Ethmoid sinus CBCT imaging as a biometric instrument: dataset creation for deep learning identification

Internal cranial structures such as the sphenoid and ethmoid bones, along with their associated sinuses, provide valuable biometric information for human identification, particularly when conventional modalities such as fingerprints, dental records, or DNA are unavailable. Cone Beam Computed Tomography (CBCT) offers a non-invasive imaging modality to visualize these complex anatomical structures.

To construct an annotated CBCT dataset of the ethmoid bone and evalute its utility for deep learning-based gender classification.

A total of 565 CBCT scans (312 males and 253 females) with age range 6-74 years were collected. All proceedures were conducted under ethical approval and helsinki compliance. Expert radiologists annotated the ethmoid region across axial slices. A CNN-based model was trained as a proff-of-concept for gender classification.

The best- performing model (fine-tuned ResNet-50) achieved an F1-score of 87% demonstrating strong discriminative potential for ethmoid-based gender classification.

The dataset provides a reproduciable resource for forensic radiology and deep-learning research. Ethmoid CBCT iamging show promise as a biometric marker for identifying-related tasks.