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A vision transformer deep learning model for assessing pediatric ileocolic intussusception severity using ultrasound images. 期刊论文

编号：

63879F55E98C7C28FC0B2F8CE50415A6
作者：

Liu Jie^[0,3,0] Wang Yue^[6] Zeng Danping^[7] Mao Xiaowen^[8] Yu Yunyun^[8] Feng Huijun^[0] Xiu Wenli^[11] Dong Qian^[11] Duan Yuhe^[11] Jiang Zhihui^[12] Zhang Pan^[13] Zhao Zhangshuai^[14] Qi Shiqin^[15] Li Zhongce^[15] Qi Haiyan^[16] Cao Yingying^[17] Wang Hao^[17] Wang Peng^[17] Li Zhifeng^[18] Bao Linsen^[18] Shang Kelei^[19] Duan Zemeng^[19] Lin Aiqin^[20] Shen Gang^[0] Zhang Tianyi^[23] Wang Guangyu^[0] Hao Qiang^[0] Han Lu^[0] Mi Wenrui^[0] Li Huan^[0] Li Min^[25]
地址：

(Liu Jie)Department of Pediatric Surgery, The First Affiliated Hospital of Wannan Medical University (Yijishan Hospital of Wannan Medical University), Wannan Medical University, Wuhu, China. liujie19842020@163.com.;School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China. liujie19842020@163.com.;Department of General Surgery, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China. liujie19842020@163.com.,(Wang Yue)School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China.,(Zeng Danping)Department of General surgery and urology, Maternal and Child Health Hospital/Obstetrics and Gynecology Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.,(Mao Xiaowen)Department of Pediatric Surgery, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,(Yu Yunyun)Department of Pediatric Surgery, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,(Feng Huijun)Department of Ultrasound Medicine, The First Affiliated Hospital of Wannan Medical University (Yijishan Hospital of Wannan Medical University), Wannan Medical University, Wuhu, China.,(Xiu Wenli)Department of Pediatric Surgery, Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, China.,(Dong Qian)Department of Pediatric Surgery, Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, China.,(Duan Yuhe)Department of Pediatric Surgery, Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, China.,(Jiang Zhihui)Department of General Surgery, Qingdao Women and Children's Hospital, Qingdao, China.,(Zhang Pan)Department of Pediatric Surgery, Baoji Maternal and Child Health Hospital, Baoji, China.,(Zhao Zhangshuai)Department of Pediatric Surgery, Liaocheng People's Hospital, Liaocheng, China.,(Qi Shiqin)Department of General Surgery, Anhui Provincial Children's hospital, Hefei, China.,(Li Zhongce)Department of General Surgery, Anhui Provincial Children's hospital, Hefei, China.,(Qi Haiyan)Department of Pediatric Surgery, Huaibei Maternal and Child Health Hospital, Huaibei, China.,(Cao Yingying)Department of Pediatric Surgery, Wanbei Coal and Electricity Group General Hospital Co., Ltd., Suzhou, China.,(Wang Hao)Department of Pediatric Surgery, Wanbei Coal and Electricity Group General Hospital Co., Ltd., Suzhou, China.,(Wang Peng)Department of Pediatric Surgery, Wanbei Coal and Electricity Group General Hospital Co., Ltd., Suzhou, China.,(Li Zhifeng)Department of Pediatric Surgery for Hernia of Abdominal Wall, Bozhou People's Hospital, Bozhou, China.,(Bao Linsen)Department of Pediatric Surgery for Hernia of Abdominal Wall, Bozhou People's Hospital, Bozhou, China.,(Shang Kelei)Department of Pediatric Surgery, Fuyang women and children's hospital, Fuyang, China.,(Duan Zemeng)Department of Pediatric Surgery, Fuyang women and children's hospital, Fuyang, China.,(Lin Aiqin)Department of Medical Biology of Wannan Medical University, Wannan Medical University, Wuhu, China.,(Shen Gang)Department of Thoracic And Oncology Surgery, Children's Hospital Affiliated to Shandong University (Jinan Children's Hospital), Jinan, China.,(Zhang Tianyi)Department of Pediatric Surgery, Shandong University Qilu Hospital Dezhou Hospital, Dezhou, China.,(Wang Guangyu)Department of Pediatric Surgery, The First Affiliated Hospital of Wannan Medical University (Yijishan Hospital of Wannan Medical University), Wannan Medical University, Wuhu, China.,(Hao Qiang)Department of Pediatric Surgery, The First Affiliated Hospital of Wannan Medical University (Yijishan Hospital of Wannan Medical University), Wannan Medical University, Wuhu, China.,(Han Lu)Department of Pediatric Surgery, The First Affiliated Hospital of Wannan Medical University (Yijishan Hospital of Wannan Medical University), Wannan Medical University, Wuhu, China.,(Mi Wenrui)Department of Pediatric Surgery, The First Affiliated Hospital of Wannan Medical University (Yijishan Hospital of Wannan Medical University), Wannan Medical University, Wuhu, China.,(Li Huan)Department of General Surgery, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China. erwailihuan@163.com.,(Li Min)School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China. ml025@njust.edu.cn.
语种：

英文
期刊：

NPJ digital medicine ISSN：2398-6352 2026 年 ; 2026-Jul-10
收录：
摘要：

Timely identification of children with ileocolic intussusception likely to fail air-enema reduction is critical to avoid delays and bowel perforation. However, even expert sonographers show inter-observer variability. We developed and prospectively validated a Vision Transformer (ViT) deep learning system to predict reduction failure from static B-mode ultrasound images. This multicenter bidirectional cohort study included 5602 children (4-60 months) who underwent air-enema reduction at 14 Chinese tertiary hospitals (retrospective cohort: 2019-2024). After data augmentation, 10,151 images (8122 training, 2029 validation) were used to train a ViT model for binary classification ("success" vs. "failure"). External validation was performed on a prospective cohort of 190 patients (March-June 2025), with three junior and three senior sonographers independently predicting outcomes. The study was approved by the Ethics Committee of Yijishan Hospital of Wannan Medical University (approval No. 2025-04) and registered with ChiCTR2500098673. The model achieved high internal performance (failure: accuracy 0.880, precision 0.969; success: accuracy 0.970, precision 0.898). In the prospective cohort, the ViT model achieved 93.7% overall accuracy, significantly higher than senior (74.7%) and junior (60.7%) sonographers (p < 0.05). This study innovatively applies ViT to assess pediatric ileocolic intussusception severity, providing an objective, accurate tool to support clinical decision-making and reduce treatment risks.;
推荐引用方式
GB/T 7714：

Liu Jie,Wang Yue,Zeng Danping, et al. A vision transformer deep learning model for assessing pediatric ileocolic intussusception severity using ultrasound images. [J].NPJ digital medicine,2026.
APA：

Liu Jie,Wang Yue,Zeng Danping,Mao Xiaowen,&Li Min.(2026).A vision transformer deep learning model for assessing pediatric ileocolic intussusception severity using ultrasound images. .NPJ digital medicine.
MLA：

Liu Jie, et al. "A vision transformer deep learning model for assessing pediatric ileocolic intussusception severity using ultrasound images." .NPJ digital medicine(2026).
入库时间：

9/2/2026 9:18:13 PM
更新时间：

9/2/2026 9:18:13 PM

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