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<p>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 (<i>p</i> &lt; 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.</p>

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A vision transformer deep learning model for assessing pediatric ileocolic intussusception severity using ultrasound images

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月10日

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  • Jie Liu,
  • Yue Wang,
  • Danping Zeng,
  • Xiaowen Mao,
  • Yunyun Yu,
  • Huijun Feng,
  • Wenli Xiu,
  • Qian Dong,
  • Yuhe Duan,
  • Zhihui Jiang,
  • Pan Zhang,
  • Zhangshuai Zhao,
  • Shiqin Qi,
  • Zhongce Li,
  • Haiyan Qi,
  • Yingying Cao,
  • Hao Wang,
  • Peng Wang,
  • Zhifeng Li,
  • Linsen Bao,
  • Kelei Shang,
  • Zemeng Duan,
  • Aiqin Lin,
  • Gang Shen,
  • Tianyi Zhang,
  • Guangyu Wang,
  • Qiang Hao,
  • Lu Han,
  • Wenrui Mi,
  • Huan Li,
  • Min Li

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.