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Isfahan Artificial Intelligence Event 2023: Reflux Detection Competition
Azra Rasouli Kenari1, Ahmadreza Montazerolghaem2, Zahra Zojaji2
1Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Journal of Medical Signals and Sensors
|April 7, 2025
Summary
Diagnosing gastroesophageal reflux disease (GERD) using multichannel intraluminal impedance-pH (MII-pH) monitoring is challenging. This study presents a curated dataset and competition results to advance MII data analysis for accurate reflux event detection.
Area of Science:
- Gastroenterology and Medical Devices
- Artificial Intelligence in Medicine
Background:
- Gastroesophageal reflux disease (GERD) affects millions worldwide.
- Multichannel intraluminal impedance-pH (MII-pH) monitoring is the gold standard for GERD diagnosis.
- Reliable extraction of reflux events from MII data remains a significant challenge.
Purpose of the Study:
- To address the challenge of reflux event detection in MII data.
- To establish a gold standard dataset for MII data analysis.
- To facilitate advancements in GERD diagnosis through improved MII data interpretation.
Main Methods:
- Assembled a dataset of 201 MII episodes.
- Developed precise reflux episode definition criteria.
- Utilized a competition framework to assess signal-analyzing methodologies.
Main Results:
- A curated dataset with gold standard labels was created.
- The first Isfahan Artificial Intelligence Competition included MII data evaluations.
- Various signal-analyzing methods were formally assessed.
Conclusions:
- Accurate reflux event characterization is crucial for GERD diagnosis.
- Collaboration between expert criteria and data analysis is essential.
- The competition highlighted diverse approaches to MII data analysis.
