ReviewJournal of ophthalmology2026
Artificial Intelligence Applications in Sickle Cell Retinopathy Imaging: Current Progress, Challenges, and Future Directions.
Review in Journal of ophthalmology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Artificial Intelligence Applications in Sickle Cell Retinopathy Imaging: Current Progress, Challenges, and Future Directions.Journal of ophthalmology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: 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. Materials and Methods: 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. Results: 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. Conclusions: 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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.