aceThaCoder: an explainable deep learning framework for thalassemia screening using routine complete blood count: a multi-center validation study
Thalassemia remains a global public health challenge, yet definitive diagnosis via genetic testing is often restricted by high costs and infrastructure requirements. We aimed to develop and externally evaluate an interpretable deep-learning framework using routine complete blood count (CBC) parameters for thalassemia screening. Using 2317 CBC records from the internal derivation cohort, we implemented a two-stage analytical architecture. First, exploratory factor analysis (EFA) with Varimax rotation reduced nine…
A deep neural network using routine blood count values separated likely thalassemia cases from others in four external centers, and a simulation suggests it could cut genetic-testing referrals by about 76%.
The same model detected only about half of silent α-thalassemia carriers, so milder carriers are missed at the chosen threshold.
Sensitivity for silent α-thalassemia carriers was only 0.5270, the cost estimate comes from a preliminary simulation, and absolute risk estimates need site-specific recalibration.
Evidence
- Peer-reviewedClinica Chimica Acta2026-10-17
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Truvace Impact Record TRV-2026-1959, v1: “ThaCoder: an explainable deep learning framework for thalassemia screening using routine complete blood count: a multi-center validation study.” Truvace, 2026-10-20. /record/TRV-2026-1959 (accessed at citation time). sha256 b2a1a59c1ba812e6…
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