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No sonographer, no radiologist: Assessing accuracy of artificial intelligence on breast ultrasound volume sweep imaging scans

Breast ultrasound provides a first-line evaluation for breast masses, but the majority of the world lacks access to any form of diagnostic imaging. In this pilot study, we assessed the combination of artificial intelligence (Samsung S-Detect for Breast) with volume sweep imaging (VSI) ultrasound sca...

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Autores principales: Marini, Thomas J., Castaneda, Benjamin, Parker, Kevin, Baran, Timothy M., Romero, Stefano, Iyer, Radha, Zhao, Yu T., Hah, Zaegyoo, Park, Moon Ho, Brennan, Galen, Kan, Jonah, Meng, Steven, Dozier, Ann, O’Connell, Avice
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931251/
https://www.ncbi.nlm.nih.gov/pubmed/36812553
http://dx.doi.org/10.1371/journal.pdig.0000148
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author Marini, Thomas J.
Castaneda, Benjamin
Parker, Kevin
Baran, Timothy M.
Romero, Stefano
Iyer, Radha
Zhao, Yu T.
Hah, Zaegyoo
Park, Moon Ho
Brennan, Galen
Kan, Jonah
Meng, Steven
Dozier, Ann
O’Connell, Avice
author_facet Marini, Thomas J.
Castaneda, Benjamin
Parker, Kevin
Baran, Timothy M.
Romero, Stefano
Iyer, Radha
Zhao, Yu T.
Hah, Zaegyoo
Park, Moon Ho
Brennan, Galen
Kan, Jonah
Meng, Steven
Dozier, Ann
O’Connell, Avice
author_sort Marini, Thomas J.
collection PubMed
description Breast ultrasound provides a first-line evaluation for breast masses, but the majority of the world lacks access to any form of diagnostic imaging. In this pilot study, we assessed the combination of artificial intelligence (Samsung S-Detect for Breast) with volume sweep imaging (VSI) ultrasound scans to evaluate the possibility of inexpensive, fully automated breast ultrasound acquisition and preliminary interpretation without an experienced sonographer or radiologist. This study was conducted using examinations from a curated data set from a previously published clinical study of breast VSI. Examinations in this data set were obtained by medical students without prior ultrasound experience who performed VSI using a portable Butterfly iQ ultrasound probe. Standard of care ultrasound exams were performed concurrently by an experienced sonographer using a high-end ultrasound machine. Expert-selected VSI images and standard of care images were input into S-Detect which output mass features and classification as “possibly benign” and “possibly malignant.” Subsequent comparison of the S-Detect VSI report was made between 1) the standard of care ultrasound report by an expert radiologist, 2) the standard of care ultrasound S-Detect report, 3) the VSI report by an expert radiologist, and 4) the pathological diagnosis. There were 115 masses analyzed by S-Detect from the curated data set. There was substantial agreement of the S-Detect interpretation of VSI among cancers, cysts, fibroadenomas, and lipomas to the expert standard of care ultrasound report (Cohen’s κ = 0.73 (0.57–0.9 95% CI), p<0.0001), the standard of care ultrasound S-Detect interpretation (Cohen’s κ = 0.79 (0.65–0.94 95% CI), p<0.0001), the expert VSI ultrasound report (Cohen’s κ = 0.73 (0.57–0.9 95% CI), p<0.0001), and the pathological diagnosis (Cohen’s κ = 0.80 (0.64–0.95 95% CI), p<0.0001). All pathologically proven cancers (n = 20) were designated as “possibly malignant” by S-Detect with a sensitivity of 100% and specificity of 86%. Integration of artificial intelligence and VSI could allow both acquisition and interpretation of ultrasound images without a sonographer and radiologist. This approach holds potential for increasing access to ultrasound imaging and therefore improving outcomes related to breast cancer in low- and middle- income countries.
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spelling pubmed-99312512023-02-16 No sonographer, no radiologist: Assessing accuracy of artificial intelligence on breast ultrasound volume sweep imaging scans Marini, Thomas J. Castaneda, Benjamin Parker, Kevin Baran, Timothy M. Romero, Stefano Iyer, Radha Zhao, Yu T. Hah, Zaegyoo Park, Moon Ho Brennan, Galen Kan, Jonah Meng, Steven Dozier, Ann O’Connell, Avice PLOS Digit Health Research Article Breast ultrasound provides a first-line evaluation for breast masses, but the majority of the world lacks access to any form of diagnostic imaging. In this pilot study, we assessed the combination of artificial intelligence (Samsung S-Detect for Breast) with volume sweep imaging (VSI) ultrasound scans to evaluate the possibility of inexpensive, fully automated breast ultrasound acquisition and preliminary interpretation without an experienced sonographer or radiologist. This study was conducted using examinations from a curated data set from a previously published clinical study of breast VSI. Examinations in this data set were obtained by medical students without prior ultrasound experience who performed VSI using a portable Butterfly iQ ultrasound probe. Standard of care ultrasound exams were performed concurrently by an experienced sonographer using a high-end ultrasound machine. Expert-selected VSI images and standard of care images were input into S-Detect which output mass features and classification as “possibly benign” and “possibly malignant.” Subsequent comparison of the S-Detect VSI report was made between 1) the standard of care ultrasound report by an expert radiologist, 2) the standard of care ultrasound S-Detect report, 3) the VSI report by an expert radiologist, and 4) the pathological diagnosis. There were 115 masses analyzed by S-Detect from the curated data set. There was substantial agreement of the S-Detect interpretation of VSI among cancers, cysts, fibroadenomas, and lipomas to the expert standard of care ultrasound report (Cohen’s κ = 0.73 (0.57–0.9 95% CI), p<0.0001), the standard of care ultrasound S-Detect interpretation (Cohen’s κ = 0.79 (0.65–0.94 95% CI), p<0.0001), the expert VSI ultrasound report (Cohen’s κ = 0.73 (0.57–0.9 95% CI), p<0.0001), and the pathological diagnosis (Cohen’s κ = 0.80 (0.64–0.95 95% CI), p<0.0001). All pathologically proven cancers (n = 20) were designated as “possibly malignant” by S-Detect with a sensitivity of 100% and specificity of 86%. Integration of artificial intelligence and VSI could allow both acquisition and interpretation of ultrasound images without a sonographer and radiologist. This approach holds potential for increasing access to ultrasound imaging and therefore improving outcomes related to breast cancer in low- and middle- income countries. Public Library of Science 2022-11-23 /pmc/articles/PMC9931251/ /pubmed/36812553 http://dx.doi.org/10.1371/journal.pdig.0000148 Text en © 2022 Marini et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Marini, Thomas J.
Castaneda, Benjamin
Parker, Kevin
Baran, Timothy M.
Romero, Stefano
Iyer, Radha
Zhao, Yu T.
Hah, Zaegyoo
Park, Moon Ho
Brennan, Galen
Kan, Jonah
Meng, Steven
Dozier, Ann
O’Connell, Avice
No sonographer, no radiologist: Assessing accuracy of artificial intelligence on breast ultrasound volume sweep imaging scans
title No sonographer, no radiologist: Assessing accuracy of artificial intelligence on breast ultrasound volume sweep imaging scans
title_full No sonographer, no radiologist: Assessing accuracy of artificial intelligence on breast ultrasound volume sweep imaging scans
title_fullStr No sonographer, no radiologist: Assessing accuracy of artificial intelligence on breast ultrasound volume sweep imaging scans
title_full_unstemmed No sonographer, no radiologist: Assessing accuracy of artificial intelligence on breast ultrasound volume sweep imaging scans
title_short No sonographer, no radiologist: Assessing accuracy of artificial intelligence on breast ultrasound volume sweep imaging scans
title_sort no sonographer, no radiologist: assessing accuracy of artificial intelligence on breast ultrasound volume sweep imaging scans
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931251/
https://www.ncbi.nlm.nih.gov/pubmed/36812553
http://dx.doi.org/10.1371/journal.pdig.0000148
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