Cargando…

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

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Drukker, Lior, Sharma, Harshita, Droste, Richard, Alsharid, Mohammad, Chatelain, Pierre, Noble, J. Alison, Papageorghiou, Aris T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8266837/
https://www.ncbi.nlm.nih.gov/pubmed/34238950
http://dx.doi.org/10.1038/s41598-021-92829-1
_version_ 1783720016644931584
author Drukker, Lior
Sharma, Harshita
Droste, Richard
Alsharid, Mohammad
Chatelain, Pierre
Noble, J. Alison
Papageorghiou, Aris T.
author_facet Drukker, Lior
Sharma, Harshita
Droste, Richard
Alsharid, Mohammad
Chatelain, Pierre
Noble, J. Alison
Papageorghiou, Aris T.
author_sort Drukker, Lior
collection PubMed
description 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 Online
Article
Text
id pubmed-8266837
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-82668372021-07-12 Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video Drukker, Lior Sharma, Harshita Droste, Richard Alsharid, Mohammad Chatelain, Pierre Noble, J. Alison Papageorghiou, Aris T. Sci Rep Article 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. Nature Publishing Group UK 2021-07-08 /pmc/articles/PMC8266837/ /pubmed/34238950 http://dx.doi.org/10.1038/s41598-021-92829-1 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Drukker, Lior
Sharma, Harshita
Droste, Richard
Alsharid, Mohammad
Chatelain, Pierre
Noble, J. Alison
Papageorghiou, Aris T.
Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video
title 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_short 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8266837/
https://www.ncbi.nlm.nih.gov/pubmed/34238950
http://dx.doi.org/10.1038/s41598-021-92829-1
work_keys_str_mv AT drukkerlior transformingobstetricultrasoundintodatascienceusingeyetrackingvoicerecordingtransducermotionandultrasoundvideo
AT sharmaharshita transformingobstetricultrasoundintodatascienceusingeyetrackingvoicerecordingtransducermotionandultrasoundvideo
AT drosterichard transformingobstetricultrasoundintodatascienceusingeyetrackingvoicerecordingtransducermotionandultrasoundvideo
AT alsharidmohammad transformingobstetricultrasoundintodatascienceusingeyetrackingvoicerecordingtransducermotionandultrasoundvideo
AT chatelainpierre transformingobstetricultrasoundintodatascienceusingeyetrackingvoicerecordingtransducermotionandultrasoundvideo
AT noblejalison transformingobstetricultrasoundintodatascienceusingeyetrackingvoicerecordingtransducermotionandultrasoundvideo
AT papageorghiouarist transformingobstetricultrasoundintodatascienceusingeyetrackingvoicerecordingtransducermotionandultrasoundvideo