aceA hybrid deep learning framework for early autism screening
Objectives Early diagnosis of autism spectrum disorder (ASD) remains a significant challenge due to the time-consuming and subjective nature of traditional diagnostic methods. This study proposes a reliability-oriented hybrid deep learning framework that provides a low-cost, scalable, non-invasive, and AI-assisted pre-screening tool for early ASD risk indication and referral support. Methods The proposed system integrates two independent deep learning architectures: (1) a ResNet18 model optimized with 10-fold cr…
A reliability-oriented hybrid framework combining a periocular ResNet18 and a facial ensemble of ResNet50, EfficientNet-B0 and DenseNet121 increased early ASD risk indication to 90% sensitivity in the periocular pathway, 87.1% sensitivity with 0.948 AUC in the facial pathway, and an analytically estimated 98.71% system
System is positioned only as pre-screening and referral support, not a replacement for clinical evaluation, and the 0.9871 system sensitivity is an analytical estimate under a conditional-independence assumption rather than a directly measured clinical outcome.
Evidence
- Peer-reviewedPrimary Health Care Research & Development2026-09-15
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Truvace Impact Record TRV-2026-1111, v1: “A hybrid deep learning framework for early autism screening.” Truvace, 2026-09-16. /record/TRV-2026-1111 (accessed at citation time). sha256 20b741fa2ffb8f56…
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