# A review of optimization strategies for deep and machine learning in diabetic macular edema

## TL;DR

This paper reviews optimization strategies in deep and machine learning for diabetic macular edema, highlighting their impact on model performance and challenges in clinical deployment.

## Contribution

The paper introduces a comprehensive review of optimization algorithms' role in improving DL/ML efficacy for DME diagnosis.

## Key findings

- Hybrid architectures with meta-heuristic optimizers like Jaya achieved 99.57% accuracy in DME grading.
- YOLO-based models showed low mean average precision (0.1540) in lesion identification.
- A Sankey diagram is used to visualize data flow in the survey.

## Abstract

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 (DL) and machine learning (ML) 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 DL and ML, 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 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 diabetic retinopathy-DME grading, illustrating the higher efficacy of hybrid architectures augmented by meta-heuristic optimizers, such as Jaya and ant colony optimization. Successful deployment, however, depends on overcoming hurdles, such as the low mean average precision in lesion identification (0.1540) in YOLO-based models in the 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.

B. (2025, November 2). A Review of Optimization Strategies for Deep and Machine Learning in DME. Retrieved from osf.io/qsh4j.