Evaluation of an AI-based segmentation model for mandibular fracture segmentation on CBCT using volumetric and 2D slice-based validation approaches.

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Abstract

This study aimed to evaluate the diagnostic performance of an artificial intelligence (AI)-based segmentation model for mandibular fracture segmentation on cone-beam computed tomography (CBCT) scans using both volumetric (3D) and 2D slice-based validation approaches. CBCT data from 92 patients, comprising 142 mandibular fracture sites, were retrospectively collected. Ground truth annotations were established through manual segmentation by two oral and maxillofacial surgeons. The AI model was developed using the nnU-Net v2 architecture in 3D full-resolution mode. The dataset was divided into training (90%) and test (10%) subsets. Model performance was evaluated by comparing AI-generated segmentations with expert annotations using accuracy, Dice similarity coefficient (DICE), Jaccard index, precision, recall, average surface distance (ASD), and the 95% Hausdorff distance. These metrics were calculated for both volumetric (3D) and 2D slice-based validation approaches. Volumetric (3D) evaluation yielded a DICE of 0.605, a Jaccard index of 0.446, a precision of 0.61, and a recall of 0.663. In contrast, 2D slice-based validation resulted in a DICE of 0.492, a Jaccard index of 0.350, a precision of 0.60, and a recall of 0.484. The average surface distance was 4.88 mm for the 3D model and 7.85 mm for the 2D model, while the 95% Hausdorff distance was 16.48 mm and 22.04 mm, respectively. The AI-based segmentation model demonstrated a moderate level of performance in segmenting mandibular fracture regions on CBCT scans. Although overall segmentation accuracy was limited, the volumetric (3D) validation approach consistently outperformed the 2D slice-based method. These findings suggest that volumetric evaluation provides a more comprehensive and reliable framework for assessing AI-assisted mandibular fracture segmentation on CBCT images.

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