## 1 Introduction
(2024) advanced automated AMD detection through a novel classification approach combining deep feature extraction with traditional ML classifiers, achieving robust performance across diverse OCT presentations [18]. [PERSON] et al. introduced a fine-grained model combining OCT and OCT angiography to stratify Port Wine Stains into vascular subtypes [20]. For diabetic macular edema, [PERSON] et al. advanced hyperreflective foci detection using KiU-Net-derived segmentation, addressing microlesion quantification in small-sample environments [23]. [PERSON] et al. validated convolutional neural networks for OCT-based classification [24], while [PERSON] et al. established multimodal OCT predictions for myopic maculopathy progression [25]. Semi-supervised architectures combining transformers and CNNs improved wide-field OCT segmentation accuracy over conventional methods [21]. [PERSON] et al. demonstrated hierarchical networks for joint retinal layer/fluid segmentation [22]. Post-surgical detection from OCT biomarkers achieved high accuracy via ResNet18 models [26]. [PERSON] et al. proposed retinal biomarker screening models for psychiatric disorders [27], while [PERSON] et al. correlated deep learning-derived ellipsoid zone parameters with functional outcomes in geographic atrophy [28]. [PERSON] et al. identified OCT radiomic features predictive of anti-VEGF responses [29]. Expanding systemic prognostic applications, ML models leveraging OCT-derived retinal microstructure features have demonstrated predictive utility for survival outcomes in glioblastoma patients, revealing correlations between neural thinning and disease progression [30], with glaucoma progression detection models achieving error thresholds necessary for clinical viability in monitoring functional decline [31]. Explainable frameworks linked OCT-derived retinal nerve fiber layer metrics to visual field indices [32], while [PERSON] et al. highlighted OCT dataset standardization gaps [33]. [PERSON] et al. validated OCT vascular analytics for cardiovascular risk stratification [34], and cross-disciplinary innovations now include agricultural applications, where OCT-based deep learning models enable non-invasive chick gender classification through automated analysis of cloacal microstructures [35]. Generative adversarial networks demonstrated OCT super-resolution capabilities [36], with potential extensions enabling virtual histopathology translation for endometrial receptivity analysis in reproductive medicine, building on recent advances in OCT imaging of reproductive processes [37]. [PERSON] et al. optimized multiple sclerosis diagnosis via SHAP-driven OCT feature selection [38]. Collectively, these advancements underscore OCT's evolution into a multidimensional diagnostic and analytical tool, bridging medical and non-medical domains through optimized ML frameworks. This study introduces a methodological approach with three principal aims. The primary objective involves developing ML models trained on integrated multisource datasets, employing stratified data partitioning to preserve pathological feature distributions while evaluating generalization on anatomically distinct conditions excluded from training. Secondarily, the framework systematically compares multiple classification algorithms through clinical utility metrics, examines resampling techniques to mitigate class imbalance effects, and validates clinical translatability via domain-specific performance thresholds. By combining cross-dataset training with controlled validation on unseen pathological presentations, the research establishes an optimized pipeline for developing diagnostic tools that reduce interpretation variability while maintaining robustness across imaging protocols and disease manifestations.