Evidence map›Paper›PMID 42455244›Full record

ArticleJournal of orthopaedics and traumatology : official journal of the Italian Society of Orthopaedics and Traumatology2026

Development and validation of a vision transformer model for radiographic assessment of pediatric humeroradial joint alignment.

WenTao Wang, ShengHua He, YiFan Rao, Federico Canavese, ShengPing Tang, ChongZhi Zhao, Hang Liu, Antonio Andreacchio, Maria Beatrice Michelis, Chiara Arrigoni and 6 more

Abstract readValidation Study
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Article in Journal of orthopaedics and traumatology : official journal of the Italian Society of Orthopaedics and Traumatology, 2026. 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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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

16 authors.

WenTao Wang *The fourth Clinical Medical College of Guangzhou University of Chinese Medicine, ShenZhen, GuangDong, China. wangwt53@mail2.sysu.edu.cn.ORCID http://orcid.org/0000-0001-7506-6651
ShengHua He *The fourth Clinical Medical College of Guangzhou University of Chinese Medicine, ShenZhen, GuangDong, China.
YiFan Rao *School of Integrated Circuits (International School of Microelectronics), Dongguan University of Technology, DongGuan, GuangDong, China.
Federico CanaveseOrthopedic and Traumatology Department, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini 5, Genoa, Italy.
ShengPing TangDepartment of Pediatric Surgery, Shenzhen Maternity and Child Healthcare Hospital, ShenZhen, GuangDong, China.
ChongZhi ZhaoDepartment of Pediatric Orthopedics, Foshan Hospital of Traditional Chinese Medicine, FoShan, GuangDong, China.
Hang LiuDepartment of Pediatric Orthopedics, Children's Hospital of Chongqing Medical University, ChongQing, China.
Antonio AndreacchioDepartment of Pediatric Orthopedics, Vittore Buzzi Children's Hospital, 20154, Milan, Italy.
Maria Beatrice MichelisOrthopedic and Traumatology Department, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini 5, Genoa, Italy.
Chiara ArrigoniOrthopedic and Traumatology Department, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini 5, Genoa, Italy.
Nunzio CatenaOrthopedic and Traumatology Department, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini 5, Genoa, Italy.
Sandro GregorioOrthopedic and Traumatology Department, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini 5, Genoa, Italy.
Lorenza MarengoOrthopedic and Traumatology Department, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini 5, Genoa, Italy.
ShunYou ChenDepartment of Pediatric Orthopedics, Fuzhou Second General Hospital, FuZhou, FuJian, China. csy508@163.com.
SuiGu TangSchool of Integrated Circuits (International School of Microelectronics), Dongguan University of Technology, DongGuan, GuangDong, China. tangsuigu@dgut.edu.cn.
QianQian MeiDepartment of Pediatric Orthopedics, Shenzhen Children's Hospital, ShenZhen, GuangDong, China. QianqianMei2023@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWhether the radiographic assessments of pediatric anatomical (A-HRJA) and nonanatomical humeroradial joint alignment (NA-HRJA) among pediatric orthopedic surgeons (POS) relies on clinical experience remains unknown. No vision transformer (ViT) model for automated and accurate radiographic pediatric HRJAs categorization has been developed and validated. This study aims to evaluate the radiographic assessment outcomes of pediatric A-HRJAs and NA-HRJAs among POS with varying levels of experience, and to develop and validate a model for automated and accurate categorization of pediatric HRJAs on radiographs. MATERIALS AND

methodsA total of 294 pediatric radiographs were independently recorded by four junior-level, four mid-level, and three senior-level POS. Then, the performance of the POS with various levels of experience was evaluated. A total of 5118 pediatric radiographs (2932 A-HRJAs and 2186 NA-HRJAs) were prepared to construct the vision transfer (ViT) model. The ViT model's clinical efficacy was evaluated using an external testing dataset (84 A-HRJAs and 132 NA-HRJAs). A total of 11 POS independently recorded the HRJA types of the external testing dataset twice with a 1-month interval: first without ViT model assistance, and second with assistance. The rating performances of the POS without and with ViT model assistance were compared.

resultsThe accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were similar among junior-level, mid-level, and senior-level POS (P > 0.050). The ViT model's area under the receiver operating characteristic curve was 0.944. The ViT model's rating performance was significantly superior to that of POS in the first round regarding accuracy (P = 0.001), sensitivity (P = 0.001), and NPV (P < 0.001). However, no significant differences were found between the ViT model and POS in the second round. The accuracy (P = 0.008), PPV (P = 0.043), and NPV (P = 0.030) improved significantly in the second round compared with the first, as demonstrated by the following values: 86.9-92.6%, 88.3-93.6%, and 85.8-93.2%, respectively. The kappa value reflecting the reliability among POS improved by 24.8% in the second round.

conclusionsThe accuracy of manually classifying radiographic pediatric HRJA types did not depend on clinical experience. The ViT model can accurately categorize radiographic pediatric HRJA types and significantly improve the POS's rating performance when categorizing A-HRJAs and NA-HRJAs. LEVEL OF EVIDENCE: III.

Indexed as

Elbow JointChildChild, PreschoolClinical CompetenceFemaleHumansHumerusMaleRadiographyRadiusReproducibility of ResultsAssessmentChildrenDeep learningHumeroradial jointMonteggia fractureVision transformer

Identifiers

PMID42455244
PMCPMC13654914

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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.