Real-time esophagus achalasia detection method for esophagoscopy assistance

Kai Jiang; Masahiro Oda; Hironari Shiwaku; Masashi Misawa; Kensaku Mori

doi:10.1117/12.2613289

4 April 2022 Real-time esophagus achalasia detection method for esophagoscopy assistance

Kai Jiang, Masahiro Oda, Hironari Shiwaku, Masashi Misawa, Kensaku Mori

Author Affiliations +

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 1203304 (2022) https://doi.org/10.1117/12.2613289
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

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 to ensure that there is not a malignancy causing the disease or esophageal squamous cell carcinoma complicating achalasia. 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 videos. The features of achalasia cannot be easily distinguished. To better extract features from esophagoscopy frames, we introduce dense pooling connections and dilated convolutions in the CNN. 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 ComputerAided Diagnosis (CAD) system with the trained network. The CAD system can detect each frame from the input esophagoscopy videos with only 0.1 milliseconds delay. The real-time achalasia detection system achieved 0.872 accuracy, and 0.943 AUC score on our dataset.

Citation Download Citation

Kai Jiang, Masahiro Oda, Hironari Shiwaku, Masashi Misawa, and Kensaku Mori "Real-time esophagus achalasia detection method for esophagoscopy assistance", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 1203304 (4 April 2022); https://doi.org/10.1117/12.2613289

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