Diagnostic accuracy of frontotemporal dementia. An artificial intelligence-powered study of symptoms, imaging and clinical judgement.

Journal: Advances in medical sciences
PMID: 30952029

Abstract

PURPOSE: Frontotemporal dementia (FTD) is a neurodegenerative disorder associated with a poor prognosis and a substantial reduction in quality of life. The rate of misdiagnosis of FTD is very high, with patients often waiting for years without a firm diagnosis. This study investigates the current state of the misdiagnosis of FTD using a novel artificial intelligence-based algorithm.

Authors

  • Maksymilian A Brzezicki
    Bristol Institute of Clinical Neurosciences, University of Bristol, Southmead Hospital, Bristol, UK. Electronic address: mbrzezicki@neurologicalsociety.org.
  • Matthew D Kobetić
    Faculty of Health Sciences, University of Bristol, Bristol, UK.
  • Sandra Neumann
    Department of Physiology and Pharmacology, Clinical Research and Imaging Centre, University of Bristol, Bristol, UK.
  • Catherine Pennington
    Bristol Institute of Clinical Neurosciences, University of Bristol, Southmead Hospital, Bristol, UK.

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