Enhancing the diagnostic yield of esophageal manometry using distension-contraction plots of peristalsis and artificial intelligence.

IF 3.9 3区 医学 Q1 GASTROENTEROLOGY & HEPATOLOGY American journal of physiology. Gastrointestinal and liver physiology Pub Date : 2024-09-01 Epub Date: 2024-07-02 DOI:10.1152/ajpgi.00139.2024
Ali Zifan, Ji Min Lee, Ravinder K Mittal
{"title":"Enhancing the diagnostic yield of esophageal manometry using distension-contraction plots of peristalsis and artificial intelligence.","authors":"Ali Zifan, Ji Min Lee, Ravinder K Mittal","doi":"10.1152/ajpgi.00139.2024","DOIUrl":null,"url":null,"abstract":"<p><p>Our prior study reveals that the distension-contraction profiles using high-resolution manometry impedance recordings can distinguish patients with dysphagia symptom but normal esophageal function testing (\"functional dysphagia\") from control subjects. The aim of this study was to determine the diagnostic value of the recording protocol used in our prior studies (10-mL swallows with subjects in the Trendelenburg position) against the standard clinical protocol (5-mL swallows with subjects in the supine position). We used advanced machine learning techniques and robust metrics for classification purposes. Studies were performed on 30 healthy subjects and 30 patients with functional dysphagia. A custom-built software was used to extract the relevant distension-contraction features of esophageal peristalsis. Ensemble methods, i.e., gradient boost, support vector machines (SVMs), and logit boost, were used as the primary machine learning algorithms. Although the individual contraction features were marginally different between the two groups, the distension features of peristalsis were significantly different. The receiver operating characteristic (ROC) curve values for the standard recording protocol and the distension features ranged from 0.74 to 0.82; they were significantly better for the protocol used in our prior studies, ranging from 0.81 to 0.91. The ROC curve values using three machine learning algorithms were far superior for the distension than the contraction features of esophageal peristalsis, revealing a value of 0.95 for the SVM algorithm. Current patient classification for esophageal motility disorders, based on the contraction phase of peristalsis, ignores a large number of patients who have an abnormality in the distension phase of peristalsis. Distension-contraction plots should be the standard for assessing esophageal peristalsis in clinical practice.<b>NEW & NOTEWORTHY</b> Our findings underscore the superiority of distension features over contraction metrics in diagnosing esophageal dysfunctions. By leveraging state-of-the-art machine learning techniques, our study highlights the diagnostic potential of distension-contraction plots of peristalsis. Implementation of these plots could significantly enhance the accuracy of identifying patients with esophageal motor disorders, advocating for their adoption as the standard in clinical practice.</p>","PeriodicalId":7725,"journal":{"name":"American journal of physiology. Gastrointestinal and liver physiology","volume":" ","pages":"G405-G413"},"PeriodicalIF":3.9000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11427102/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"American journal of physiology. Gastrointestinal and liver physiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1152/ajpgi.00139.2024","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/7/2 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"GASTROENTEROLOGY & HEPATOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Our prior study reveals that the distension-contraction profiles using high-resolution manometry impedance recordings can distinguish patients with dysphagia symptom but normal esophageal function testing ("functional dysphagia") from control subjects. The aim of this study was to determine the diagnostic value of the recording protocol used in our prior studies (10-mL swallows with subjects in the Trendelenburg position) against the standard clinical protocol (5-mL swallows with subjects in the supine position). We used advanced machine learning techniques and robust metrics for classification purposes. Studies were performed on 30 healthy subjects and 30 patients with functional dysphagia. A custom-built software was used to extract the relevant distension-contraction features of esophageal peristalsis. Ensemble methods, i.e., gradient boost, support vector machines (SVMs), and logit boost, were used as the primary machine learning algorithms. Although the individual contraction features were marginally different between the two groups, the distension features of peristalsis were significantly different. The receiver operating characteristic (ROC) curve values for the standard recording protocol and the distension features ranged from 0.74 to 0.82; they were significantly better for the protocol used in our prior studies, ranging from 0.81 to 0.91. The ROC curve values using three machine learning algorithms were far superior for the distension than the contraction features of esophageal peristalsis, revealing a value of 0.95 for the SVM algorithm. Current patient classification for esophageal motility disorders, based on the contraction phase of peristalsis, ignores a large number of patients who have an abnormality in the distension phase of peristalsis. Distension-contraction plots should be the standard for assessing esophageal peristalsis in clinical practice.NEW & NOTEWORTHY Our findings underscore the superiority of distension features over contraction metrics in diagnosing esophageal dysfunctions. By leveraging state-of-the-art machine learning techniques, our study highlights the diagnostic potential of distension-contraction plots of peristalsis. Implementation of these plots could significantly enhance the accuracy of identifying patients with esophageal motor disorders, advocating for their adoption as the standard in clinical practice.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用蠕动扩张收缩图和人工智能提高食管测压的诊断率
背景:我们之前的研究表明,使用高分辨率测压阻抗(HRMZ)记录的胀缩曲线可以将有吞咽困难症状但食管功能测试正常("功能性吞咽困难")的患者与对照组区分开来。目的:确定我们之前研究中使用的记录方案(10cc 吞咽,受试者取 Trendelenburg 体位)与标准临床方案(5cc 吞咽,受试者取仰卧位)的诊断价值。我们采用先进的机器学习技术和稳健的指标进行分类:研究对象为 30 名健康受试者和 30 名功能性吞咽困难患者。我们使用定制软件提取食管蠕动的相关扩张-收缩特征。使用梯度提升、支持向量机(SVM)和对数提升等集合方法作为主要的机器学习算法:结果:虽然两组的单个收缩特征差异不大,但蠕动的扩张特征却有显著差异。标准记录方案的扩张特征 ROC 曲线值在 0.74 至 0.82 之间;我们之前研究中使用的方案的 ROC 曲线值在 0.81 至 0.91 之间,明显优于标准记录方案。使用 3 种机器学习算法得出的 ROC 曲线值显示,食管蠕动的扩张特征远优于收缩特征,SVM 算法的 ROC 曲线值为 0.95:结论:目前基于蠕动收缩期的患者分类遗漏了大量蠕动舒张期异常的患者。舒张收缩图应成为临床实践中评估食管蠕动的标准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
9.40
自引率
2.20%
发文量
104
审稿时长
1 months
期刊介绍: The American Journal of Physiology-Gastrointestinal and Liver Physiology publishes original articles pertaining to all aspects of research involving normal or abnormal function of the gastrointestinal tract, hepatobiliary system, and pancreas. Authors are encouraged to submit manuscripts dealing with growth and development, digestion, secretion, absorption, metabolism, and motility relative to these organs, as well as research reports dealing with immune and inflammatory processes and with neural, endocrine, and circulatory control mechanisms that affect these organs.
期刊最新文献
Simultaneous optical imaging of gastric slow waves and contractions in the in vivo porcine stomach. Parenteral nutrition results in peripheral ileal to hepatic circadian discordance in mice. AHCC inhibited hepatic stellate cells activation by regulation of cytoglobin induction via TLR2-SAPK/JNK pathway and collagen production via TLR4-NF-κβ pathway. Formal degree programs in physiology promote careers of clinical scientists and benefit basic science departments. Hepatic bile acid accretion correlates with cholestatic liver injury and therapeutic response in Cyp2c70 knockout mice with a humanized bile acid composition.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1