Evidence map›Paper›PMID 41493698›Full record

ArticleOral radiology2026

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

Kamenjiang Abudoureheman, Fan Du, Zhidong Zhang, Bo Li, Jinxian Wei, Gang Lu, Chao Xie, Jizu Ling, Jingxiang Zhang, Baoping Zhang

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Article in Oral radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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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.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Kamenjiang Abudoureheman *School (Hospital) of Stomatology, Lanzhou University, Lanzhou, 730000, China.
Fan Du *School (Hospital) of Stomatology, Lanzhou University, Lanzhou, 730000, China.
Zhidong Zhang *School (Hospital) of Stomatology, Lanzhou University, Lanzhou, 730000, China.
Bo LiState Key Laboratory of Military Stomatology & National Clinical Research Center for Oral Diseases & Shanxi Key Laboratory of Stomatology, School of Stomatology, The Fourth Military Medical University, Xi'an, 710032, China.
Jinxian WeiSchool (Hospital) of Stomatology, Lanzhou University, Lanzhou, 730000, China.
Gang LuCollege of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730000, China.
Chao XieState Key Laboratory of Military Stomatology & National Clinical Research Center for Oral Diseases & Shanxi Key Laboratory of Stomatology, School of Stomatology, The Fourth Military Medical University, Xi'an, 710032, China. xchy2006@fmmu.edu.cn.
Jizu LingDepartment of Pediatric Medicine, The First Hospital of Lanzhou University, Lanzhou, 730000, China. lingjizu@126.com.
Jingxiang ZhangSchool (Hospital) of Stomatology, Lanzhou University, Lanzhou, 730000, China. zhangjingxiang@lzu.edu.cn.
Baoping ZhangSchool (Hospital) of Stomatology, Lanzhou University, Lanzhou, 730000, China. zhangbp@lzu.edu.cn.

Funding

the Key Research Topics of the Clinical Research at the National Clinical Research Center for Oral Diseases LCA202402the Research Program of Gansu Province Health and Wellness Industry GSWSKY2023-19
6 · The paper itself

Abstract

objectivesTo 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

methodsWe 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.

resultsThe 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).

conclusionsThis groundbreaking model's utility markedly improves the accuracy of clinical diagnoses on mandibular nerve canal bifurcations.

Indexed as

Deep LearningMandibular CanalHumansSensitivity and SpecificityDeep learningImage recognitionMandibular canal bifurcationRetromolar canal

Identifiers

PMID41493698

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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.