# Artificial Intelligence Applications in Sickle Cell Retinopathy Imaging: Current Progress, Challenges, and Future Directions

## TL;DR

This paper reviews how artificial intelligence is being used to detect and monitor sickle cell retinopathy, a cause of vision loss in sickle cell disease patients.

## Contribution

The paper provides a systematic review of AI applications in SCR imaging, highlighting current progress and future clinical translation opportunities.

## Key findings

- Four studies used deep learning to detect SCR features in ophthalmological images.
- Two studies applied classical machine learning to classify SCR based on imaging data.
- AI tools could improve SCR detection and monitoring, leading to better patient outcomes.

## Abstract

Sickle cell retinopathy (SCR) is a leading cause of vision loss in patients with sickle cell disease, but its detection and monitoring remain difficult due to heterogeneous retinal microvascular changes and reliance on expert interpretation. Artificial intelligence has shown success in retinal disease detection, classification, and staging on retinal images, achieving expert‐level performance. This review summarizes recent artificial intelligence progress in SCR imaging and highlights future opportunities for clinical translation.

This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines. A comprehensive literature search was performed in PubMed/MEDLINE, Embase, and Web of Science.

A comprehensive PubMed search returned 15 records. After removal of duplicates and screening of titles and abstracts, 7 full‐text articles were assessed for eligibility. Of these, 4 studies met inclusion criteria and were included in the final review. Three full text articles were assessed for eligibility and inclusion from non‐PubMed sources. Two studies applied classical machine learning on features extracted from imaging data to classify SCR. Four studies used deep‐learning algorithms to detect SCR features on ophthalmological images. One study applied deep learning algorithms to classify SCR from other retinal diseases.

Deep learning has the potential to improve detection, stage, and monitor sickle cell retinopathy across multiple ophthalmological imaging methods. Additional research is needed to support clinical adoption. With continued development, AI‐based tools could enhance diagnostic precision, enable personalized care, and ultimately improve outcomes for patients with sickle cell retinopathy.