Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning

Kainz, B., Heinrich, M.P., Makropoulos, A. et al. (11 more authors) (2021) Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning. npj Digital Medicine, 4. 137. ISSN 2398-6352

Abstract

Metadata

Authors/Creators:
  • Kainz, B.
  • Heinrich, M.P.
  • Makropoulos, A.
  • Oppenheimer, J.
  • Mandegaran, R.
  • Sankar, S.
  • Deane, C.
  • Mischkewitz, S.
  • Al-Noor, F.
  • Rawdin, A.C.
  • Ruttloff, A.
  • Stevenson, M.D. ORCID logo https://orcid.org/0000-0002-3099-9877
  • Klein-Weigel, P.
  • Curry, N.
Copyright, Publisher and Additional Information: © The Author(s) 2021. 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.
Dates:
  • Accepted: 6 August 2021
  • Published (online): 15 September 2021
  • Published: 15 September 2021
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Health and Related Research (Sheffield) > Sheffield Centre for Health and Related Research
Depositing User: Symplectic Sheffield
Date Deposited: 22 Sep 2021 13:52
Last Modified: 27 Sep 2021 21:05
Status: Published
Publisher: Springer Science and Business Media LLC
Refereed: Yes
Identification Number: https://doi.org/10.1038/s41746-021-00503-7
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