Semi-automated sex estimation from the cochlea on routine clinical CT: limited incremental value of curvature-based shape descriptors

Background Sex estimation is fundamental in forensic anthropology and bioarchaeology, yet classical methods rely on skeletal elements that are often absent or poorly preserved. The bony labyrinth, enclosed within the petrous bone, is a promising alternative owing to its early maturation and taphonomic resistance, and cochlear shape carries a sex-specific signal. However, most approaches depend on manual segmentation, micro-CT, or complex shape-analysis frameworks that limit routine applicability. Objective To evaluate the feasibility of a semi-automated method for sex estimation from cochlear shape using standard clinical CT and automated OTOPLAN segmentation. Materials and methods We analyzed 139 cochleae from 87 adults using clinical CT scans (slice thickness ≤ 0.6 mm). The three-dimensional lateral wall path was extracted with OTOPLAN; after normalization and resampling, curvature- and torsion-based descriptors and scalar cochlear metrics were computed to train a supervised classifier. Preprocessing, feature, and hyperparameter choices were jointly optimized by Bayesian optimization on the training set. Results On an independent holdout set (n = 29), the XGBoost model reached an area under the ROC curve (AUC-ROC) of 0.77 (95% CI 0.55–0.95), with 79% overall and balanced accuracy. Cochlear diameter was the most influential feature, followed by the torsion-to-curvature (τ/κ) ratio across several spiral segments. Conclusion Sex-related cochlear shape information is recoverable from routine clinical CT using a semi-automated pipeline. Although accuracy remains moderate, the approach is non-destructive, scalable, and clinically compatible, offering a potential adjunct for forensic anthropology and bioarchaeology when conventional methods are unavailable.