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Machine Learning Algorithm Applied to pH-Impedance Monitoring Tracings Identifies Pathologic Supragastric Belching.

Benjamin D Rogers1,2, Michael Holloway1, Akinara Sawada3

  • 1Division of Gastroenterology, Hepatology, and Nutrition, University of Louisville School of Medicine, Louisville, Kentucky, USA.

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Summary
This summary is machine-generated.

Machine learning accurately identifies supragastric belches (SGB), improving detection beyond manual review. This algorithm effectively distinguishes SGB patients from those with GERD and healthy individuals.

Keywords:
machine learningpH‐impedancesupragastric belching

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Area of Science:

  • Gastroenterology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Supragastric belches (SGB) are difficult to detect with current software and manual review.
  • Accurate identification of SGB is crucial for patient diagnosis and management.

Purpose of the Study:

  • To determine if machine learning (ML) can accurately identify SGB events.
  • To assess the clinical utility of an ML algorithm for SGB detection.

Main Methods:

  • A convolutional neural network (CNN) was developed using pH-impedance data from GERD patients, excessive SGB patients, and healthy volunteers.
  • The CNN was trained, validated, and tested on manually reviewed SGB events, with novel events identified by the algorithm.

Main Results:

  • The ML algorithm demonstrated high accuracy in segregating SGB patients from GERD and healthy volunteers (AUC 0.859).
  • Sensitivity was 100% and specificity was 62.5% at an optimal threshold of 11 events.
  • The algorithm identified unique SGB events missed during manual review, particularly in the SGB cohort.

Conclusions:

  • A de novo ML algorithm can accurately identify SGB events.
  • The ML approach reliably differentiates SGB patients from healthy individuals and those with GERD.
  • This technology has the potential to improve SGB diagnosis and management.