Deep learning-based artificial intelligence model for identifying swallow types in esophageal high-resolution manometry.

Wenjun KouGalal Osama GalalMatthew William KlugVladislav MukhinDustin A CarlsonMozziyar EtemadiPeter J KahrilasJohn E Pandolfino

Published in: Neurogastroenterology and motility : the official journal of the European Gastrointestinal Motility Society (2021)

A deep-learning AI model can automatically and accurately identify the Chicago Classification swallow types and peristalsis classification from raw HRM data. While future work to refine this model and incorporate overall manometric diagnoses are needed, this study demonstrates the role that AI will serve in the interpretation and classification of esophageal HRM studies.

Keyphrases

deep learning
artificial intelligence
machine learning
big data
convolutional neural network
high resolution
mass spectrometry