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

Qi Zhao^1,2, Qian Gao^1,2, Xiang Guo³

¹Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China.

|May 15, 2025

Summary

This study developed a machine learning model to diagnose neonatal intestinal diseases using bowel sound analysis, aiming for earlier and more accurate detection than manual methods. The model

Keywords:

Artificial Intelligence Machine Learning NEONATOLOGY Neonatal intensive & critical care

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Area of Science:

Neonatal Medicine
Biomedical Engineering
Artificial Intelligence in Healthcare

Background:

Neonatal intestinal diseases require early detection for improved outcomes.
Manual assessment of bowel sounds (BSs) for intestinal function is inconsistent.
Developing objective diagnostic tools for neonatal intestinal conditions is crucial.

Purpose of the Study:

To develop and validate a machine learning (ML) model for diagnosing neonatal intestinal diseases using BS analysis.
To compare the diagnostic accuracy of the ML model against manual clinical assessment.

Main Methods:

A cross-sectional diagnostic study involving neonates with and without intestinal diseases.
Bowel sounds collected using a 3M stethoscope (Littmann 3200).
Model development and internal validation (7:3 ratio) followed by external validation across multiple hospitals.

Main Results:

The study aims to compare the diagnostic accuracy of the ML model with manual assessments.
Clinical diagnosis serves as the gold standard for comparison.
Results will highlight the ML model's performance against physicians of varying experience levels.

Conclusions:

A novel ML-based approach using BS analysis shows promise for accurate neonatal intestinal disease diagnosis.
This technology could enhance early detection and improve patient prognosis.
Further validation is essential to integrate this tool into clinical practice.