OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning
Abstract Glaucoma poses a growing health challenge projected to escalate in the coming decades. However, current automated diagnostic approaches on Glaucoma diagnosis solely rely on black-box deep learning models, lacking explainability and trustworthiness. To address the issue, this study uses opti...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87219-w |
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| author | Md Mahmudul Hasan Jack Phu Henrietta Wang Arcot Sowmya Michael Kalloniatis Erik Meijering |
| author_facet | Md Mahmudul Hasan Jack Phu Henrietta Wang Arcot Sowmya Michael Kalloniatis Erik Meijering |
| author_sort | Md Mahmudul Hasan |
| collection | DOAJ |
| description | Abstract Glaucoma poses a growing health challenge projected to escalate in the coming decades. However, current automated diagnostic approaches on Glaucoma diagnosis solely rely on black-box deep learning models, lacking explainability and trustworthiness. To address the issue, this study uses optical coherence tomography (OCT) images to develop an explainable artificial intelligence (XAI) tool for diagnosing and staging glaucoma, with a focus on its clinical applicability. A total of 334 normal and 268 glaucomatous eyes (86 early, 72 moderate, 110 advanced) were included, signal processing theory was employed, and model interpretability was rigorously evaluated. Leveraging SHapley Additive exPlanations (SHAP)-based global feature ranking and partial dependency analysis (PDA) estimated decision boundary cut-offs on machine learning (ML) models, a novel algorithm was developed to implement an XAI tool. Using the selected features, ML models produce an AUC of 0.96 (95% CI: 0.95–0.98), 0.98 (95% CI: 0.96–1.00) and 1.00 (95% CI: 1.00–1.00) respectively on differentiating early, moderate and advanced glaucoma patients. Overall, machine outperformed clinicians in the early stage and overall glaucoma diagnosis with 10.4 –11.2% higher accuracy. The developed user-friendly XAI software tool shows potential as a valuable tool for eye care practitioners, offering transparent and interpretable insights to improve decision-making. |
| format | Article |
| id | doaj-art-2b323a63bbf4431cb2cfab6119a62a59 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-2b323a63bbf4431cb2cfab6119a62a592025-02-02T12:22:02ZengNature PortfolioScientific Reports2045-23222025-01-0115112010.1038/s41598-025-87219-wOCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learningMd Mahmudul Hasan0Jack Phu1Henrietta Wang2Arcot Sowmya3Michael Kalloniatis4Erik Meijering5School of Computer Science and Engineering, University of New South WalesSchool of Optometry and Vision Science, University of New South WalesSchool of Optometry and Vision Science, University of New South WalesSchool of Computer Science and Engineering, University of New South WalesSchool of Optometry and Vision Science, University of New South WalesSchool of Computer Science and Engineering, University of New South WalesAbstract Glaucoma poses a growing health challenge projected to escalate in the coming decades. However, current automated diagnostic approaches on Glaucoma diagnosis solely rely on black-box deep learning models, lacking explainability and trustworthiness. To address the issue, this study uses optical coherence tomography (OCT) images to develop an explainable artificial intelligence (XAI) tool for diagnosing and staging glaucoma, with a focus on its clinical applicability. A total of 334 normal and 268 glaucomatous eyes (86 early, 72 moderate, 110 advanced) were included, signal processing theory was employed, and model interpretability was rigorously evaluated. Leveraging SHapley Additive exPlanations (SHAP)-based global feature ranking and partial dependency analysis (PDA) estimated decision boundary cut-offs on machine learning (ML) models, a novel algorithm was developed to implement an XAI tool. Using the selected features, ML models produce an AUC of 0.96 (95% CI: 0.95–0.98), 0.98 (95% CI: 0.96–1.00) and 1.00 (95% CI: 1.00–1.00) respectively on differentiating early, moderate and advanced glaucoma patients. Overall, machine outperformed clinicians in the early stage and overall glaucoma diagnosis with 10.4 –11.2% higher accuracy. The developed user-friendly XAI software tool shows potential as a valuable tool for eye care practitioners, offering transparent and interpretable insights to improve decision-making.https://doi.org/10.1038/s41598-025-87219-wOptical coherence tomographyGlaucomaPerimetryExplainable machine learningSHAP analysisPartial dependency analysis |
| spellingShingle | Md Mahmudul Hasan Jack Phu Henrietta Wang Arcot Sowmya Michael Kalloniatis Erik Meijering OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning Scientific Reports Optical coherence tomography Glaucoma Perimetry Explainable machine learning SHAP analysis Partial dependency analysis |
| title | OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning |
| title_full | OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning |
| title_fullStr | OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning |
| title_full_unstemmed | OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning |
| title_short | OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning |
| title_sort | oct based diagnosis of glaucoma and glaucoma stages using explainable machine learning |
| topic | Optical coherence tomography Glaucoma Perimetry Explainable machine learning SHAP analysis Partial dependency analysis |
| url | https://doi.org/10.1038/s41598-025-87219-w |
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