Evidence map›Paper›PMID 41541682›Full record

ArticleiScience2026

Deep learning for pediatric femoral neck fracture detection in a multicenter study.

Xiaoliang Chen, Mingdi Xue, Xudong Wang, Lei Jiang, Tao Zhang, Ning Ling, Haocheng Xu, Weihang Gao, Lek Hang Cheang, Jiaming Yang and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

Xiaoliang ChenDepartment of Orthopedics Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Mingdi XueDepartment of Orthopedics Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Xudong WangDepartment of Orthopedics Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Lei JiangDepartment of Orthopedics, People's Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, Yinchuan 750000, China.
Tao ZhangNational Key Laboratory of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China.
Ning LingNational Key Laboratory of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China.
Haocheng XuNational Key Laboratory of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China.
Weihang GaoDepartment of Orthopedics Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Lek Hang CheangDepartment of Orthopedic Surgery, Centro Hospitalar Conde de Sao Januario, Macau 999078, China.
Jiaming YangDepartment of Orthopedics Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Wa Hou TaiDepartment of Neurosurgery, Centro Hospitalar Conde de Sao Januario, Macau 999078, China.
Jialang HuDepartment of Orthopedics, Wuhan Pu'ai Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430010, China.
Pengran LiuDepartment of Orthopedics Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Tongtong HuoSchool of Electronic Information, Wuhan University of Science and Technology, Wuhan 430081, China.
Zhewei YeDepartment of Orthopedics Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pediatric femoral neck fractures (FNFs) are uncommon but may result in severe complications if undiagnosed. This study developed a deep learning model for automated detection and localization of FNFs on pediatric hip radiographs. The model was trained on 2,594 hip radiographs from 2,116 patients across eight centers. The optimal model (YOLOv11s) achieved a mean average precision at 0.5 IoU threshold (mAP@0.5) of 90.6% and an AUC of 0.921 on the internal test set, and a mAP@0.5 of 96.8% and an AUC of 0.968 on the external test set. To our knowledge, this represents one of the most comprehensive multicenter AI diagnostic studies for detecting pediatric FNFs. In a single-center reader study, AI assistance significantly improved diagnostic performance among emergency department orthopedic surgeons, particularly those with limited experience. These findings suggest the potential clinical utility of this model for supporting decision-making in emergency settings.

Indexed as

health sciences

Identifiers

PMID41541682
PMCPMC12799790

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Registered trials

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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.