A BMC-Net model for the recognition and segmentation of mandibular canal bifurcation.

Journal: Oral radiology
Published Date: Jan 6, 2026

Abstract

OBJECTIVES: To accurately identify the anatomical structure and trajectory of the mandibular canal, helping dentists avoid surgical risks and develop effective treatment plans, this study aimed to develop a deep learning model for rapidly diagnosing and identifying mandibular canal bifurcation. MATERIALS AND METHODS: We collected a total of 160 reported case images from the PubMed and Web of Science databases. Of these, 140 images were allocated to a training set and 20 to a testing set, to develop the BMC-Net deep learning model. Performance was evaluated using the dice similarity coefficient (DSC), area under the curve (AUC), intersection over union (IoU), recall, precision, and a confusion matrix, with comparisons made against the UNet model. To determine clinical utility, a comparative analysis with clinicians was performed, focusing on AUC, sensitivity, specificity, and time efficiency. RESULTS: The BMC-Net model achieved a DSC of 0.9704, AUC of 0.9458, IoU of 0.9412, recall of 0.9458, and precision of 0.9829 in the training set, with significant improvements in the testing set, where it reached a DSC of 0.9877, AUC of 0.9965, IoU of 0.9539, recall of 0.9569, and precision of 0.9976. Compared to clinicians, the BMC-Net model achieved an AUC of 0.9636, sensitivity of 0.9314, and specificity of 0.9461, and detected mandibular canal divergence in just 0.1004 s, significantly faster than the average clinician's recognition time of 95.5 to 164.75 s (P < 0.05). CONCLUSIONS: This groundbreaking model's utility markedly improves the accuracy of clinical diagnoses on mandibular nerve canal bifurcations.

Authors

  • Kamenjiang Abudoureheman
    School (Hospital) of Stomatology, Lanzhou University, Lanzhou, China.
  • Fan Du
    Division of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Zhidong Zhang
    School of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei 230026, China.
  • Bo Li
    Electric Power Research Institute, Yunnan Power Grid Co., Ltd., Kunming, Yunnan, China.
  • Jinxian Wei
    School (Hospital) of Stomatology, Lanzhou University, Lanzhou, 730000, China.
  • Gang Lu
    Innovation Research Institute of Combined Acupuncture and Medicine, Shaanxi University of CM, Xianyang 712046, China; Shaanxi Key Laboratory of Combined Acupuncture and Medicine, Xianyang 712046.
  • Chao Xie
    Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
  • Jizu Ling
    Department of Pediatric Medicine, The First Hospital of Lanzhou University, Lanzhou, 730000, China. [email protected].
  • Jingxiang Zhang
    Centre for Digital Transformation, School of Computing and Information Technology, Faculty of Engineering and Information Sciences, University of Wollongong, Australia.
  • Baoping Zhang
    Department of Anesthesiology and Operation, The First Hospital of Lanzhou University, Lanzhou, Gansu, China.

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