AI-assisted decision support for sickle cell disease severity stratification using routine blood tests: a systematic review and meta-analysis.

BACKGROUND: Stratifying sickle cell disease (SCD) severity remains challenging, particularly in resource-limited settings. Artificial intelligence (AI) models using routine complete blood count (CBC) parameters have been proposed as accessible tools for risk stratification; however, their overall performance and clinical applicability remain uncertain. METHODS: We conducted a PRISMA-DTA 2020-compliant systematic review and meta-analysis (PROSPERO: CRD420251078389) including 60 studies (25,354 patients across 15 countries, 2010-2025). AI models based on CBC parameters were evaluated against heterogeneous reference standards, including clinical severity classifications and event-based outcomes. Risk of bias was assessed using QUADAS-2 and PROBAST-AI. Pooled estimates were generated using hierarchical summary receiver operating characteristic (HSROC) models. RESULTS: The pooled area under the curve (AUC) was 0.87 (95% CI: 0.84-0.90), with sensitivity 0.83 (0.79-0.86) and specificity 0.85 (0.81-0.88). Performance varied by setting, with higher accuracy in high-income countries (AUC 0.90) compared with low- and middle-income settings (AUC 0.82; p < 0.001). Red cell distribution width and platelet count were consistently identified as important predictors. However, substantial heterogeneity in outcome definitions and reference standards limits interpretability. CONCLUSIONS: AI models using routine CBC parameters demonstrate promising analytical performance for SCD risk stratification. However, the absence of a universally accepted severity gold standard, variability in outcome definitions, and differences across healthcare settings limit direct clinical applicability. These findings support cautious prospective validation and clinical utility assessment before consideration of broader real-world implementation. CLINICAL TRIAL NUMBER: Not applicable.