Fully automated cognitive screening tool based on assessment of speech and language.

alzheimer's disease cognition dementia speech telemetry

Journal

Journal of neurology, neurosurgery, and psychiatry
ISSN: 1468-330X
Titre abrégé: J Neurol Neurosurg Psychiatry
Pays: England
ID NLM: 2985191R

Informations de publication

Date de publication:
20 Nov 2020
Historique:
received: 02 12 2019
revised: 16 07 2020
accepted: 22 09 2020
entrez: 21 11 2020
pubmed: 22 11 2020
medline: 22 11 2020
Statut: aheadofprint

Résumé

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. 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. 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%. 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.

Identifiants

pubmed: 33219045
pii: jnnp-2019-322517
doi: 10.1136/jnnp-2019-322517
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Medical Research Council
ID : MC_PC_17176
Pays : United Kingdom

Informations de copyright

© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.

Déclaration de conflit d'intérêts

Competing interests: None declared.

Auteurs

Ronan Peter Daniel O'Malley (RPD)

Neuroscience, The University of Sheffield, Sheffield, Sheffield, UK.

Bahman Mirheidari (B)

Department of Computer Science, The University of Sheffield, Sheffield, Sheffield, UK.

Kirsty Harkness (K)

Neuroscience, The University of Sheffield, Sheffield, Sheffield, UK.

Markus Reuber (M)

Neuroscience, The University of Sheffield, Sheffield, Sheffield, UK.

Annalena Venneri (A)

Neuroscience, The University of Sheffield, Sheffield, Sheffield, UK.

Traci Walker (T)

Human Communication Sciences, The University of Sheffield, Sheffield, Sheffield, UK.

Heidi Christensen (H)

Department of Computer Science, The University of Sheffield, Sheffield, Sheffield, UK.

Dan Blackburn (D)

Neuroscience, The University of Sheffield, Sheffield, Sheffield, UK d.blackburn@sheffield.ac.uk.

Classifications MeSH