Deep learning-based optical coherence tomography and retinal images for detection of diabetic retinopathy: a systematic and meta analysis
ObjectiveTo systematically review and meta-analyze the effectiveness of deep learning algorithms applied to optical coherence tomography (OCT) and retinal images for the detection of diabetic retinopathy (DR).MethodsWe conducted a comprehensive literature search in multiple databases including PubMe...
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2025.1485311/full |
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| author | Zheng Bi Jinju Li Qiongyi Liu Zhaohui Fang Zhaohui Fang |
| author_facet | Zheng Bi Jinju Li Qiongyi Liu Zhaohui Fang Zhaohui Fang |
| author_sort | Zheng Bi |
| collection | DOAJ |
| description | ObjectiveTo systematically review and meta-analyze the effectiveness of deep learning algorithms applied to optical coherence tomography (OCT) and retinal images for the detection of diabetic retinopathy (DR).MethodsWe conducted a comprehensive literature search in multiple databases including PubMed, Cochrane library, Web of Science, Embase and IEEE Xplore up to July 2024. Studies that utilized deep learning techniques for the detection of DR using OCT and retinal images were included. Data extraction and quality assessment were performed independently by two reviewers. Meta-analysis was conducted to determine pooled sensitivity, specificity, and diagnostic odds ratios.ResultsA total of 47 studies were included in the systematic review, 10 were meta-analyzed, encompassing a total of 188268 retinal images and OCT scans. The meta-analysis revealed a pooled sensitivity of 1.88 (95% CI: 1.45-2.44) and a pooled specificity of 1.33 (95% CI: 0.97-1.84) for the detection of DR using deep learning models. All of the outcome of deep learning-based optical coherence tomography ORs ≥0.785, indicating that all included studies with artificial intelligence assistance produced good boosting results.ConclusionDeep learning-based approaches show high accuracy in detecting diabetic retinopathy from OCT and retinal images, supporting their potential as reliable tools in clinical settings. Future research should focus on standardizing datasets, improving model interpretability, and validating performance across diverse populations.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42024575847. |
| format | Article |
| id | doaj-art-074d3ed3475f4ea5a9be493c4fd47f86 |
| institution | DOAJ |
| issn | 1664-2392 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj-art-074d3ed3475f4ea5a9be493c4fd47f862025-08-20T03:01:58ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922025-03-011610.3389/fendo.2025.14853111485311Deep learning-based optical coherence tomography and retinal images for detection of diabetic retinopathy: a systematic and meta analysisZheng Bi0Jinju Li1Qiongyi Liu2Zhaohui Fang3Zhaohui Fang4Department of Endocrinology, The First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, Anhui, ChinaFirst Clinical Medical College, Anhui University of Traditional Chinese Medicine, Hefei, Anhui, ChinaFirst Clinical Medical College, Anhui University of Traditional Chinese Medicine, Hefei, Anhui, ChinaDepartment of Endocrinology, The First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, Anhui, ChinaXin ‘an Medical and Chinese Medicine Modernization Research Institute, Hefei Comprehensive National Science Center, Hefei, Anhui, ChinaObjectiveTo systematically review and meta-analyze the effectiveness of deep learning algorithms applied to optical coherence tomography (OCT) and retinal images for the detection of diabetic retinopathy (DR).MethodsWe conducted a comprehensive literature search in multiple databases including PubMed, Cochrane library, Web of Science, Embase and IEEE Xplore up to July 2024. Studies that utilized deep learning techniques for the detection of DR using OCT and retinal images were included. Data extraction and quality assessment were performed independently by two reviewers. Meta-analysis was conducted to determine pooled sensitivity, specificity, and diagnostic odds ratios.ResultsA total of 47 studies were included in the systematic review, 10 were meta-analyzed, encompassing a total of 188268 retinal images and OCT scans. The meta-analysis revealed a pooled sensitivity of 1.88 (95% CI: 1.45-2.44) and a pooled specificity of 1.33 (95% CI: 0.97-1.84) for the detection of DR using deep learning models. All of the outcome of deep learning-based optical coherence tomography ORs ≥0.785, indicating that all included studies with artificial intelligence assistance produced good boosting results.ConclusionDeep learning-based approaches show high accuracy in detecting diabetic retinopathy from OCT and retinal images, supporting their potential as reliable tools in clinical settings. Future research should focus on standardizing datasets, improving model interpretability, and validating performance across diverse populations.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42024575847.https://www.frontiersin.org/articles/10.3389/fendo.2025.1485311/fullmeta analysisdeep learningdiabetic retinopathyimage detectionoptical coherence tomography |
| spellingShingle | Zheng Bi Jinju Li Qiongyi Liu Zhaohui Fang Zhaohui Fang Deep learning-based optical coherence tomography and retinal images for detection of diabetic retinopathy: a systematic and meta analysis Frontiers in Endocrinology meta analysis deep learning diabetic retinopathy image detection optical coherence tomography |
| title | Deep learning-based optical coherence tomography and retinal images for detection of diabetic retinopathy: a systematic and meta analysis |
| title_full | Deep learning-based optical coherence tomography and retinal images for detection of diabetic retinopathy: a systematic and meta analysis |
| title_fullStr | Deep learning-based optical coherence tomography and retinal images for detection of diabetic retinopathy: a systematic and meta analysis |
| title_full_unstemmed | Deep learning-based optical coherence tomography and retinal images for detection of diabetic retinopathy: a systematic and meta analysis |
| title_short | Deep learning-based optical coherence tomography and retinal images for detection of diabetic retinopathy: a systematic and meta analysis |
| title_sort | deep learning based optical coherence tomography and retinal images for detection of diabetic retinopathy a systematic and meta analysis |
| topic | meta analysis deep learning diabetic retinopathy image detection optical coherence tomography |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2025.1485311/full |
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