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

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.

Methods and analysis: This diagnostic study employs a cross-sectional design. The case group includes neonates diagnosed with intestinal diseases (using clinical diagnosis as the gold standard), such as neonatal necrotising enterocolitis (NEC), food protein-induced allergic proctocolitis, and other intestinal conditions (eg, intestinal obstruction, midgut volvulus, congenital megacolon). The control group will be established using frequency matching, stratified by gestational age and postnatal age. Based on the distribution of each stratum in the case group, neonates without intestinal diseases who were hospitalised during the same period will be randomly selected in proportion from the corresponding strata. BSs will be collected using a 3M stethoscope (Littmann 3200). The study will occur in two phases. In the first phase (July 2024 to July 2025), participants from West China Second University Hospital will be randomly divided into a training cohort (for model development with 10-fold cross-validation) and an internal validation cohort in a 7:3 ratio. The second phase (July 2025 to July 2026) will involve external validation, with patients from Sichuan Provincial Children's Hospital and Shenzhen Children's Hospital. Clinical diagnosis will serve as the gold standard, and diagnostic outcomes between the machine learning-based model and manual clinical assessment by physicians of varying experience levels will be compared.

Ethics and dissemination: Ethical approval has been obtained from the Medical Ethics Committee of West China Second University Hospital (Registration No.: 2023SCHH0021), Sichuan Provincial Children's Hospital (Registration No.: 2021YFC2701704) and the Shenzhen Children's Hospital (Registration No.: 2021015). Written informed consent will be collected from all participants prior to BS collection. Study findings will be disseminated through conferences and publications in peer-reviewed journals.

Trial registration number: ChiCTR2400086713.

Keywords: Artificial Intelligence; Machine Learning; NEONATOLOGY; Neonatal intensive & critical care.

Figure 1. Study flow chart. BS, bowel sound; NEC, necrotising enterocolitis; FPIAP, food protein-induced allergic proctocolitis; FMPV, midgut volvulus.

Figure 2. Outline of bowel sounds recording and storage. BS, bowel sound.

Figure 3. Flowchart of neonatal intestinal disease diagnosis system based on the idea of the BERT model.