A Review of Optimization Strategies for Deep and Machine Learning in Diabetic Macular Edema

A. M. Mutawa^1*Khalid Sabtib²Bibin Shalini Sundaram Thankaleela³Seemant Raizadab²

• ¹Kuwait University, Kuwait City, Kuwait
• ²Kuwait University Faculty of Medicine, Safat, Kuwait
• ³Kuwait University, Safat, Kuwait

Diabetic Macular Edema (DME) is a primary contributor to visual impairment in diabetic patients, necessitating precise and prompt analysis for optimal treatment. Recent breakthroughs in deep learning and machine learning have yielded promising outcomes in ophthalmic image analysis. However, researchers often overlook the significance of optimization algorithms in enhancing the efficacy of their models for DME-related tasks. This review aims to consolidate, seek, discover, assess, and integrate existing work on the application of deep learning and machine learning, with emphasis on the integration and impact of optimization algorithms in enhancing their efficacy, robustness, and performance for DME in the fields of computer science and engineering. The Population, Intervention, Comparison, and Outcome (PICO) framework was employed in this study to facilitate a clear and comprehensive analysis. The procedural superiority of the included investigations was evaluated using the Joanna Briggs Institute Critical Appraisal Tools for assessing methodological quality. The Auto-Metric Graph Neural Network achieved the greatest accuracy of 99.57% for combined DR-DME grading, illustrating the higher efficacy of hybrid architectures augmented by meta-heuristic optimizers, such as Jaya, ACO. Successful deployment, however, depends on overcoming hurdles, such as the low mean Average Precision (mAP) in lesion identification, which is 0.1540 in YOLO-based models in test set performance, and improved clinical interpretability to foster clinician trust. A Sankey diagram visually analyzes the flow of quantities between different entities of the survey.