Evidence map›Paper›PMID 41214004›Full record

ArticleScientific data2025

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

Jinyang Wu, Lai Jiang, Liangjing Shao, Wensheng Wang, Xiaofeng Xu, Yifan Zhou, Xing Wang, Junnan Wang, Jinlong Wu, Xinrong Chen and 1 more

Abstract readDataset
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Jinyang Wu *Department of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.ORCID 0000-0002-3285-7389
Lai Jiang *Department of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Liangjing Shao *Academy for Engineering and Technology, Fudan University, Shanghai, China.
Wensheng WangAcademy for Engineering and Technology, Fudan University, Shanghai, China.
Xiaofeng XuDepartment of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yifan ZhouDepartment of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xing WangDepartment of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Junnan WangDepartment of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jinlong WuDepartment of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xinrong ChenAcademy for Engineering and Technology, Fudan University, Shanghai, China. chenxinrong@fudan.edu.cn.
Shilei ZhangDepartment of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. leinnymd@hotmail.com.

Funding

Natural Science Foundation of Shanghai (Natural Science Foundation of Shanghai Municipality) 24ZR1443700Shanghai Jiao Tong University (SJTU) SJTU Trans-med Awards Research (20230104)SJTU | School of Medicine, Shanghai Jiao Tong University (School of Medicine, Shanghai Jiao Tong University) Research Doctor, XKQJS202401, JS22A03
6 · The paper itself

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.

Indexed as

MandibleMandibular ReconstructionSurgery, OralAlgorithmsDeep LearningHumans

Identifiers

PMID41214004
PMCPMC12603100

What OpenQuestion holds

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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.