## 1 Introduction  
Optical Coherence Tomography (OCT), first developed in the early 1990s, revolutionized non-invasive imaging by enabling cross-sectional visualization of microanatomical structures through the analysis of light reflections and temporal delays [1]. This technology advanced significantly in the 2000s with the integration of multilayer scanning, which facilitated three-dimensional reconstructions while reducing imaging time, thereby broadening its clinical utility [2]. Initially applied in cardiology for stent placement guidance and gastroenterology for gastrointestinal disease detection, retinal OCT has since become indispensable in ophthalmology, where its ability to delineate retinal layers supports early diagnosis of pathologies such as age-related macular degeneration (AMD), choroidal neovascularization (CNV), and diabetic macular edema (DME) [3, 4, 5, 6]. Despite its transformative impact, retinal OCT analysis remains constrained by reliance on manual interpretation, a challenge exacerbated by variability in image quality across patient populations [7, 8]. In response, machine learning (ML) has emerged as a promising tool for automating diagnostics. Recent studies demonstrate the efficacy of deep learning models in classifying retinal OCT scans, with reported sensitivity and specificity exceeding 90% in distinguishing AMD from healthy retinas [9]. Furthermore, ML algorithms have enabled the tracking of disease progression, uncovering biomarkers that inform therapeutic strategies at distinct pathological stages [10; 11]. Nevertheless, the generalization of these retinal OCT models across diverse clinical datasets remains problematic due to inconsistencies in imaging protocols and environmental factors [12]. The integration of ML into OCT analysis has evolved through distinct phases over the past fifteen years. Early studies (2010-2015) established foundational approaches using traditional ML algorithms, such as support vector machines and Random Forests, to classify retinal pathologies [13; 14]. These pioneering works demonstrated promising accuracy in distinguishing conditions like AMD from healthy retinas but were limited by small, homogeneous datasets [15; 16]. Early approaches for DME classification achieved notable success using traditional ML techniques [17], while recent work has demonstrated improved performance through novel deep learning architectures [18]. The subsequent period (2016-2020) saw significant methodological advances with the introduction of deep learning architectures, achieving improved classification accuracy exceeding 90% sensitivity and specificity [9; 10]. However, these studies faced persistent challenges in dataset standardization and model generalizability across diverse clinical settings. Recent research (2021-2025) has focused on addressing these limitations through standardized datasets like OCT5k [19] and advanced architectures combining OCT with OCT angiography [20]. Despite these advances, critical gaps remain in ensuring robust cross-dataset performance, transparent training processes, and clinical applicability across unseen pathologies. While emerging semi-supervised architectures integrating transformers and CNNs show promise [21], challenges in wide-field OCT imaging and cross-dataset learning persist [22]. Our study builds upon this historical progression by specifically addressing these gaps through multisource dataset integration and validation of model generalization capabilities. Recent advances in ML-enhanced OCT classification demonstrate broadening applications beyond ophthalmology, with significant progress in systemic disease diagnostics and interdisciplinary domains. Emerging standardized datasets like OCT5k, providing multigraded annotations for retinal layers and pathologies across 1672 scans, now enable robust benchmarking of segmentation algorithms while addressing data harmonization challenges [19]. [PERSON] et al.