Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video

Abstract Ultrasound is the primary modality for obstetric imaging and is highly sonographer dependent. Long training period, insufficient recruitment and poor retention of sonographers are among the global challenges in the expansion of ultrasound use. For the past several decades, technical advance...

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Autores principales:	Lior Drukker, Harshita Sharma, Richard Droste, Mohammad Alsharid, Pierre Chatelain, J. Alison Noble, Aris T. Papageorghiou
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1d93af074cd94bdbaddb39cec3fed5fe
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spelling	oai:doaj.org-article:1d93af074cd94bdbaddb39cec3fed5fe2021-12-02T15:40:00ZTransforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video10.1038/s41598-021-92829-12045-2322https://doaj.org/article/1d93af074cd94bdbaddb39cec3fed5fe2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92829-1https://doaj.org/toc/2045-2322Abstract Ultrasound is the primary modality for obstetric imaging and is highly sonographer dependent. Long training period, insufficient recruitment and poor retention of sonographers are among the global challenges in the expansion of ultrasound use. For the past several decades, technical advancements in clinical obstetric ultrasound scanning have largely concerned improving image quality and processing speed. By contrast, sonographers have been acquiring ultrasound images in a similar fashion for several decades. The PULSE (Perception Ultrasound by Learning Sonographer Experience) project is an interdisciplinary multi-modal imaging study aiming to offer clinical sonography insights and transform the process of obstetric ultrasound acquisition and image analysis by applying deep learning to large-scale multi-modal clinical data. A key novelty of the study is that we record full-length ultrasound video with concurrent tracking of the sonographer’s eyes, voice and the transducer while performing routine obstetric scans on pregnant women. We provide a detailed description of the novel acquisition system and illustrate how our data can be used to describe clinical ultrasound. Being able to measure different sonographer actions or model tasks will lead to a better understanding of several topics including how to effectively train new sonographers, monitor the learning progress, and enhance the scanning workflow of experts.Lior DrukkerHarshita SharmaRichard DrosteMohammad AlsharidPierre ChatelainJ. Alison NobleAris T. PapageorghiouNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lior Drukker Harshita Sharma Richard Droste Mohammad Alsharid Pierre Chatelain J. Alison Noble Aris T. Papageorghiou Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video
description	Abstract Ultrasound is the primary modality for obstetric imaging and is highly sonographer dependent. Long training period, insufficient recruitment and poor retention of sonographers are among the global challenges in the expansion of ultrasound use. For the past several decades, technical advancements in clinical obstetric ultrasound scanning have largely concerned improving image quality and processing speed. By contrast, sonographers have been acquiring ultrasound images in a similar fashion for several decades. The PULSE (Perception Ultrasound by Learning Sonographer Experience) project is an interdisciplinary multi-modal imaging study aiming to offer clinical sonography insights and transform the process of obstetric ultrasound acquisition and image analysis by applying deep learning to large-scale multi-modal clinical data. A key novelty of the study is that we record full-length ultrasound video with concurrent tracking of the sonographer’s eyes, voice and the transducer while performing routine obstetric scans on pregnant women. We provide a detailed description of the novel acquisition system and illustrate how our data can be used to describe clinical ultrasound. Being able to measure different sonographer actions or model tasks will lead to a better understanding of several topics including how to effectively train new sonographers, monitor the learning progress, and enhance the scanning workflow of experts.
format	article
author	Lior Drukker Harshita Sharma Richard Droste Mohammad Alsharid Pierre Chatelain J. Alison Noble Aris T. Papageorghiou
author_facet	Lior Drukker Harshita Sharma Richard Droste Mohammad Alsharid Pierre Chatelain J. Alison Noble Aris T. Papageorghiou
author_sort	Lior Drukker
title	Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video
title_short	Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video
title_full	Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video
title_fullStr	Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video
title_full_unstemmed	Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video
title_sort	transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1d93af074cd94bdbaddb39cec3fed5fe
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Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video

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