Stratified Multisource Optical Coherence Tomography Integration and Cross-Pathology Validation Framework for Automated Retinal Diagnostics
This study presents a clinical utility-driven machine learning framework for retinal Optical Coherence Tomography classification, addressing challenges posed by manual interpretation variability and dataset heterogeneity. The methodology integrates biomimetic data partitioning, deep biomarker extrac...
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| Format: | Article |
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MDPI AG
2025-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/9/4985 |
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| author | Michael Sher Riah Sharma David Remyes Daniel Nasef Demarcus Nasef Milan Toma |
| author_facet | Michael Sher Riah Sharma David Remyes Daniel Nasef Demarcus Nasef Milan Toma |
| author_sort | Michael Sher |
| collection | DOAJ |
| description | This study presents a clinical utility-driven machine learning framework for retinal Optical Coherence Tomography classification, addressing challenges posed by manual interpretation variability and dataset heterogeneity. The methodology integrates biomimetic data partitioning, deep biomarker extraction via pretrained VGG16 networks, and automated model selection optimized for clinical decision-making. Stratified data curation preserved pathological distributions across training, validation, and testing subsets, while SMOTE optimization mitigated class imbalance. Cross-pathology testing evaluated generalizability on anatomically distinct retinal conditions excluded from training, assessing the framework’s robustness to unseen pathologies. Clinical utility metrics prioritized alignment with ophthalmological imperatives, emphasizing negative predictive value to minimize false negatives and enhance diagnostic reliability. The framework advances AI-driven Optical Coherence Tomography diagnostics by harmonizing computational performance with patient-centered outcomes, enabling standardized disease detection across diverse clinical datasets through robust feature generalization. |
| format | Article |
| id | doaj-art-abb39ee2f26e4f53be98f09f23efc8a0 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-abb39ee2f26e4f53be98f09f23efc8a02025-08-20T03:52:56ZengMDPI AGApplied Sciences2076-34172025-04-01159498510.3390/app15094985Stratified Multisource Optical Coherence Tomography Integration and Cross-Pathology Validation Framework for Automated Retinal DiagnosticsMichael Sher0Riah Sharma1David Remyes2Daniel Nasef3Demarcus Nasef4Milan Toma5Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, Nassau County, Long Island, NY 11568, USADepartment of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, Nassau County, Long Island, NY 11568, USADepartment of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, Nassau County, Long Island, NY 11568, USADepartment of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, Nassau County, Long Island, NY 11568, USADepartment of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, Nassau County, Long Island, NY 11568, USADepartment of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, Nassau County, Long Island, NY 11568, USAThis study presents a clinical utility-driven machine learning framework for retinal Optical Coherence Tomography classification, addressing challenges posed by manual interpretation variability and dataset heterogeneity. The methodology integrates biomimetic data partitioning, deep biomarker extraction via pretrained VGG16 networks, and automated model selection optimized for clinical decision-making. Stratified data curation preserved pathological distributions across training, validation, and testing subsets, while SMOTE optimization mitigated class imbalance. Cross-pathology testing evaluated generalizability on anatomically distinct retinal conditions excluded from training, assessing the framework’s robustness to unseen pathologies. Clinical utility metrics prioritized alignment with ophthalmological imperatives, emphasizing negative predictive value to minimize false negatives and enhance diagnostic reliability. The framework advances AI-driven Optical Coherence Tomography diagnostics by harmonizing computational performance with patient-centered outcomes, enabling standardized disease detection across diverse clinical datasets through robust feature generalization.https://www.mdpi.com/2076-3417/15/9/4985retinal OCT classificationmachine learningclinical utilitySMOTE optimizationlogistic regression |
| spellingShingle | Michael Sher Riah Sharma David Remyes Daniel Nasef Demarcus Nasef Milan Toma Stratified Multisource Optical Coherence Tomography Integration and Cross-Pathology Validation Framework for Automated Retinal Diagnostics Applied Sciences retinal OCT classification machine learning clinical utility SMOTE optimization logistic regression |
| title | Stratified Multisource Optical Coherence Tomography Integration and Cross-Pathology Validation Framework for Automated Retinal Diagnostics |
| title_full | Stratified Multisource Optical Coherence Tomography Integration and Cross-Pathology Validation Framework for Automated Retinal Diagnostics |
| title_fullStr | Stratified Multisource Optical Coherence Tomography Integration and Cross-Pathology Validation Framework for Automated Retinal Diagnostics |
| title_full_unstemmed | Stratified Multisource Optical Coherence Tomography Integration and Cross-Pathology Validation Framework for Automated Retinal Diagnostics |
| title_short | Stratified Multisource Optical Coherence Tomography Integration and Cross-Pathology Validation Framework for Automated Retinal Diagnostics |
| title_sort | stratified multisource optical coherence tomography integration and cross pathology validation framework for automated retinal diagnostics |
| topic | retinal OCT classification machine learning clinical utility SMOTE optimization logistic regression |
| url | https://www.mdpi.com/2076-3417/15/9/4985 |
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