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.

This study evaluated the generalization and reliability of machine learning models for multiclass classification of retinal pathologies using a diverse set of images representing eight disease categories. Images were aggregated from two public datasets and divided into training, validation, and test sets, with an additional independent dataset used for external validation. Multiple modeling approaches were compared, including classical machine learning algorithms, convolutional neural networks with and without data augmentation, and a deep neural network using pre-trained feature extraction. Analysis of learning dynamics revealed that classical models and unaugmented convolutional neural networks exhibited overfitting and poor generalization, while models with data augmentation and the deep neural network showed healthy, parallel convergence of training and validation performance. Only the deep neural network demonstrated a consistent, monotonic decrease in accuracy, F1-score, and recall from training through external validation, indicating robust generalization. These results underscore the necessity of evaluating learning dynamics (not just summary metrics) to ensure model reliability and patient safety. Typically, model performance is expected to decrease gradually as data becomes less familiar. Therefore, models that do not exhibit these healthy learning dynamics, or that show unexpected improvements in performance on subsequent datasets, should not be considered for clinical application, as such patterns may indicate methodological flaws or data leakage rather than true generalization.