Automated Chicago Classification for Esophageal Motility Disorder Diagnosis Using Machine Learning

Automated Chicago Classification for Esophageal Motility Disorder Diagnosis Using Machine Learning

The goal of this paper is to provide a Machine Learning-based solution that can be utilized to automate the Chicago Classification algorithm, the state-of-the-art scheme for esophageal motility disease identification. First, the photos were preprocessed by locating the area of interest—the precise i...

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Bibliographic Details
Main Authors: Teodora Surdea-Blaga, Gheorghe Sebestyen, Zoltan Czako, Anca Hangan, Dan Lucian Dumitrascu, Abdulrahman Ismaiel, Liliana David, Imre Zsigmond, Giuseppe Chiarioni, Edoardo Savarino, Daniel Corneliu Leucuta, Stefan Lucian Popa
Format: Article
Language:English
Published: MDPI AG 2022-07-01
Series:Sensors
Subjects:
artificial intelligence
Convolutional Neural Network
Chicago classification
Esophageal Motility Disorder Diagnosis
high-resolution esophageal manometry
machine learning
Online Access:https://www.mdpi.com/1424-8220/22/14/5227

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