Evaluating the Diagnostic Performance of AI and Machine Learning in Sickle Cell Disease Detection: A Systematic Review.

Authors

Najat Elamin Abdelgader Adam, Omer Kamal Ahmed Omer, Fayyas Ahamed, Mohammed Elzaki Mohammed Mansoor, Sara Mohamed Abuelgasim Hassan, Alaa Kamal Mohamed Abdalla, Safaa Mohammed Abdelghaffar Osman

Published in

Cureus. Volume 18. Issue 5. Pages e109880. Epub May 29, 2026.

Abstract

Sickle cell disease (SCD) is a major global health burden, and early, accurate diagnosis is critical for effective management. Conventional diagnostic methods are often resource-intensive and inaccessible in high-burden, low-resource settings. Artificial intelligence (AI) and machine learning (ML) technologies have emerged as promising tools to automate and enhance SCD detection. This systematic review aimed to critically evaluate the diagnostic and predictive performance of AI and ML models for SCD detection and to assess their methodological quality and readiness for clinical implementation. A systematic search of PubMed, Web of Science, Scopus, and Embase was conducted for studies published between 2021 and 2025, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Original research employing AI/ML models for SCD detection, classification, severity stratification, or outcome prediction was included. Data on study characteristics, model types, and diagnostic performance metrics were extracted. The risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). A narrative synthesis was performed due to substantial methodological heterogeneity precluding meta-analysis. Seventeen studies were included, demonstrating a diverse landscape of model architectures, including deep learning (DL) for blood smear image analysis, ensemble methods for classification, and prognostic models for pain and mortality prediction. Diagnostic performance was consistently high, with accuracies frequently exceeding 94% for image-based SCD detection and area under the receiver operating characteristic curve (AUC-ROC) values reaching up to 0.99 for ensemble classifiers. Prognostic models for mortality and readmission achieved C-indices and AUCs of 0.76 and 0.77, respectively. PROBAST assessment revealed that a majority of studies (14 of 17) had a low overall risk of bias, while three studies were rated as high risk due to small sample sizes and methodological reporting limitations. AI and ML models demonstrate substantial diagnostic accuracy and promising prognostic capability in SCD. However, the field remains at a proof-of-concept stage, with a predominant reliance on internal validation and a lack of standardized reporting that hinders direct model comparison. For these technologies to achieve clinical impact, a rigorous paradigm shift toward prospective, externally validated studies in high-burden populations, alongside strict adherence to emerging reporting standards, is essential.

PMID:
42371462
Bibliographic data and abstract were imported from PubMed on 29 Jun 2026.

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