The role of artificial intelligence in diabetic retinopathy screening in type 1 diabetes: A systematic review

Abstract

Background/objectives: Diabetic retinopathy (DR) is one of the leading causes of blindness in adults worldwide and represents a critical complication in both type 1 (T1D) and type 2 (T2D) diabetes. Artificial Intelligence (AI) offers a promising opportunity to enhance both the accuracy of screening and the efficiency of ongoing care management, assisting healthcare providers in mitigating the incidence and complications of DR.

Methods: Systematic review of the literature was conducted following PRISMA guidelines. Searches were performed using PubMed-Medline, Scopus, and Embase databases, with the protocol registered on the Open Science Framework (OSF) database: (doi.org/10.17605/OSF.IO/TJ9UH). A predefined search strategy utilizing Boolean operators was applied, and two researchers independently selected articles, with a third resolving any discrepancies.

Results: Of the 2127 articles identified, 8 studies were included. The results highlighted that AI is particularly effective in enhancing the DR screening process in patients with T1D, offering rapid and reliable analysis. Healthcare providers reported positive feedback, noting its significant contribution to improving patient management.

Conclusions: The integration of AI into DR care pathways shows substantial potential for improving early diagnosis and disease management, particularly for patients with T1D. Further research is required to optimize AI implementation and ensure its positive and sustainable impact on public health.

Keywords: Artificial intelligence; Diabetic retinopathy screening; Health technology assessment; Public health; Type 1 diabetes.