Machine learning-based diagnostic model for neonatal intestinal diseases in multiple centres: a cross-sectional study protocol.

Journal: BMJ open
Published Date: May 14, 2025

Abstract

BACKGROUND: Neonatal intestinal diseases often have an insidious onset and can lead to poor outcomes if not identified early. Early assessment of abnormal bowel function is critical for timely intervention and improving prognosis, underscoring the clinical importance of reducing mortality related to these conditions through rapid diagnosis and treatment. Bowel sounds (BSs), produced by intestinal contractions, are a key physiological indicator reflecting intestinal function. However, manual clinical assessment of BSs has limitations in terms of consistency and interpretative accuracy, which restricts its clinical application. This study aims to develop an machine learning-based diagnostic model for neonatal intestinal diseases using BS analysis and to compare its diagnostic accuracy with that of manual clinical assessment.

Authors

  • Qi Zhao
  • Qian Gao
    Department of Obstetrics, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China.
  • Xiang Guo
    State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangzhou, 510060, P. R. China. guoxiang@sysucc.org.cn.
  • Yue Han
  • Jin Zhang
    Department of Otolaryngology, The Second People's Hospital of Yibin, Yibin, Sichuan, China.
  • Yi Yang
    Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Jun Tang
    School of Electronics and Information Engineering, Anhui University, Hefei, China.
  • Jing Shi
    The First Affiliated Hospital of China Medical University, Shenyang 110122, Liaoning Province, China.
  • Ling He
    Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Tao Xiong
    State Key Laboratory of Food Science & Technology, No. 235 Nanjing East Road, Nanchang, Jiangxi, 330047, PR China; School of Food Science & Technology, Nanchang University, No. 235 Nanjing East Road, Nanchang, Jiangxi, 330047, PR China. Electronic address: xiongtao0907@163.com.

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