Related Experiment Video
Updated: Aug 6, 2026
Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
Published on: February 23, 2024
Automated forensic human identification on dental panoramic radiographs using transformer-based detection with
Hye-Ran Choi1, Sang-Heon Lim2, Ji Yong Han2
1Department of Advanced General Dentistry, Inje University Sanggye Paik Hospital, Seoul, 01757, Republic of Korea.
Background:
Dental radiographs are a primary modality for human identification in disaster victim identification, when fingerprint and visual recognition are not feasible. As antemortem (AM) databases grow, exhaustive AM-postmortem (PM) comparisons by expert visual inspection scale poorly to large reference databases. An automated method that pre-screens an AM database into a ranked shortlist of candidates for expert verification would address this bottleneck.
Methods:
The framework integrates a Detection Transformer (DETR) with a Circle Loss objective. DETR detects individual teeth and outputs 256-dimensional embeddings from the detection queries. Circle Loss then optimizes these embeddings to minimize same-individual pair distances and maximize different-individual pair distances. Evaluated on 1,029 individuals with paired AM-PM DPRs, identification performance was assessed by deriving the retrieval indices including Rank-K accuracy, mean average precision (mAP), and normalized discounted cumulative gain (nDCG). This detection-based approach was benchmarked against representative convolutional neural network (CNN)-based detectors, including Faster Region-based CNN (Faster R-CNN), RetinaNet, and You Only Look Once (YOLOv9), as well as image-level representations that bypass tooth localization.
Results:
The framework achieved 65.4% Rank-1 and 87.4% Rank-10 accuracy, 72.8% mAP, and 75.3% nDCG, consistently outperforming CNN-based detection architectures. Detection-based structural representations improved Rank-1 accuracy by 44.4% over image-level counterparts, with embeddings capturing fine-grained anatomical details such as root curvature and arch configuration.
Conclusions:
The proposed detection-based metric learning framework provides a practical pre-screening tool that compresses exhaustive AM-PM database comparisons into a ranked shortlist of candidates for forensic expert verification. The system is intended to operate upstream of expert verification rather than to replace in disaster victim identification and missing person investigations.
