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

Parim Shah¹, Hamza Ahmed Farah¹, Daniel J Wisotsky¹

¹Department of Radiology, Albert Einstein College of Medicine and Montefiore Health System, Bronx, New York, USA.

Journal of Ophthalmology

|February 23, 2026

Summary

Artificial intelligence (AI) shows promise in detecting and monitoring sickle cell retinopathy (SCR), a major cause of vision loss. AI tools can improve diagnostic accuracy and patient outcomes, but further research is needed for widespread clinical use.

Keywords:

AI convolutional neural networks (CNNs)deep learning fluorescein angiography fundus photography optical coherence tomography retinal imaging sickle cell disease

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Sickle cell retinopathy (SCR) is a primary cause of vision loss in sickle cell disease patients.
Current SCR detection and monitoring face challenges due to complex retinal changes and reliance on expert interpretation.
Artificial intelligence (AI) has demonstrated expert-level performance in analyzing retinal images for various eye conditions.

Purpose of the Study:

To review recent advancements in AI applications for sickle cell retinopathy imaging.
To highlight the potential of AI in improving SCR diagnosis, staging, and monitoring.
To identify future opportunities for the clinical translation of AI-based tools in SCR management.

Main Methods:

Systematic literature review adhering to PRISMA guidelines.
Comprehensive searches conducted in PubMed/MEDLINE, Embase, and Web of Science databases.
Analysis of studies employing classical machine learning and deep learning algorithms for SCR detection and classification from ophthalmological images.

Main Results:

Four studies met the inclusion criteria for the review.
Two studies utilized classical machine learning for SCR classification based on extracted imaging features.
Four studies employed deep learning algorithms for detecting SCR features in ophthalmological images, with one study differentiating SCR from other retinal diseases.

Conclusions:

Deep learning holds significant potential for enhancing SCR detection, staging, and monitoring across various imaging modalities.
Further research is essential to facilitate the clinical adoption of AI-driven SCR diagnostic tools.
AI-based solutions can potentially improve diagnostic precision, personalize patient care, and enhance outcomes for individuals with sickle cell retinopathy.