A Mandibular Defect Dataset for Autonomous Reconstruction Planning in Oral and Maxillofacial Surgery

The mandibular defect reconstruction algorithms that are based on deep learning demonstrate significant potential for efficiently addressing clinical challenges in oral and maxillofacial surgery. The algorithm heavily relies on high-quality datasets, which remain one of the main bottlenecks. In this study, the first clinically derived Mandibular Defect Dataset, comprising 147 models of various mandibular defects, is introduced. Each model is manually annotated by experienced surgeons, and all processing workflows strictly adhere to clinical standards. In contrast to previous datasets, this dataset accurately represents the complexity of clinical defect boundaries and the diverse anatomical structures of individual patients, making it a valuable resource for developing AI models that exhibit improved generalizability and adaptability in mandibular reconstruction. Additionally, the dataset provides HCL classification diagnoses and relevant information for each defect model, thereby supporting more diverse clinical research in the future.

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Publication Details

Journal
Scientific Data
Published
2025-11-10
DOI
https://doi.org/10.1038/s41597-025-06048-8
Primary Topic
Dental Radiography and Imaging
Type
article
Field-Weighted Citation Impact
0.00

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article

A Mandibular Defect Dataset for Autonomous Reconstruction Planning in Oral and Maxillofacial Surgery

Xinrong Chen, Jinyang Wu, Liangjing Shao, Junnan Wang et al.
Scientific Data
Dental Radiography and Imaging
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A Mandibular Defect Dataset for Autonomous Reconstruction Planning in Oral and Maxillofacial Surgery

Xinrong Chen, Jinyang Wu, Liangjing Shao, Junnan Wang, Wensheng Wang, Jinlong Wu, Xu Xiaofeng, Xing Wang, Yifan Zhou, Lai Jiang, Shilei Zhang
article en

Abstract

The mandibular defect reconstruction algorithms that are based on deep learning demonstrate significant potential for efficiently addressing clinical challenges in oral and maxillofacial surgery. The algorithm heavily relies on high-quality datasets, which remain one of the main bottlenecks. In this study, the first clinically derived Mandibular Defect Dataset, comprising 147 models of various mandibular defects, is introduced. Each model is manually annotated by experienced surgeons, and all processing workflows strictly adhere to clinical standards. In contrast to previous datasets, this dataset accurately represents the complexity of clinical defect boundaries and the diverse anatomical structures of individual patients, making it a valuable resource for developing AI models that exhibit improved generalizability and adaptability in mandibular reconstruction. Additionally, the dataset provides HCL classification diagnoses and relevant information for each defect model, thereby supporting more diverse clinical research in the future.

Scientific DataVol. 12(1)
Shanghai Medical College of Fudan University (CN), Shanghai Jiao Tong University (CN), Fudan University (CN), Shanghai Stomatological Hospital (CN), Shanghai Ninth People's Hospital (CN), National Clinical Research (US), Shanghai Institute of Computing Technology (CN)
Natural Science Foundation of Shanghai, Shanghai Jiao Tong University, School of Medicine, Shanghai Jiao Tong University, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 52%
Dental Radiography and Imaging
DOIOpen Access PDFOpenAlex
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