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

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

Communication Sciences and Disorders

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 language	English
Title of host publication	Forum Acusticum 2023 - 10th Convention of the European Acoustics Association, EAA 2023
ISBN (Electronic)	9788888942674
State	Published - 2023
Event	10th Convention of the European Acoustics Association, EAA 2023 - Torino, Italy Duration: Sep 11 2023 → Sep 15 2023

Publication series

Name	Proceedings of Forum Acusticum
ISSN (Print)	2221-3767

Conference

Conference	10th Convention of the European Acoustics Association, EAA 2023
Country/Territory	Italy
City	Torino
Period	9/11/23 → 9/15/23

Bibliographical note

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.

Funding

This work was supported by a Mayo Clinic Jerry A. Wenger Career Development Award and a Mayo Clinic Division of Gastroenterology and Hepatology MAX Innovation Award.

Funders	Funder number
Mayo Clinic Rochester

Keywords

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

ASJC Scopus subject areas

Acoustics and Ultrasonics

Cite this

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title = "EXPLORING AUTOMATED DETECTION OF BARRETT'S ESOPHAGUS VIA MACHINE MODELING AND ACOUSTIC ANALYSIS",

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.",

keywords = "AI, barrett's esophagus, computer-aided diagnosis (CAD), gerd, machine learning",

author = "Mary Pietrowicz and Kamboj, {Amrit K.} and Keiko Ishikawa and Diana Orbelo and Yarlagadda, {Manoj Krishna} and Kevin Buller and Cadman Leggett",

note = "Publisher Copyright: {\textcopyright} 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.; 10th Convention of the European Acoustics Association, EAA 2023 ; Conference date: 11-09-2023 Through 15-09-2023",

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Pietrowicz, M, Kamboj, AK, Ishikawa, K, Orbelo, D, Yarlagadda, MK, Buller, K & Leggett, C 2023, EXPLORING AUTOMATED DETECTION OF BARRETT'S ESOPHAGUS VIA MACHINE MODELING AND ACOUSTIC ANALYSIS. in Forum Acusticum 2023 - 10th Convention of the European Acoustics Association, EAA 2023. Proceedings of Forum Acusticum, 10th Convention of the European Acoustics Association, EAA 2023, Torino, Italy, 9/11/23.

EXPLORING AUTOMATED DETECTION OF BARRETT'S ESOPHAGUS VIA MACHINE MODELING AND ACOUSTIC ANALYSIS. / Pietrowicz, Mary; Kamboj, Amrit K.; Ishikawa, Keiko et al.
Forum Acusticum 2023 - 10th Convention of the European Acoustics Association, EAA 2023. 2023. (Proceedings of Forum Acusticum).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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.

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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

T2 - 10th Convention of the European Acoustics Association, EAA 2023

Y2 - 11 September 2023 through 15 September 2023

ER -

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

Abstract

Publication series

Conference

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

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Cite this