Kenari Azra Rasouli, Montazerolghaem Ahmadreza, Zojaji Zahra, Ghatee Mehdi, Yousefimehr Behnam, Rahmani Amin, Kalani Mahdi, Kiyanpour Farnoush, Kiani-Abari Mohamad, Fakhar Mohammad Yasin, Rezaei Safiyeh, Tahernia Mojtaba, Vafaie Mohammad Hossein, Besharatnezhad Hamidreza, Bafrani Vahid Rahimi, Tofighi Mohamad Taghi, Sedeh Peyman Adibi, Soheilipour Maryam, Rabbani Hossein
Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran.
J Med Signals Sens. 2025 Feb 28;15:6. doi: 10.4103/jmss.jmss_46_24. eCollection 2025.
Gastroesophageal reflux disease (GERD) is a prevalent digestive disorder that impacts millions of individuals globally. Multichannel intraluminal impedance-pH (MII-pH) monitoring represents a novel technique and currently stands as the gold standard for diagnosing GERD. Accurately characterizing reflux events from MII data are crucial for GERD diagnosis. Despite the initial introduction of clinical literature toward software advancements several years ago, the reliable extraction of reflux events from MII data continues to pose a significant challenge. Achieving success necessitates the seamless collaboration of two key components: a reflux definition criteria protocol established by gastrointestinal experts and a comprehensive analysis of MII data for reflux detection.
In an endeavor to address this challenge, our team assembled a dataset comprising 201 MII episodes. We meticulously crafted precise reflux episode definition criteria, establishing the gold standard and labels for MII data.
A variety of signal-analyzing methods should be explored. The first Isfahan Artificial Intelligence Competition in 2023 featured formal assessments of alternative methodologies across six distinct domains, including MII data evaluations.
This article outlines the datasets provided to participants and offers an overview of the competition results.
胃食管反流病（GERD）是一种常见的消化系统疾病，影响着全球数百万人。多通道腔内阻抗-pH（MII-pH）监测是一种新技术，目前是诊断GERD的金标准。从MII数据中准确表征反流事件对于GERD诊断至关重要。尽管几年前临床文献首次介绍了软件进展，但从MII数据中可靠提取反流事件仍然是一个重大挑战。要取得成功，需要两个关键组件的无缝协作：胃肠病专家制定的反流定义标准协议以及对MII数据进行反流检测的全面分析。
为应对这一挑战，我们的团队收集了一个包含201个MII发作的数据集。我们精心制定了精确的反流发作定义标准，为MII数据建立了金标准和标签。
应探索多种信号分析方法。2023年的首届伊斯法罕人工智能竞赛对包括MII数据评估在内的六个不同领域的替代方法进行了正式评估。
本文概述了提供给参与者的数据集，并对竞赛结果进行了概述。
