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TRV-2026-0715Certified recordPeer-reviewed

First-stage assessment of AI-based anatomical measurement accuracy for the Cameriere European dental age estimation method

Aim This study represents the first methodological stage of a broader AI-assisted Cameriere European dental age estimation workflow. The aim of this first stage was to evaluate the performance of YOLOv8-based deep learning models in automatically detecting the anatomical landmarks and apical structures required for the Cameriere European method. Rather than directly estimating dental age, the proposed system was designed to automate the measurement-related inputs needed for subsequent Cameriere European-based ag…

Health · G Space — documented gain · certified 2026-08-09 · v1 · article view · machine-readable

Current reading — gain

YOLOv8x-pose and YOLOv8x-seg models automated landmark detection and apical segmentation for the Cameriere European method in children aged 5-13, achieving high detection accuracy and low measurement error in a first-stage validation.

What this doesn’t fix

Findings represent measurement-level validation only; dental age was not calculated and AI-derived measurements have not yet been integrated into the Cameriere European formula or compared to chronological age.

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Truvace Impact Record TRV-2026-0715, v1: “First-stage assessment of AI-based anatomical measurement accuracy for the Cameriere European dental age estimation method.” Truvace, 2026-08-09. /record/TRV-2026-0715 (accessed at citation time). sha256 56ddf6e7c7a0e6ea…

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