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A new open-access platform for measuring and sharing mTBI data.
Domel, August G; Raymond, Samuel J; Giordano, Chiara; Liu, Yuzhe; Yousefsani, Seyed Abdolmajid; Fanton, Michael; Cecchi, Nicholas J; Vovk, Olga; Pirozzi, Ileana; Kight, Ali; Avery, Brett; Boumis, Athanasia; Fetters, Tyler; Jandu, Simran; Mehring, William M; Monga, Sam; Mouchawar, Nicole; Rangel, India; Rice, Eli; Roy, Pritha; Sami, Sohrab; Singh, Heer; Wu, Lyndia; Kuo, Calvin; Zeineh, Michael; Grant, Gerald; Camarillo, David B.
Afiliação
  • Domel AG; Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
  • Raymond SJ; Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA. sjray@stanford.edu.
  • Giordano C; Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
  • Liu Y; Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
  • Yousefsani SA; Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
  • Fanton M; Department of Mechanical Engineering, Stanford University, Stanford, CA, 94305, USA.
  • Cecchi NJ; Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
  • Vovk O; General Dynamics Information Technology, 3150 Fairview Park Drive, Falls Church, VA, 22042, USA.
  • Pirozzi I; Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
  • Kight A; Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
  • Avery B; Stanford Center for Clinical Research, Stanford University, Stanford, CA, 94305, USA.
  • Boumis A; Stanford Center for Clinical Research, Stanford University, Stanford, CA, 94305, USA.
  • Fetters T; General Dynamics Information Technology, 3150 Fairview Park Drive, Falls Church, VA, 22042, USA.
  • Jandu S; Stanford Center for Clinical Research, Stanford University, Stanford, CA, 94305, USA.
  • Mehring WM; Stanford Center for Clinical Research, Stanford University, Stanford, CA, 94305, USA.
  • Monga S; Intel Sports, Intel Corporation, Santa Clara, CA, 95054, USA.
  • Mouchawar N; Asterix Inc, Toronto, ON, M4B 1B3, Canada.
  • Rangel I; Department of Radiology, Stanford University, 300 Pasteur Dr, MC 5105, Stanford, CA, USA.
  • Rice E; Stanford Center for Clinical Research, Stanford University, Stanford, CA, 94305, USA.
  • Roy P; Stanford Center for Clinical Research, Stanford University, Stanford, CA, 94305, USA.
  • Sami S; Stanford Center for Clinical Research, Stanford University, Stanford, CA, 94305, USA.
  • Singh H; Stanford Center for Clinical Research, Stanford University, Stanford, CA, 94305, USA.
  • Wu L; Stanford Center for Clinical Research, Stanford University, Stanford, CA, 94305, USA.
  • Kuo C; Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
  • Zeineh M; Department of Mechanical Engineering, University of British Columbia, 6250 Applied Science Lane, Vancouver, BC, V6T 1Z4, Canada.
  • Grant G; Department of Mechanical Engineering, Stanford University, Stanford, CA, 94305, USA.
  • Camarillo DB; Department of Biomedical Engineering, University of British Columbia, Lower Mall Research Station, Vancouver, BC, 2259V6T1Z4, Canada.
Sci Rep ; 11(1): 7501, 2021 04 05.
Article em En | MEDLINE | ID: mdl-33820939
ABSTRACT
Despite numerous research efforts, the precise mechanisms of concussion have yet to be fully uncovered. Clinical studies on high-risk populations, such as contact sports athletes, have become more common and give insight on the link between impact severity and brain injury risk through the use of wearable sensors and neurological testing. However, as the number of institutions operating these studies grows, there is a growing need for a platform to share these data to facilitate our understanding of concussion mechanisms and aid in the development of suitable diagnostic tools. To that end, this paper puts forth two contributions (1) a centralized, open-access platform for storing and sharing head impact data, in collaboration with the Federal Interagency Traumatic Brain Injury Research informatics system (FITBIR), and (2) a deep learning impact detection algorithm (MiGNet) to differentiate between true head impacts and false positives for the previously biomechanically validated instrumented mouthguard sensor (MiG2.0), all of which easily interfaces with FITBIR. We report 96% accuracy using MiGNet, based on a neural network model, improving on previous work based on Support Vector Machines achieving 91% accuracy, on an out of sample dataset of high school and collegiate football head impacts. The integrated MiG2.0 and FITBIR system serve as a collaborative research tool to be disseminated across multiple institutions towards creating a standardized dataset for furthering the knowledge of concussion biomechanics.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Algoritmos / Acesso à Informação / Disseminação de Informação / Lesões Encefálicas Traumáticas Idioma: En Ano de publicação: 2021 Tipo de documento: Article
Texto completo
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XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Algoritmos / Acesso à Informação / Disseminação de Informação / Lesões Encefálicas Traumáticas Idioma: En Ano de publicação: 2021 Tipo de documento: Article