Work

AI‐Augmented Hematological Signatures for Equitable Detection of Hereditary Hemolytic Anemia Carriers: A Global Systematic Review and Meta‐Analysis

Year: 2026

Type: review

Study designs: Systematic Review, Meta-Analysis

Abstract: Artificial intelligence (AI) augmentation of routine hematological tests offers a promising strategy to improve hereditary hemolytic anemia (HHA) carrier detection in premarital screening, especially in resource‐limited settings. HHA in this review specifically encompasses β ‐thalassemia, α ‐thalassemia, sickle cell disease (including HbS and HbC variants), and other hemoglobinopathies with autosomal recessive inheritance patterns requiring carrier detection for prevention. These conditions share hemolytic phenotypes but differ in hematological signatures, necessitating separate subgroup analyses. This global systematic review and meta‐analysis evaluated the diagnostic accuracy, equity implications, and implementation challenges of AI‐augmented complete blood count (CBC), blood smear, and erythrocyte sedimentation rate (ESR) for HHA carrier identification. We systematically searched seven databases and included 85 studies ( n = 133,498 participants, 23 countries). AI‐augmented screening achieved a pooled sensitivity of 92.8% (95% CI: 91.3%–94.1%) and specificity of 91.5% (89.7%–93.0%), representing a 12.3% sensitivity improvement over conventional interpretation ( p < 0.001). However, significant geographic disparities were observed: sensitivity in Sub‐Saharan Africa was 86.5% compared with 94.8% in the Middle East ( p < 0.001), partly due to algorithmic bias against African HbS/HbC variants and infrastructural barriers. Deep learning models achieved the highest sensitivity (95.1%), whereas explainable artificial intelligence (XAI) provided optimal specificity (94.3%). Integrating CBC with blood smear increased specificity by 5.5% at minimal additional cost. AI triage reduced confirmatory testing by 23.7%, saving $8.50 per individual. For equitable implementation, we recommend the following: (1) federated learning to include underrepresented genotypes, (2) WHO/CDC certification of affordable, offline‐capable edge AI devices, and (3) mandatory XAI compliance with bias audits. AI can transform HHA screening, but deliberate efforts are needed to avoid exacerbating global health inequities. Importantly, 68% of validation studies used research‐grade rather than routine clinical samples, and prospective clinic‐to‐algorithm validation remains a critical gap requiring urgent attention before real‐world deployment.

Source: Human Mutation

DOI: 10.1155/humu/9405486