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

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)292-302
Number of pages11
JournalAdvances in Medical Sciences
Volume64
Issue number2
Early online date2 Apr 2019
DOIs
DateSubmitted - 9 Jul 2018
DateAccepted/In press - 19 Mar 2019
DateE-pub ahead of print - 2 Apr 2019
DatePublished (current) - 1 Sep 2019

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. Patients & Methods: An artificial intelligence algorithm has been developed to retrospectively analyse the patient journeys of 47 individuals diagnosed with FTD (age range 52–80). The algorithm analysed the efficiency of patient pathways by utilizing a reward signal of ‒1 to +1 to assess the symptoms, imaging techniques, and clinical judgement in both behavioural and language variants of the disease. Results: On average, every patient was subjected to 4.93 investigations, of which 67.4% were radiological scans. From first presentation it took on average 939 days for a firm diagnosis. The mean time between appointments was 204 days, and the average patient had their diagnosis altered 7.37 times during their journey. The algorithm proposed improvements by evaluating the interventions that resulted in a decreased reward signal to both the individual and the population as a whole. Conclusions: The study proves that the algorithm can efficiently guide clinical practice and improve the accuracy of the diagnosis of FTD whilst making the process of auditing faster and more economically viable.

    Research areas

  • Artificial intelligence, Cognitive disorders and dementia, Computational neurology, Frontotemporal dementia, Imaging

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Documents

  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the accepted author manuscript (AAM). The final published version (version of record) is available online via Elsevier at https://doi.org/10.1016/j.advms.2019.03.002 . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 384 KB, PDF document

    Embargo ends: 2/04/20

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    Licence: CC BY-NC-ND

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