EXPLORING AUTOMATED DETECTION OF BARRETT'S ESOPHAGUS VIA MACHINE MODELING AND ACOUSTIC ANALYSIS

Mary Pietrowicz, Amrit K. Kamboj, Keiko Ishikawa, Diana Orbelo, Manoj Krishna Yarlagadda, Kevin Buller, Cadman Leggett

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Gastroesophageal reflux disease (GERD) affects approximately 18-27% of adults in North America; and chronic GERD is associated with Barrett's esophagus (BE), a precursor to esophageal adenocarcinoma. Current screening and diagnostic procedures for GERD/BE are invasive, expensive, and uncomfortable for the patient. Automated screening tools for GERD/BE based on voice analysis and modern machine learning techniques could, however, potentially enable early detection of GERD/BE without invasive procedures. In this study, standardized, scripted speech is collected, analyzed, and compared across three groups, including a) patients with BE (BE+), b) patients without endoscopic evidence of BE (BE-), and c) patients without GERD and without voice symptoms (normal). Acoustic differences across groups are reported. In addition, multiple machine learning techniques are explored, and machine models are trained to detect the BE+ condition. The ability of selected machine learning models to discern across BE+, BE-, and normal conditions is reported.

Original languageEnglish (US)
Title of host publicationForum Acusticum 2023 - 10th Convention of the European Acoustics Association, EAA 2023
PublisherEuropean Acoustics Association, EAA
ISBN (Electronic)9788888942674
StatePublished - 2023
Externally publishedYes
Event10th Convention of the European Acoustics Association, EAA 2023 - Torino, Italy
Duration: Sep 11 2023Sep 15 2023

Publication series

NameProceedings of Forum Acusticum
ISSN (Print)2221-3767

Conference

Conference10th Convention of the European Acoustics Association, EAA 2023
Country/TerritoryItaly
CityTorino
Period9/11/239/15/23

Keywords

  • AI
  • barrett's esophagus
  • computer-aided diagnosis (CAD)
  • gerd
  • machine learning

ASJC Scopus subject areas

  • Acoustics and Ultrasonics

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