ArticleBMC oral health2026
Evaluation of an AI-based segmentation model for mandibular fracture segmentation on CBCT using volumetric and 2D slice-based validation approaches.
Article in BMC oral health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis 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. MATERIALS AND
methodsCBCT 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.
resultsVolumetric (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.
conclusionThe 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.