Deep learning–based artificial intelligence model for identifying swallow types in esophageal high‐resolution manometry
Neurogastroenterology & Motility2021Vol. 34(7), pp. e14290–e14290
Citations Over TimeTop 11% of 2021 papers
Wenjun Kou, Galal Galal, Matthew Klug, Vladislav Mukhin, Dustin A. Carlson, Mozziyar Etemadi, Peter J. Kahrilas, John E. Pandolfino
Abstract
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.
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