O'Malley, R.P.D., Mirheidari, B., Harkness, K. et al. (5 more authors) (2021) Fully automated cognitive screening tool based on assessment of speech and language. Journal of Neurology, Neurosurgery & Psychiatry, 92 (1). pp. 12-15. ISSN 0022-3050
Abstract
Introduction: Recent years have seen an almost sevenfold rise in referrals to specialist memory clinics. This has been associated with an increased proportion of patients referred with functional cognitive disorder (FCD), that is, non-progressive cognitive complaints. These patients are likely to benefit from a range of interventions (eg, psychotherapy) distinct from the requirements of patients with neurodegenerative cognitive disorders. We have developed a fully automated system, ‘CognoSpeak’, which enables risk stratification at the primary–secondary care interface and ongoing monitoring of patients with memory concerns.
Methods: We recruited 15 participants to each of four groups: Alzheimer’s disease (AD), mild cognitive impairment (MCI), FCD and healthy controls. Participants responded to 12 questions posed by a computer-presented talking head. Automatic analysis of the audio and speech data involved speaker segmentation, automatic speech recognition and machine learning classification.
Results: CognoSpeak could distinguish between participants in the AD or MCI groups and those in the FCD or healthy control groups with a sensitivity of 86.7%. Patients with MCI were identified with a sensitivity of 80%.
Discussion: Our fully automated system achieved levels of accuracy comparable to currently available, manually administered assessments. Greater accuracy should be achievable through further system training with a greater number of users, the inclusion of verbal fluency tasks and repeat assessments. The current data supports CognoSpeak’s promise as a screening and monitoring tool for patients with MCI. Pending confirmation of these findings, it may allow clinicians to offer patients at low risk of dementia earlier reassurance and relieve pressures on specialist memory services.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Author(s). This is an author-produced version of a paper subsequently published in Journal of Neurology, Neurosurgery and Psychiatry. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Health and Related Research (Sheffield) > ScHARR - Sheffield Centre for Health and Related Research The University of Sheffield > Sheffield Teaching Hospitals |
Funding Information: | Funder Grant number Medical Research Council N/A |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 21 Jan 2021 10:40 |
Last Modified: | 25 Jan 2021 13:57 |
Status: | Published |
Publisher: | BMJ Publishing Group |
Refereed: | Yes |
Identification Number: | 10.1136/jnnp-2019-322517 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:169297 |
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Licence: CC-BY-NC 4.0