Evidence map›Paper›PMID 41140452›Full record

ArticleEClinicalMedicine2025

Deep learning algorithms for identifying developmental dysplasia of the hip based on sonographic images: a retrospective, prospective, multicenter study in China.

Na Xu, Tong Han, Bingxuan Huang, Wei Fan, Xiping Chen, Miaochan Zhu, Lili Miao, Yuhao Huang, Zhiyuan Zhu, Lili Tong and 20 more

Abstract read
PubMed Full text at PMC Publisher
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Article in EClinicalMedicine, 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

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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

30 authors.

Na XuDepartment of Medical Ultrasonics, Affiliated Shenzhen Children's Hospital, College of Medicine, Shantou University, No. 7019 Yitian Road, Shenzhen, China.
Tong HanCancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Bingxuan HuangDepartment of Medical Ultrasonics, Affiliated Shenzhen Children's Hospital, College of Medicine, Shantou University, No. 7019 Yitian Road, Shenzhen, China.
Wei FanDepartment of Ultrasonography, Kunming Children's Hospital (Children's Hospital of Kunming Medical University), No. 288 Qianxing Road, Kunming, China.
Xiping ChenDepartment of Ultrasound, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Intelligent Application of Big Data in Pediatrics Engineering Research Center of Chongqing Education Commission of China, China.
Miaochan ZhuDepartment of Ultrasound Medicine, Maternal and Child Health Hospital of Panyu District, No. 2 Qinghe East Road, Panyu District, Guangzhou City, Guangdong Province, China.
Lili MiaoDepartment of Ultrasound, Shandong University Affiliated Children's Hospital (Jinan Children's Hospital), Jinan, 250022, China.
Yuhao HuangNational-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University, No. 1066, Xueyuan Avenue, Fuguang Community, Taoyuan Street, Nanshan District, Shenzhen, China.
Zhiyuan ZhuNational-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University, No. 1066, Xueyuan Avenue, Fuguang Community, Taoyuan Street, Nanshan District, Shenzhen, China.
Lili TongDepartment of Ultrasound Medicine, Changde Maternal and Child Health Hospital, No. 1058, Dongting Avenue, Wuling District, Changde City, Hunan Province, China.
Liang ChenYichang Maternity & Child Healthcare Hospital, School of Maternity and Children's Clinical Medicine, China Three Gorges University, No. 99 Chengdong Road, Yichang, Hubei, China.
Jinqiao LiuDepartment of Ultrasound (The Affiliated Children's Hospital of Xiangya School of Medicine), Central South University (Hunan Children's Hospital), No. 86 Ziyuan Road, Changsha, Hunan Province, 410007, China.
Shangfu LinShenzhen Nanshan Maternity & Child Healthcare Hospital, No. 1, Wanxia Road, Shenzhen, China.
Lili NieDepartment of Ultrasound Diagnosis, Hebei Children's Hospital, No.133 Jianhua South Street, Shijiazhuang City, Hebei Province, China.
Chuan LiuBaoding Maternal and Child Health Hospital, No.399 Jinzhuan Road, Lianchi District, Baoding City, Hebei, China.
Jun GaoDepartment of Ultrasound Imaging, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science & Technology, Wuhan, Hubei, 430016, China.
Xia ZhanUltrasonography Department, Miluo Maternity and Child Care Hospital, Yueyang, Hunan Province, China.
Lin LinDepartment of Ultrasonography, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, Guangdong, 518020, China.
Mudan MengDepartment of Ultrasound Medicine, Hechi Maternal and Child Health Hospital, No. 43, Wenti Road, Jinchengjiang Subdistrict, Jinchengjiang District, Hechi City, Guangxi Zhuang Autonomous Region, China.
ShanShan XuThe Second Affiliated Hospital of Hainan Medical University, No. 368 Yehai Avenue, Longhua District, Haikou City, Hainan Province, China.
Yehong WangUltrasonography Department, Fifth Affiliated Hospital (Zhuhai), Zunyi Medical University, No. 1439 Zhufeng Avenue, Doumen District, Zhuhai, China.
Honglan PengDepartment of Ultrasound, Affiliated Minda Hospital of Hubei Minzu University, No. 2 Wufengshan Road, Enshi, Hubei, China.
Xuanfan HuDepartment of Ultrasound Medicine, Southern Central Hospital of Yunnan Province (The First People's Hospital of Honghe State), Gejiu, Yunnan, 661199, China.
Ziyi CaoLinfen Maternal and Child Health Hospital Children's Hospital, No. 174, North Ring Road, Linfen, China.
Zongyu ZhangDepartment of Ultrasonic Medicine, Shenzhen Longgang District Maternity and Child Healthcare Hospital, No. 6, Ailong Road, Shenzhen, China.
Dejuan KongDepartment of Pediatric Ultrasound, Ultrasound Diagnostic Center, The First Hospital of Jilin University, 1 Xinmin Avenue, Changchun, Jilin, China.
Tao FengBinzhou People's Hospital, No. 515, Seventh Yellow River Road, Bincheng District, Binzhou City, Shandong, China.
Dong NiNational-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University, No. 1066, Xueyuan Avenue, Fuguang Community, Taoyuan Street, Nanshan District, Shenzhen, China.
Xin YangNational-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University, No. 1066, Xueyuan Avenue, Fuguang Community, Taoyuan Street, Nanshan District, Shenzhen, China.
Luyao ZhouDepartment of Medical Ultrasonics, Affiliated Shenzhen Children's Hospital, College of Medicine, Shantou University, No. 7019 Yitian Road, Shenzhen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Hip ultrasound is the first-line tool to identify developmental dysplasia of the hip (DDH) among suspected infants, yet it has limitations including poor reproducibility and high diagnostic error rates. This study aims to develop and validate a deep convolutional neural network algorithm, named HipSonoNeuNet model (HSNN), using multicenter hip ultrasound data. Methods: This multicenter cross-sectional study combined data from 22 Chinese hospitals (September 2022-January 2025), enrolling 3082 participants. A total of 7286 hip ultrasound images (1429 dynamic, 5857 static) were collected and were divided into three datasets. The study was conducted in three phases. Phase I trained the models using 2431 participants (Dataset 1). Phase II compared diagnostic performance between radiologists of varied experience and the model across 500 participants (Dataset 2). Phase III prospectively validated the model's generalizability with 151 participants (Dataset 3). Findings: In Phase I, the HSNN yielded AUC of 0.99 (95% CI: 0.99-1.00), sensitivity of 1.00 (95% CI: 0.99-1.00), specificity of 0.91 (95% CI: 0.88-1.00), F1 score of 0.90 (95% CI: 0.87-1.00) on internal test dataset. In Phase II, the HSNN achieved an accuracy of 0.94 (95% CI: 0.88-1.00), AUC of 0.99 (95% CI: 0.99-1.00), sensitivity of 1.00 (95% CI: 0.99-1.00), specificity of 0.94 (95% CI: 0.87-1.00), F1 score of 0.58 (95% CI: 0.50-0.66), and strong agreement with expert (κ = 0.77). AI assistance improved all 7 junior radiologists' diagnostic performance (accuracy from 0.90 to 0.93, AUC from 0.80 to 0.95, sensitivity from 0.69 to 0.97) and reduced examination time with enhanced interobserver agreement. In Phase III, the model maintained robust performance (accuracy = 0.92, AUC = 0.99, sensitivity = 1.00, κ with experts = 0.76). Interpretation: The HSNN demonstrates accurate, robust, and generalizable performance in DDH detection. Funding: 1. Guangdong High-level Hospital Construction Fund (SZGSP012), 2. Shenzhen Clinical Research Center (20220819113341005) "Shenzhen Clinical Research Center for Child Health and Disease (szcrc2024_005)", 3. Guangdong Medical Research Funded Project (A2024019), 4. Shenzhen Science and Technology Innovation Commission General Program for Basic Research (JCYJ20220530160000001), and National Natural Science Foundation of China (82271996).

Indexed as

Deep learningDevelopmental dysplasia of the hipInfantsSonographic images

Identifiers

PMID41140452
PMCPMC12547208

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