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How to predict abnormal acid reflux: recent developments.

Yu Kyung Cho1, Donghoon Kang1, Jae Myung Park1

  • 1Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.

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|February 6, 2026
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Summary
This summary is machine-generated.

Novel physiological markers and AI integration enhance the diagnosis of Gastroesophageal reflux disease (GERD), improving accuracy for inconclusive cases. These advancements offer precision in identifying abnormal acid reflux beyond traditional monitoring.

Keywords:
Gastroesophageal refluxacid exposure timeartificial intelligencebaseline impedancepost-reflux swallow-induced peristaltic wave

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Area of Science:

  • Gastroenterology
  • Physiology
  • Medical Technology

Background:

  • Gastroesophageal reflux disease (GERD) diagnosis traditionally relies on pH monitoring and acid exposure time (AET).
  • Inconclusive results from traditional methods necessitate advanced diagnostic approaches.
  • Recent physiological and technological advancements offer new parameters for GERD assessment.

Purpose of the Study:

  • To review recent advancements in predicting abnormal acid reflux.
  • To highlight novel physiological markers and artificial intelligence (AI) applications in GERD diagnosis.
  • To discuss the potential of these new tools in improving diagnostic accuracy.

Main Methods:

  • Review of novel physiologic indices including baseline impedance and post-reflux swallow-induced peristaltic wave (PSPW).
  • Inclusion of high-resolution esophageal manometry (HRM) parameters like EGJ contractile integral (EGJ-CI).
  • Exploration of artificial intelligence (AI) for automating reflux event detection and classification.

Main Results:

  • Novel metrics like baseline impedance and PSPW offer insights into mucosal integrity and chemical clearance.
  • HRM provides indirect evidence of esophageal function.
  • AI demonstrates potential in automating complex analyses, reducing variability and supporting precision diagnosis.

Conclusions:

  • Novel physiologic metrics and AI integration significantly enhance the prediction of abnormal acid reflux, especially in borderline cases.
  • These advanced tools promise improved diagnostic accuracy for GERD.
  • Standardization and multicenter validation are crucial for clinical adoption.