Veena Mayya

Accurate and early identification of ocular diseases is essential to prevent vision impairment and enable timely medical intervention. In routine clinical practice, ophthalmologists rely on a structured diagnostic workflow that incorporates multiple imaging modalities to manually assess and diagnose ocular diseases. However, interpreting each modality requires significant clinical experience and can be time-consuming. These limitations can be effectively addressed through the application of AI (Artificial intelligence)-driven multimodal fusion techniques. In this study, we conducted an empirical investigation to assess the impact of different fusion strategies—including early, intermediate, and late fusion—on diagnostic performance, training requirements, and interpretability. The proposed methodology was evaluated using three publicly available datasets: FFA-Fundus (Fundus fluorescein angiography), GAMMA (Glaucoma Analysis and Multi-Modal Assessment), and OLIVES (Ophthalmic Labels to Investigate Visual Eye Semantics). Experimental results demonstrate that multimodal feature fusion improves disease detection performance. Although fused models typically required an increase in training parameters compared to single-modality models, they provided interpretability on par with that of individual single-modal networks. However, inference time increased by approximately 50% for multimodal architectures. These findings underscore the value of integrating diverse ophthalmic imaging modalities to enhance diagnostic accuracy in automated disease detection systems. At the same time, the results highlight that unimodal models containing highly discriminative features can also perform competitively, particularly when a single modality is sufficient for disease identification. Multimodal fusion provides the greatest benefit in scenarios where complementary information across modalities contributes distinct and non-redundant features. Furthermore, fusing all available modalities may not be optimal due to increased computational cost and reduced inference efficiency; thus, selective modality integration and lightweight fusion strategies are essential to balance accuracy, interpretability, and efficiency in clinical deployment.

This comprehensive review investigates recent advances in deep learning-based methods for assessing the integrity of retinal layers—specifically the External Limiting Membrane (ELM) and Ellipsoid Zone (EZ)—in Optical Coherence Tomography (OCT) imaging. A structured search was conducted on PubMed, IEEE Xplore, ScienceDirect and Google Scholar to identify peer-reviewed studies published between 2015 and 2025 that applied deep learning for ELM/EZ segmentation or integrity assessment with quantifiable outcomes such as dice, intersection over union (IoU) or volume metrics. Forty-three eligible studies were synthesized, revealing the dominance of U-Net-based models, attention mechanisms, and emerging transformer architectures. The review highlights advances in model performance, dataset diversity, and clinical applicability while identifying persistent challenges in generalization, smoothness quantification, and real-time deployment. To address these gaps, we recommend standardized annotation protocols, publicly available benchmarks, multicenter and multidevice validation, and prospective studies linking automated ELM/EZ metrics with clinical outcomes. Clearer reporting of statistical uncertainty, improved model explainability, and optimized lightweight architectures for clinical use will be essential to translate automated ELM / EZ analysis into reliable and actionable tools for diagnosis, prognosis, and treatment monitoring.

Background: Periodontal disease (PD) is a primary contributor to tooth loss, which negatively affects oral functionality and quality of life. This research aims to investigate the effectiveness of various machine learning (ML) classifiers in identifying PD among U.S. adults. Method: Nineteen features, selected based on prior literature and expert dentist input, were preprocessed using feature engineering techniques. Eleven machine learning classifiers, including basic and ensemble models, were evaluated to identify the best performing model. The interpretability of the model was evaluated using Shapley additive explanations and individual conditional expectation plots to determine key predictors of periodontitis. Results: The predictive efficacy of the ML classifiers is assessed using metrics such as the area under the receiver operating curve (AUC), accuracy, sensitivity, and specificity. The CatBoost classifier performed best in identifying PD. It achieved an AUC of 84.5%, an accuracy of 75.8%, a precision of 75.8%, a sensitivity of 78.8%, and a specificity of 72.5%. Having an annual dentist visit and age emerged as the most influential variables. Conclusions: The ML models utilized in this study exhibited robust predictive performance and can be further improved by incorporating additional clinical parameters. The proposed models effectively identified individuals at high risk for developing PD.