ArticleInternational journal of legal medicine2026
A comparative analysis of YOLOv8 and nnU-Net v2 based pipelines for sex and age estimation from maxillary sinus morphometry on panoramic radiographs.
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. Cited by 1 paper.
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1 citing paper in PubMed.
- Whole-axis versus odontoid process CBCT radiomics for forensic sex estimation: a retrospective paired-ROI model-development study.International journal of legal medicine · 2026Article
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Abstract
objectiveThis study aimed to develop and compare two deep learning-based segmentation-radiomics pipelines - YOLOv8-Hybrid and nnU-Net v2 - for automated sex classification and age estimation from maxillary sinus morphometry on panoramic radiographs.
methodsA balanced dataset of 1,024 panoramic radiographs (512 males, 512 females; age 18-81 years) was collected from Near East University, North Cyprus. Ground truth sinus annotations were generated by an expert oral radiologist and validated through dual-annotator inter-observer reliability assessment (ICC (2,1) = 0.94-0.97). The YOLOv8-Hybrid pipeline employed YOLOv8n-seg coarse segmentation, U-Net boundary refinement, > 120 morphometric and radiomic features, and CatBoost/XGBoost classifiers. The nnU-Net v2 pipeline used auto-configured 2D U-Net segmentation with identical feature extraction and XGBoost prediction. Both pipelines underwent 5-fold cross-validation with patient-level splitting, transfer learning, Bayesian hyperparameter optimization, and SHAP interpretability analysis.
resultsnnU-Net v2 achieved statistically significant superiority in sex classification (AUC = 0.927 [95% CI: 0.881-0.964]) over YOLOv8-CatBoost (AUC = 0.893 [0.841-0.938]; DeLong p = 0.024, Cohen's d = 0.48). Both pipelines demonstrated comparable age estimation performance (MAE ≈ 7.2 years). YOLOv8 showed exceptional consistency (mAP@50 = 98.19%, CV = 0.77%). SHAP analysis identified bilateral area difference as the most determinant feature (sex: 0.42, age: 0.51). External validation on 50 independent images confirmed model generalizability.
conclusionsThis study provides the first systematic comparison of YOLOv8 and nnU-Net v2 for forensic maxillary sinus analysis. nnU-Net v2 is recommended for precision-critical forensic reporting, while YOLOv8-Hybrid is suited for high-throughput screening. The > 120 radiomic/morphometric features establish a comprehensive framework for automated biological profiling.
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