ArticleBMC oral health2026
A customised 3D U-Net model for bifid mandibular canal segmentation and detection on CBCT: a diagnostic accuracy study.
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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBifid mandibular canals (BMCs) represent a clinically significant anatomical variation that increases the risk of iatrogenic inferior alveolar nerve injury during oral and maxillofacial procedures. Accurate identification of BMCs on cone-beam computed tomography (CBCT) remains challenging due to their subtle morphology and reliance on observer expertise. This study aimed to evaluate the diagnostic performance of a customised deep learning–based three-dimensional (3D) U-Net model for the automated delineation and detection of BMCs on CBCT volumes.
methodsA total of 208 CBCT volumes acquired using the Planmeca ProMax 3D MID system (Planmeca Oy, Helsinki, Finland) were retrospectively selected. The dataset was stratified into a training set, intentionally enriched with anatomical variants, and an independent test set. Ground truth annotations were established through manual voxel-wise segmentation by experienced oral and maxillofacial radiologists. A customised 3D U-Net architecture was utilised via the MONAI framework with five-fold cross-validation. Model performance was evaluated on the independent test set using voxel-wise metrics, including the Dice Similarity Coefficient (DSC) and Intersection-over-Union (IoU), as well as case-level detection metrics (sensitivity and specificity).
resultsThe model achieved mean DSC values of 0.91 for standard mandibular canals and 0.77 for bifid variants. On the independent test set, enriched with complex morphological subtypes, the model demonstrated 100% sensitivity, 81.8% specificity, and 90.5% overall diagnostic accuracy for detecting BMCs.
conclusionsThis study demonstrates that a customised 3D U-Net framework can accurately identify BMCs on CBCT volumes within a single-centre setting. When used as a decision-support tool, the proposed approach has the potential to assist radiologists in recognising anatomically complex canal variants. Further multicentre validation is required to confirm broader clinical applicability.
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.