Evidence map›Paper›PMID 41808048›Full record

ArticleBMC oral health2026

Root canal segmentation from cone-beam computed tomography guided by micro-computed tomography based on deep learning.

Xianhua Gao, Jingzhi Ma, Bo Li, Yimeng Fang, Lianting Hu, Min Zhou, Bing Fan

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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.

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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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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Xianhua GaoState Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, 237 Luoyu Road, Wuhan, 430079, China.
Jingzhi MaDepartment of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Bo LiState Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, 237 Luoyu Road, Wuhan, 430079, China.
Yimeng FangThe Data Center, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan city, Hubei Province, 430016, China.
Lianting HuThe Data Center, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan city, Hubei Province, 430016, China. liantinghu@whu.edu.cn.
Min ZhouDepartment of Industrial and Systems Engineering, Wuhan University of Science and Technology, Wuhan City, Hubei Province, China. zhoumin@wust.edu.cn.
Bing FanState Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, 237 Luoyu Road, Wuhan, 430079, China. bingfan@whu.edu.cn.

Funding

National Natural Science Foundation of China Grant No. 81771067the Basic and Applied Basic Research Foundation of Guangdong Province Grant No. 2022A1515110722the Guangdong Provincial Medical Science and Technology Research Fund Project Grant No. A2023011the Key Research and Development Project of Hubei Province of China Grant No. 2022BCA033
6 · The paper itself

Abstract

backgroundAccurate root canal segmentation from cone-beam computed tomography (CBCT) is essential for endodontic diagnosis and treatment planning. This study aims to explore the feasibility of using deep learning (DL) models, trained on CBCT images of extracted teeth guided by micro-computed tomography (µCT), for clinical CBCT image segmentation.

methodsA dataset of 56 extracted teeth with diverse root canal complexities was constructed, combining CBCT and µCT scans. Ground truth annotations were derived from µCT-based masks and registered to CBCT images. DL models based on U2-Net architecture were trained and evaluated for tooth and root canal segmentation, comparing µCT-guided and manual-label-based approaches. We further investigated the impact of voxel size and image resampling on performance. Finally, the trained models were applied to segment root canals in clinical CBCT images, with the results validated by endodontic specialists.

resultsThe µCT-guided AI segmentation method outperformed the manual-label-based approach. Utilizing a smaller native voxel size (80 μm), coupled with image resampling, proved particularly advantageous for capturing intricate anatomical details. In clinical validation, the model delivered rapid and accurate root canal segmentation, with 94% of single-rooted teeth and 100% of molars rated as “excellent” or “good”.

conclusionsResults demonstrated the potential of µCT-guided AI models for enhancing root canal segmentation in clinical practice, offering a promising tool for digital dentistry. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Cone-Beam Computed TomographyDeep LearningDental Pulp CavityImage Processing, Computer-AssistedX-Ray MicrotomographyHumansAI segmentation modeCone-beam computed tomographyDeep learningMicro-computed tomographyRoot canal segmentation

Identifiers

PMID41808048
PMCPMC13088830

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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.