A vision transformer deep learning model for assessing pediatric ileocolic intussusception severity using ultrasound images.
Authors
Affiliations (22)
Affiliations (22)
- Department of Pediatric Surgery, The First Affiliated Hospital of Wannan Medical University (Yijishan Hospital of Wannan Medical University), Wannan Medical University, Wuhu, China. [email protected].
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China. [email protected].
- Department of General Surgery, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China. [email protected].
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China.
- Department of General surgery and urology, Maternal and Child Health Hospital/Obstetrics and Gynecology Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
- Department of Pediatric Surgery, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- Department of Ultrasound Medicine, The First Affiliated Hospital of Wannan Medical University (Yijishan Hospital of Wannan Medical University), Wannan Medical University, Wuhu, China.
- Department of Pediatric Surgery, Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, China.
- Department of General Surgery, Qingdao Women and Children's Hospital, Qingdao, China.
- Department of Pediatric Surgery, Baoji Maternal and Child Health Hospital, Baoji, China.
- Department of Pediatric Surgery, Liaocheng People's Hospital, Liaocheng, China.
- Department of General Surgery, Anhui Provincial Children's hospital, Hefei, China.
- Department of Pediatric Surgery, Huaibei Maternal and Child Health Hospital, Huaibei, China.
- Department of Pediatric Surgery, Wanbei Coal and Electricity Group General Hospital Co., Ltd., Suzhou, China.
- Department of Pediatric Surgery for Hernia of Abdominal Wall, Bozhou People's Hospital, Bozhou, China.
- Department of Pediatric Surgery, Fuyang women and children's hospital, Fuyang, China.
- Department of Medical Biology of Wannan Medical University, Wannan Medical University, Wuhu, China.
- Department of Thoracic And Oncology Surgery, Children's Hospital Affiliated to Shandong University (Jinan Children's Hospital), Jinan, China.
- Department of Pediatric Surgery, Shandong University Qilu Hospital Dezhou Hospital, Dezhou, China.
- Department of Pediatric Surgery, The First Affiliated Hospital of Wannan Medical University (Yijishan Hospital of Wannan Medical University), Wannan Medical University, Wuhu, China.
- Department of General Surgery, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China. [email protected].
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China. [email protected].
Abstract
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.