A scoping review of the use of artificial intelligence models in automated OCT analysis and prediction of treatment outcomes in diabetic macular oedema - Mohaimen Al-Zubaidy; Agnieszka Stankiewicz; Matthew Anderson; Jordan Reed; Veronica Corona; Rebecca Pope; Boguslaw Obara; Maged S. Habib; David H. Steel (Informatics in Medicine Unlocked, 2025)
Objective: This review aims to identify gaps and provide direction for future research examining the use of artificial intelligence (AI) and optical coherence tomography (OCT) in the investigation and management of diabetic macular oedema (DMO). Methods: A comprehensive literature search was conducted using MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials (CENTRAL), the Cochrane Database, and the Web of Science. The search focused on AI applications in DMO diagnosis, grading, and outcome prediction, and adhered to a predefined protocol following the Cochrane Methodology for Scoping Reviews. Results: Following screening 40 studies were included for review. The review highlighted significant advancements in the use of AI for DMO, particularly in diagnosis and biomarker detection. AI models demonstrated high accuracy in distinguishing DMO from other retinal conditions and in segmenting key DMO biomarkers. Conclusion: The review concludes that future research should focus on developing robust prognostic and treatment prediction models, improving external validation and standardising performance metrics. Addressing these challenges is essential for optimising the integration of AI into DMO management, ultimately improving patient outcomes and reducing vision impairment. Significance: This review underscores AI's potential to transform DMO management, a leading cause of vision impairment in diabetes. The identified gaps and future research directions offer valuable insights for researchers and practitioners, with the potential to significantly improve patient care and healthcare efficiency.