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Yayın Automated diagnosis of Alzheimer’s Disease using OCT and OCTA: a systematic review(Institute of Electrical and Electronics Engineers Inc., 2024-08-06) Turkan, Yasemin; Tek, Faik Boray; Arpacı, Fatih; Arslan, Ozan; Toslak, Devrim; Bulut, Mehmet; Yaman, AylinRetinal optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) have emerged as promising, non-invasive, and cost-effective modalities for the early diagnosis of Alzheimer's disease (AD). However, a comprehensive review of automated deep learning techniques for diagnosing AD or mild cognitive impairment (MCI) using OCT/OCTA data is lacking. We addressed this gap by conducting a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. We systematically searched databases, including Scopus, PubMed, and Web of Science, and identified 16 important studies from an initial set of 4006 references. We then analyzed these studies through a structured framework, focusing on the key aspects of deep learning workflows for AD/MCI diagnosis using OCT-OCTA. This included dataset curation, model training, and validation methodologies. Our findings indicate a shift towards employing end-to-end deep learning models to directly analyze OCT/OCTA images in diagnosing AD/MCI, moving away from traditional machine learning approaches. However, we identified inconsistencies in the data collection methods across studies, leading to varied outcomes. We emphasize the need for longitudinal studies on early AD and MCI diagnosis, along with further research on interpretability tools to enhance model accuracy and reliability for clinical translation.Yayın Deep learning-based analysis of retinal OCT scans for detection of Alzheimer’s disease(Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, 2026-01-23) Turkan, Yasemin; Tek, Faik Boray; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Bilgisayar Mühendisliği Doktora Programı; Işık University, School of Graduate Studies, Ph.D. in Computer EngineeringAlterations in retinal layer thickness have been associated with neurodegenerative diseases such as Alzheimer’s disease (AD). These structural changes can be measured using a noninvasive imaging technology called Optical Coherence Tomography (OCT). Previous research has mostly focused on the statistical associations between segmented retinal layer thickness and AD derived from OCT or OCTA devices. Unlike conventional medical image classification tasks, early detection is more challenging than diagnosis because imaging precedes clinical diagnosis by several years. Deep learning (DL), particularly through convolutional neural networks (CNNs) and transfer learning, has demonstrated strong performance in image-based disease detection tasks. However, the application of DL directly on unsegmented raw OCT B-scan images for early AD detection remains underexplored. Therefore, in this thesis, we address this research gap by proposing a deep learning-based approach that uses raw OCT images for early Alzheimer’s disease detection. All related studies in the literature have heavily relied on private and in-situ cohorts that lack interoperability. In contrast, the UK Biobank (2022) offers a unique resource for investigating the associations between retinal structure and systemic health, comprising over 85,000 OCT scans linked to cognitive and health-related data. Between the initial scan period (2010–2015) and July 2023, 539 participants in the dataset were diagnosed with AD. Although the UK Biobank is somewhat limited by the absence of OCTA scans, we utilized this dataset to detect early AD using OCT scans. After a rigorous data-exclusion process, this study used a targeted 4-year window, selecting participants diagnosed with AD within 4 years of their baseline assessments. The AD group was matched by age, sex, eye, and instance with a randomly selected balanced Healthy Control group (N = 30). We first evaluated the predictive value of isolated 2D B-scans using pretrained deep learning architectures. In these tests, the ResNet-34 model achieved a mean AUC of 0.624 ± 0.060. Saliency map analysis of these B-scans highlighted the critical importance of the central macular region, whereas peripheral areas showed negligible contribution to the model’s decision. To overcome the limitations of isolated B-scans and leverage 3D information, we generated a 3D-informed en-face thickness projection map from the OCT B-scans. This pipeline was optimized to focus on the diagnostically relevant 3 mm inner macular region, effectively filtering out peripheral noise. Our study of thickness maps identified the Ganglion Cell Layer (GCL) as the most significant indicator of preclinical AD. The VGG-19 model, trained on GCL thickness maps with a year-weighted loss function, achieved a peak mean AUC of 0.750 ± 0.037. Notably, the traditional clinical benchmark, the Retinal Nerve Fiber Layer (RNFL), exhibited negligible predictive value in this pre-symptomatic cohort. We also developed a Multi-Modal Soft-Voting Ensemble model to further increase predictive accuracy and emulate clinical decision-making. This model integrates structural insights from B-scans and GCIPL thickness maps with clinical and demographic data. The ensemble approach achieved the highest mean AUC of 0.85 and significantly outperformed individual modalities. Furthermore, an ablation study using only image modalities (B-scans and thickness maps) yielded an AUC of 0.84. This result highlights the strong complementary value of combined structural data. Longitudinal sensitivity analysis also established a “diagnostic horizon” for retinal biomarkers. We observed that predictive accuracy is highest between 4 and 8 years prior to clinical diagnosis. However, these signals progressively converge toward baseline by the 12-year mark. When benchmarked against the current literature, our framework outperformed existing baselines for the diagnosis of symptomatic Mild Cognitive Impairment (MCI). This demonstrates its robustness in the more challenging task of preclinical prediction. Consequently, it establishes a viable pathway for integrating retinal imaging into the early diagnostic pipeline for Alzheimer’s disease.
