Main outcomes Accuracy, F1-score, sensitivity, specificity, area under receiver operating characteristic curve (AUROC) and Cohen’s kappa were calculated to evaluate the performance of the DL algorithm.Results In classifying DME with non-DME, our model achieved an AUROCs of 0.990 (95% CI 0.983 to 0.996) and 0.916 (95% CI 0.902 to 0.930) for hold-out testing dataset and external validation dataset, respectively. To distinguish CI-DME from non-centre-involved-DME, our model achieved AUROCs of 0.859 (95% CI 0.812 to 0.906) and 0.881 (95% CI 0.859 to 0.902), respectively. In addition, our system showed comparable performance (Cohen’s κ: 0.85 and 0.75) to the retina experts (Cohen’s κ: 0.58–0.92 and 0.70–0.71).Conclusions Our DL system achieved high accuracy in multiclassification tasks on DME classification with 3-D OCT images, which can be applied to population-based DME screening. |
| format | Article |
| id | doaj-art-bd930a2797b3488d9225b8d4264e40a7 |
| institution | OA Journals |
| issn | 2044-6055 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMJ Publishing Group |
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| series | BMJ Open |
| spelling | doaj-art-bd930a2797b3488d9225b8d4264e40a72025-08-20T02:02:47ZengBMJ Publishing GroupBMJ Open2044-60552025-05-0115510.1136/bmjopen-2025-099167Development and validation of a 3-D deep learning system for diabetic macular oedema classification on optical coherence tomography imagesMingzhi Zhang0Tsz Kin Ng1Yi Zheng2Guihua Zhang3Jian-Wei Lin4Ji Wang5Jie Ji6Peiwen Xie7Yongqun Xiong8Hanfu Wu9Cui Liu10Huishan Zhu11Jinqu Huang12Leixian Lin13The Chinese University of Hong Kong, Hong Kong, ChinaJoint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, ChinaJoint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, ChinaJoint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, ChinaJoint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, ChinaJoint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, ChinaShantou University Medical College, Shantou, ChinaJoint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, ChinaJoint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, ChinaJoint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, ChinaJoint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, ChinaJoint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, ChinaJoint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, ChinaJoint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, ChinaObjectives To develop and validate an automated diabetic macular oedema (DME) classification system based on the images from different three-dimensional optical coherence tomography (3-D OCT) devices.Design A multicentre, platform-based development study using retrospective and cross-sectional data. Data were subjected to a two-level grading system by trained graders and a retina specialist, and categorised into three types: no DME, non-centre-involved DME and centre-involved DME (CI-DME). The 3-D convolutional neural networks algorithm was used for DME classification system development. The deep learning (DL) performance was compared with the diabetic retinopathy experts.Setting Data were collected from Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Chaozhou People’s Hospital and The Second Affiliated Hospital of Shantou University Medical College from January 2010 to December 2023.Participants 7790 volumes of 7146 eyes from 4254 patients were annotated, of which 6281 images were used as the development set and 1509 images were used as the external validation set, split based on the centres.Main outcomes Accuracy, F1-score, sensitivity, specificity, area under receiver operating characteristic curve (AUROC) and Cohen’s kappa were calculated to evaluate the performance of the DL algorithm.Results In classifying DME with non-DME, our model achieved an AUROCs of 0.990 (95% CI 0.983 to 0.996) and 0.916 (95% CI 0.902 to 0.930) for hold-out testing dataset and external validation dataset, respectively. To distinguish CI-DME from non-centre-involved-DME, our model achieved AUROCs of 0.859 (95% CI 0.812 to 0.906) and 0.881 (95% CI 0.859 to 0.902), respectively. In addition, our system showed comparable performance (Cohen’s κ: 0.85 and 0.75) to the retina experts (Cohen’s κ: 0.58–0.92 and 0.70–0.71).Conclusions Our DL system achieved high accuracy in multiclassification tasks on DME classification with 3-D OCT images, which can be applied to population-based DME screening.https://bmjopen.bmj.com/content/15/5/e099167.full |
| spellingShingle | Mingzhi Zhang Tsz Kin Ng Yi Zheng Guihua Zhang Jian-Wei Lin Ji Wang Jie Ji Peiwen Xie Yongqun Xiong Hanfu Wu Cui Liu Huishan Zhu Jinqu Huang Leixian Lin Development and validation of a 3-D deep learning system for diabetic macular oedema classification on optical coherence tomography images BMJ Open |
| title | Development and validation of a 3-D deep learning system for diabetic macular oedema classification on optical coherence tomography images |
| title_full | Development and validation of a 3-D deep learning system for diabetic macular oedema classification on optical coherence tomography images |
| title_fullStr | Development and validation of a 3-D deep learning system for diabetic macular oedema classification on optical coherence tomography images |
| title_full_unstemmed | Development and validation of a 3-D deep learning system for diabetic macular oedema classification on optical coherence tomography images |
| title_short | Development and validation of a 3-D deep learning system for diabetic macular oedema classification on optical coherence tomography images |
| title_sort | development and validation of a 3 d deep learning system for diabetic macular oedema classification on optical coherence tomography images |
| url | https://bmjopen.bmj.com/content/15/5/e099167.full |
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