AI model to detect contact relationship between maxillary sinus and posterior teeth.

Wanghui Ding, Yindi Jiang, Gaozhi Pang, Ziang Liu, Yuefan Wu, Jianhua Li, Fuli Wu

Abstract read

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Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

Deep Learning Models for Evaluating the Anatomical Relationship Between Posterior Maxillary Teeth and Maxillary Sinus in Panoramic Radiographs.Clinical and experimental dental research · 2025
Article

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5 · Who and what money

Authors and funding

7 authors.

Wanghui DingStomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China.

Yindi JiangHangzhou Linping Traditional Chinese Medicine Hospital, China.

Gaozhi PangCollege of Computer Science and Technology, Zhejiang University of Technology, China.

Ziang LiuStomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China.

Yuefan WuStomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China.

Jianhua LiHangzhou Dental Hospital, China.

Fuli WuCollege of Computer Science and Technology, Zhejiang University of Technology, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To establish a novel deep learning networks (MSF-MPTnet) based on panoramic radiographs (PRs) for automatic assessment of relationship between maxillary sinus floor (MSF) and maxillary posterior teeth (MPT), and to compare accuracy of MSF-MPTnet, dentists and radiologists identifying contact relationship. Study design: A total of 1035 PRs and 1035 Cone-beam computed tomographys （CBCT）images were collected from January 2018 to April 2022. The relationships were classified into class I and II by CBCT. Class I represents non-contact group, and class II represents contact group. 350 PRs were randomly selected as test dataset and accuracy of MSF-MPTnet, dentists, and radiologists was compared. Results: The intraclass correlation coefficient of dentists was 0.460-0.690 and it was 0.453-0.664 for radiologists. Sensitivity and accuracy of MSF-MPTnet were 0.682-0.852and 0.890-0.951, indicating that the output performance of MSF-MPTnet was reliable. Accuracy of maxillary premolars and molars were 79.7%-90.3 %, 76.2%-89.2 % and 72.9%-88.3 % in MSF-MPTnet model, dentists and radiologists. Accuracy of class I relationship in the MSF-MPTnet model (67.7%-94.6 %) was higher than that of dentists (56.5%-84.6 %) in maxillary first premolars and right second premolar, and accuracy of class I relationship in the MSF-MPTnet model is also higher than radiologists (40.0%-78.1 %) in all teeth positions ( Conclusions: MSF-MPTnet model could increase detecting accuracy of the relationship between MSF and MPT, minimize pseudo contact relationship and reduce frequency of CBCT use.

Indexed as

Artificial intelligenceDecision-makingDeep learning/machine learningOral diagnosisRadiology

Identifiers

PMID38799758

DOI10.1016/j.heliyon.2024.e31052

PMCPMC11126831

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LicenceCC BY-NC-ND

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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.