A Tiered 3D CNN Framework for Forensic Sex Estimation from CBCT: Matched Comparison of Full-Skull Volumes and Mandible-Only Masks

Deep learning has advanced automated sex estimation from craniofacial imaging, but differences in cohorts, input representations, and protocols between studies leave the performance gap between full-skull and mandible-only inputs insufficiently quantified. We compared the two inputs under matched conditions using cone-beam computed tomography (CBCT) scans of 353 Chinese Han individuals (102 males and 251 females, aged 6-78 years). From each scan, we derived a full-skull grayscale volume (1283) and an automatically segmented mandibular mask (803). The same lightweight three-dimensional (3D) convolutional neural network (about 572,000 parameters) was trained on each input with identical five-fold cross-validation splits and training protocol. Balanced accuracy (BAcc) was 96.20% ± 3.66% for the full-skull input and 88.67% ± 4.58% for the mandible-only input (paired difference in pooled BAcc, 7.52 percentage points, 95% confidence interval (CI) 4.55-10.50). Mandible-only BAcc was 93.17% ± 2.63% at 19-50 years (n=259), whereas pooled BAcc was 66.67% at ≤17 years and 79.80% above 50 years. In this single-center cohort, the full-skull input performed better overall, and the mandible-only input achieved high accuracy only in adults aged 19-50 years. Applying these subgroup results requires an independent age assessment, and external validation is needed before forensic use.