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

Autor:	Marini TJ; Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, United States of America., Castaneda B; Departamento de Ingeniería, Pontificia Universidad Católica del Perú, Lima, Peru., Parker K; Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, United States of America., Baran TM; Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, United States of America., Romero S; Departamento de Ingeniería, Pontificia Universidad Católica del Perú, Lima, Peru., Iyer R; Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, United States of America., Zhao YT; Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, United States of America., Hah Z; Samsung Medison Co., Ltd., Seoul, Republic of Korea., Park MH; Samsung Electronics Co., Ltd., Seoul, Republic of Korea., Brennan G; Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, United States of America., Kan J; Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, United States of America., Meng S; Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, United States of America., Dozier A; Department of Public Health, University of Rochester Medical Center, Rochester, New York, United States of America., O'Connell A; Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, United States of America.
Jazyk:	angličtina
Zdroj:	PLOS digital health [PLOS Digit Health] 2022 Nov 23; Vol. 1 (11), pp. e0000148. Date of Electronic Publication: 2022 Nov 23 (Print Publication: 2022).
DOI:	10.1371/journal.pdig.0000148
Abstrakt:	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. Competing Interests: BC has financial stake in Medical Innovation and Technology. This company seeks to bring ultrasound into rural areas. ZH is an employee of Samsung Medison. MHP is an employee of Samsung Electronics. (Copyright: © 2022 Marini et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze:	MEDLINE
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