Technical Note: Decision Tree Approach to Standardizing Scoring of Dental Restorations in PMCT Images: A pilot study

In cases of decomposition, carbonization, or drowning of bodies, dental comparison is key for forensic identification. To this end, post-mortem computed tomography (PMCT), a non-invasive and reproducible technique, enables detailed visualization of dental structures. However, metallic artefacts and density saturation may impair image quality. This study developed and validated a decision tree-based scoring scale to determine the most efficient PMCT filter and reconstruction modality for forensic dental analysis. PMCT scans from ten autopsies, each with an intraoral reference examination, were analysed. Six filters (Bone and Soft Tissue, with or without Iterative Metal Artifact Reduction (IMAR) and Extended Hounsfield Unit (EHU) filters) and four reconstruction modalities (Multiplanar Reconstruction (MPR) and Maximum Intensity Projection (MIP) at 2-, 5-, and 10-mm thickness) were compared. Each body's dental restoration was evaluated using a decision tree assigning an image quality score from 0 to 4. Inter- and intra-observer agreement was assessed using the weighted Cohen’s Kappa coefficient. Two hundred dental treatments were analysed. Intra-observer agreement was substantial to almost perfect across all filters (k > 0.61), highest for the EHU bone filter (k = 0.81). Inter-observer agreement ranged from fair (IMAR soft tissues, k= 0.33) to good (EHU soft tissue, k= 0.75). The best concordance was obtained for amalgam and composite restorations using EHU filters. The decision-tree scoring scale improved reproducibility and objectivity in PMCT interpretation. The EHU filter significantly enhanced dental image quality and could be integrated into forensic identification protocols, particularly in mass disaster contexts. • A decision-tree score improves evaluation of dental images on post-mortem CT • Observer agreement ranged from substantial to almost perfect (k > 0.61) • The Extended Hounsfield scale improves visualization of dental restorations • Method enhances reliability of forensic dental identification from CT data • Approach may support forensic identification in mass disaster scenarios