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Conference 12033 > Paper 12033-18
Paper 12033-18

Real-time esophagus achalasia detection method for esophagoscopy assistance

In person: 22 February 2022 • 9:20 AM - 9:40 AM PST

Abstract

This paper presents an automated real-time esophagus achalasia (achalasia) detection method for esophagoscopy assistance. Achalasia is a well-recognized primary esophageal motor disorder of unknown etiology. To diagnose the achalasia, endoscopic evaluation of the esophagus and stomach is recommended. However, esophagoscopy is low sensitive in the early-stage of achalasia, only about half of patients with early-stage achalasia can be identified. Thus, a quantitative detection system of real-time esophagoscopy video is required for diagnosis assistance of achalasia. This paper presents to use of a convolutional neural network (CNN) to detect all achalasia frames in esophagoscopy video. We trained and evaluated our network with an original dataset that is extracted from several esophagoscopy videos of achalasia patients. Furthermore, we develop a real-time achalasia detection Computer-Aided Diagnosis (CAD) system with the trained network.

Presenter

Nagoya Univ. (Japan)
Presenter/Author
Nagoya Univ. (Japan)
Author
Nagoya Univ. (Japan)
Author
Fukuoka Univ. (Japan)
Author
Showa Univ. Northern Yokohama Hospital (Japan)
Author
Nagoya Univ. (Japan)