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TRV-2026-1051Certified recordPeer-reviewed

Artificial intelligence -based modeling and comparative evaluation of craniofacial soft tissue and subcutaneous fat thickness using ct imaging for forensic identification in a northwestern indian population

Background Craniofacial soft tissue thickness (CFSTT) is a critical parameter in forensic facial reconstruction, serving as a link between skeletal morphology and facial appearance. However, most existing studies rely on mean CFSTT values without considering the contribution of subcutaneous fat layer thickness, limiting the accuracy of reconstruction models. Objective This study aims to develop an artificial intelligence (AI)-assisted, CT-based framework for the comparative evaluation of craniofacial soft tissue…

Science · P Space — documented harm · certified 2026-09-10 · v1 · article view · machine-readable

Current reading — problem

AI models for craniofacial soft tissue prediction lack independent external validation, leaving generalizability beyond the study dataset unestablished.

What this doesn’t fix

Findings are population-specific to northwestern India and based on within-dataset performance without independent external validation, limiting generalizability to other populations or settings.

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Truvace Impact Record TRV-2026-1051, v1: “Artificial intelligence -based modeling and comparative evaluation of craniofacial soft tissue and subcutaneous fat thickness using ct imaging for forensic identification in a northwestern indian population.” Truvace, 2026-09-10. /record/TRV-2026-1051 (accessed at citation time). sha256 5a842b8ae0a7328d…

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