A rapid identification method for soft tissue markers of dentofacial deformities based on heatmap regression.

Guilong Zhou, Yu Zhang, Jinlong Zhao, Lei Tian, Guang Jia, Qin Ma

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Article in BDJ open, 2024. 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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6 authors.

Guilong ZhouState Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Centre for Oral Diseases, Shaanxi Clinical Research Centre for Oral Diseases, Department of Orthognathic Trauma Surgery, The Third Affiliated Hospital of Air Force Medical University, 710032, Xi'an, China.

Yu ZhangSchool of Computer Science and Technology, Xidian University, 710071, Xi'an, China.

Jinlong ZhaoState Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Centre for Oral Diseases, Shaanxi Clinical Research Centre for Oral Diseases, Department of Orthognathic Trauma Surgery, The Third Affiliated Hospital of Air Force Medical University, 710032, Xi'an, China.

Lei TianState Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Centre for Oral Diseases, Shaanxi Clinical Research Centre for Oral Diseases, Department of Orthognathic Trauma Surgery, The Third Affiliated Hospital of Air Force Medical University, 710032, Xi'an, China. tianleison@163.com.

Guang JiaSchool of Computer Science and Technology, Xidian University, 710071, Xi'an, China. gjia@xidian.edu.cn.ORCID http://orcid.org/0000-0001-5306-5607

Qin MaState Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Centre for Oral Diseases, Shaanxi Clinical Research Centre for Oral Diseases, Department of Orthognathic Trauma Surgery, The Third Affiliated Hospital of Air Force Medical University, 710032, Xi'an, China. qinma@fmmu.edu.cn.ORCID http://orcid.org/0009-0000-4258-1905

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe purpose of this study was to construct a facial deformity dataset and a network model based on heatmap regression for the recognition of facial soft tissue landmarks to provide a basis for clinicians to perform cephalometric analysis of soft tissue. MATERIALS AND

methodsA 34-point face marker detection model, the Back High-Resolution Network (BHR-Net), was constructed based on the heatmap regression algorithm, and a custom dataset of 1780 facial detection images for orthognathic surgery was collected. The mean normalized error (MNE) and 10% failure rate (FR10%) were used to evaluate the performance of BHR-Net, and a test set of 50 patients was used to verify the accuracy of the landmarks and their measurement indicators. The test results were subsequently validated in 30 patients.

resultsBoth the MNE and FR10% of BHR-Net were optimal compared with other models. In the test set (50 patients), the accuracy of the markers excluding the nose root was 86%, and the accuracy of the remaining markers reached 94%. In the model validation (30 patients), using the markers detected by BHR-Net, the diagnostic accuracy of doctors was 100% for Class II and III deformities, 100% for the oral angle plane, and 70% for maxillofacial asymmetric deformities.

conclusionsBHR-Net, a network model based on heatmap regression, can be used to effectively identify landmarks in maxillofacial multipose images, providing a reliable way for clinicians to perform cephalometric measurements of soft tissue objectively and quickly.

Identifiers

PMID38429260

DOI10.1038/s41405-024-00189-5

PMCPMC10907697

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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.