<p>Manometry, the gold standard for evaluating phasic contractions in the alimentary tract, relies heavily on qualitative pattern recognition, despite generating large volumes of data. Our aim was to develop an automated detection tool that can quantify fundamental manometric events in high-resolution small bowel manometry tracings by assessing their parameters. Digitally identified propagated contractile events (PCEs) are compared to manual analysis; the system achieved 86% accuracy and 91% precision. Utilizing this automated system, we analyzed approximately 30 h of manometry recordings gathered from 10 healthy subjects. The velocity of fully propagated PCEs is significantly faster, and there was a significant decrease in the frequency of the PCEs following phase III activity. We conclude that computer vision-based automated evaluation of PCEs may improve our understanding of fasting intestinal motility in both health and disease.</p>

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Automated digital analysis of propagated contractions recorded during fasting using high resolution small intestinal manometry in healthy participants

  • Poulami Samaddar,
  • Luis Gerardo Alcala Gonzalez,
  • Jeffrey Fetzer,
  • Irene Cofrades del Castillo,
  • Shivaram Poigai Arunachalam,
  • Fernando Azpiroz,
  • Michael Camilleri,
  • Arthur Beyder,
  • Carolina Malagelada

摘要

Manometry, the gold standard for evaluating phasic contractions in the alimentary tract, relies heavily on qualitative pattern recognition, despite generating large volumes of data. Our aim was to develop an automated detection tool that can quantify fundamental manometric events in high-resolution small bowel manometry tracings by assessing their parameters. Digitally identified propagated contractile events (PCEs) are compared to manual analysis; the system achieved 86% accuracy and 91% precision. Utilizing this automated system, we analyzed approximately 30 h of manometry recordings gathered from 10 healthy subjects. The velocity of fully propagated PCEs is significantly faster, and there was a significant decrease in the frequency of the PCEs following phase III activity. We conclude that computer vision-based automated evaluation of PCEs may improve our understanding of fasting intestinal motility in both health and disease.