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

ABSTRACT:

Background

This study aimed to build and evaluate a deep learning, artificial intelligence (AI) model to automatically classify swallow types based on raw data from esophageal high-resolution manometry (HRM).

Methods

HRM studies on patients with no history of esophageal surgery were collected including 1,741 studies with 26,115 swallows labeled by swallow type (normal, hypercontractile, weak-fragmented, failed, and premature) by an expert interpreter per the Chicago Classification. The dataset was stratified and split into train/validation/test datasets for model development. Long short-term memory (LSTM), a type of deep-learning AI model, was trained and evaluated. The overall performance and detailed per-swallow type performance were analyzed. The interpretations of the su

SUBMITTER: Kou W 

PROVIDER: S-EPMC9046460 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature