Predictive modeling for early detection of refractory esophageal stricture following esophageal atresia surgery: insight from a machine learning study.

Journal: Pediatric surgery international
Published Date: Jun 26, 2025

Abstract

BACKGROUND: Refractory esophageal stricture (RES) presents a challenging complication after esophageal atresia (EA) repair. Earlier identification of patients with RES could help clinical decision-making. However, there are currently limited articles to predict the occurrence of RES after EA repair.

Authors

  • Chuanping Xie
    Department of Neonatal Surgery, Beijing Childrens Hospital, Capital Medical University, National Center for Children's Health, No. 56 Nalishi Road, Xicheng District, Beijing, 100045, China.
  • Dingding Wang
    Huizhou Municipal Central Hospital, Huizhou, China.
  • An Wang
    Department of Neonatal Surgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
  • Yong Zhao
    a School of Mathematics and Information Science , Henan Polytechnic University , Jiaozuo 454000 , People's Republic of China.
  • Junmin Liao
    Department of Neonatal Surgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
  • Yanan Zhang
    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China.
  • Kaiyun Hua
    Department of Neonatal Surgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
  • Yichao Gu
    Department of Neonatal Surgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
  • Jingbin Du
    Department of Neonatal Surgery, Beijing Childrens Hospital, Capital Medical University, National Center for Children's Health, No. 56 Nalishi Road, Xicheng District, Beijing, 100045, China.
  • Dayan Sun
    Department of Neonatal Surgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
  • Jianlin Guo
    Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No. 56 Nalishi Road, Xicheng District, Beijing, 100045, China. jianlin@163.com.
  • Shuangshuang Li
    Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security, Shenzhen University, Shenzhen 518060, China. Electronic address: lishuangshuang2016@email.szu.edu.cn.
  • Jinshi Huang
    Department of Neonatal Surgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China. jsdr2002@126.com.

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