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Automatic identification of atypical clinical fMRI results.

J Martijn JansmaGeert-Jan RuttenLenny E RamseyT J SnijdersAlberto BizziKatharina RosengarthFrank Dodoo-SchittkoElke HattingenMar Jiménez de la PeñaGord von CampeMargit JehnaNick F Ramsey
Published in: Neuroradiology (2020)
This study supports feasibility of TMA for objective identification of atypical activation patterns for motor and verb generation fMRI protocols. TMA can facilitate the use and evaluation of clinical fMRI in hospital settings that have limited access to fMRI experts. In a clinical setting, this method could be applied to automatically flag fMRI scans showing atypical activation patterns for further investigation to determine whether atypicality is caused by poor scan data quality or abnormal functional topography.
Keyphrases
  • resting state
  • functional connectivity
  • computed tomography
  • healthcare
  • machine learning
  • deep learning
  • electronic health record
  • quality improvement
  • acute care