TY - GEN
T1 - EXPLORING AUTOMATED DETECTION OF BARRETT'S ESOPHAGUS VIA MACHINE MODELING AND ACOUSTIC ANALYSIS
AU - Pietrowicz, Mary
AU - Kamboj, Amrit K.
AU - Ishikawa, Keiko
AU - Orbelo, Diana
AU - Yarlagadda, Manoj Krishna
AU - Buller, Kevin
AU - Leggett, Cadman
N1 - Publisher Copyright:
© 2023 First author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - AI
KW - barrett's esophagus
KW - computer-aided diagnosis (CAD)
KW - gerd
KW - machine learning
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M3 - Conference contribution
AN - SCOPUS:85191233795
T3 - Proceedings of Forum Acusticum
BT - Forum Acusticum 2023 - 10th Convention of the European Acoustics Association, EAA 2023
PB - European Acoustics Association, EAA
T2 - 10th Convention of the European Acoustics Association, EAA 2023
Y2 - 11 September 2023 through 15 September 2023
ER -