A deep learning model integrating domain-specific features for enhanced glaucoma diagnosis
Abstract Glaucoma is a group of serious eye diseases that can cause incurable blindness. Despite the critical need for early detection, over 60% of cases remain undiagnosed, especially in less developed regions. Glaucoma diagnosis is a costly task and some models have been proposed to automate diagn...
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BMC
2025-05-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-025-02925-9 |
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| author | Jie Xu Erkang Jing Yidong Chai |
| author_facet | Jie Xu Erkang Jing Yidong Chai |
| author_sort | Jie Xu |
| collection | DOAJ |
| description | Abstract Glaucoma is a group of serious eye diseases that can cause incurable blindness. Despite the critical need for early detection, over 60% of cases remain undiagnosed, especially in less developed regions. Glaucoma diagnosis is a costly task and some models have been proposed to automate diagnosis based on images of the retina, specifically the area known as the optic cup and the associated disc where retinal blood vessels and nerves enter and leave the eye. However, diagnosis is complicated because both normal and glaucoma-affected eyes can vary greatly in appearance. Some normal cases, like glaucoma, exhibit a larger cup-to-disc ratio, one of the main diagnostic criteria, making it challenging to distinguish between them. We propose a deep learning model with domain features (DLMDF) to combine unstructured and structured features to distinguish between glaucoma and physiologic large cups. The structured features were based upon the known cup-to-disc ratios of the four quadrants of the optic discs in normal, physiologic large cups, and glaucomatous optic cups. We segmented each cup and disc using a fully convolutional neural network and then calculated the cup size, disc size, and cup-to-disc ratio of each quadrant. The unstructured features were learned from a deep convolutional neural network. The average precision (AP) for disc segmentation was 98.52%, and for cup segmentation it was also 98.57%. Thus, the relatively high AP values enabled us to calculate the 15 reliable features from each segmented disc and cup. In classification tasks, the DLMDF outperformed other models, achieving superior accuracy, precision, and recall. These results validate the effectiveness of combining deep learning-derived features with domain-specific structured features, underscoring the potential of this approach to advance glaucoma diagnosis. |
| format | Article |
| id | doaj-art-3045fbb8bb194dce8b7b860d612b6b51 |
| institution | OA Journals |
| issn | 1472-6947 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-3045fbb8bb194dce8b7b860d612b6b512025-08-20T02:34:07ZengBMCBMC Medical Informatics and Decision Making1472-69472025-05-0125111010.1186/s12911-025-02925-9A deep learning model integrating domain-specific features for enhanced glaucoma diagnosisJie Xu0Erkang Jing1Yidong Chai2Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical UniversitySchool of Management of Hefei, Key Laboratory of Process Optimization and Intelligence Decision Making, University of Technology, Minister of EducationSchool of Management of Hefei, Key Laboratory of Process Optimization and Intelligence Decision Making, University of Technology, Minister of EducationAbstract Glaucoma is a group of serious eye diseases that can cause incurable blindness. Despite the critical need for early detection, over 60% of cases remain undiagnosed, especially in less developed regions. Glaucoma diagnosis is a costly task and some models have been proposed to automate diagnosis based on images of the retina, specifically the area known as the optic cup and the associated disc where retinal blood vessels and nerves enter and leave the eye. However, diagnosis is complicated because both normal and glaucoma-affected eyes can vary greatly in appearance. Some normal cases, like glaucoma, exhibit a larger cup-to-disc ratio, one of the main diagnostic criteria, making it challenging to distinguish between them. We propose a deep learning model with domain features (DLMDF) to combine unstructured and structured features to distinguish between glaucoma and physiologic large cups. The structured features were based upon the known cup-to-disc ratios of the four quadrants of the optic discs in normal, physiologic large cups, and glaucomatous optic cups. We segmented each cup and disc using a fully convolutional neural network and then calculated the cup size, disc size, and cup-to-disc ratio of each quadrant. The unstructured features were learned from a deep convolutional neural network. The average precision (AP) for disc segmentation was 98.52%, and for cup segmentation it was also 98.57%. Thus, the relatively high AP values enabled us to calculate the 15 reliable features from each segmented disc and cup. In classification tasks, the DLMDF outperformed other models, achieving superior accuracy, precision, and recall. These results validate the effectiveness of combining deep learning-derived features with domain-specific structured features, underscoring the potential of this approach to advance glaucoma diagnosis.https://doi.org/10.1186/s12911-025-02925-9Deep learningDisease diagnosisGlaucoma diagnosisMedical image analysisPhysiologic large cupsImage segmentation |
| spellingShingle | Jie Xu Erkang Jing Yidong Chai A deep learning model integrating domain-specific features for enhanced glaucoma diagnosis BMC Medical Informatics and Decision Making Deep learning Disease diagnosis Glaucoma diagnosis Medical image analysis Physiologic large cups Image segmentation |
| title | A deep learning model integrating domain-specific features for enhanced glaucoma diagnosis |
| title_full | A deep learning model integrating domain-specific features for enhanced glaucoma diagnosis |
| title_fullStr | A deep learning model integrating domain-specific features for enhanced glaucoma diagnosis |
| title_full_unstemmed | A deep learning model integrating domain-specific features for enhanced glaucoma diagnosis |
| title_short | A deep learning model integrating domain-specific features for enhanced glaucoma diagnosis |
| title_sort | deep learning model integrating domain specific features for enhanced glaucoma diagnosis |
| topic | Deep learning Disease diagnosis Glaucoma diagnosis Medical image analysis Physiologic large cups Image segmentation |
| url | https://doi.org/10.1186/s12911-025-02925-9 |
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