ArticleInternational journal of legal medicine2026
First-stage assessment of AI-based anatomical measurement accuracy for the Cameriere European dental age estimation method.
Article in International journal of legal medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
aimThis 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 age calculation.
methodsThis retrospective first-stage validation study included 4,050 panoramic radiographs of boys and girls aged 5-13 years. Two YOLOv8-based models were developed to automate the anatomical measurement components of the Cameriere European method: a YOLOv8x-pose model for open-apex landmark detection and tooth-length reference point localization, and a YOLOv8x-seg model for closed-apex segmentation. Owing to model-specific anatomical eligibility criteria, 3,796 images were used for the pose model and 2,971 images for the segmentation model. Image annotations were performed using CranioCatch software according to a standardized annotation protocol. Model outputs were compared with manual reference annotations to evaluate landmark detection, measurement-related localization, and apical segmentation performance.
resultsIn the YOLOv8x-pose model, mAP_0.5 = 0.963 and mAP_0.5:0.95 = 0.842 were achieved; recall was 0.928 and precision was 0.918. Error metrics were MAE = 0.0032, RMSE = 0.0045, SMAPE = 2.09%, and the coefficient of determination R² = 0.9992.
conclusionYOLOv8-based pose and segmentation models demonstrated technical feasibility for automating anatomical measurement extraction required for the Cameriere European dental age estimation method. Because dental age was not calculated in this first-stage analysis, the findings should be interpreted as measurement-level validation rather than complete dental age-estimation accuracy. Further validation is required to integrate AI-derived measurements into the Cameriere European formula and to compare AI-assisted dental age estimates with manual Cameriere-based assessment and chronological age.
Indexed as
Identifiers
42567969What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.