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Fully automated cognitive screening tool based on assessment of speech and language
  1. Ronan Peter Daniel O'Malley1,
  2. Bahman Mirheidari2,
  3. Kirsty Harkness1,
  4. Markus Reuber1,
  5. Annalena Venneri1,
  6. Traci Walker3,
  7. Heidi Christensen2,
  8. Dan Blackburn1
  1. 1Neuroscience, The University of Sheffield, Sheffield, Sheffield, UK
  2. 2Department of Computer Science, The University of Sheffield, Sheffield, Sheffield, UK
  3. 3Human Communication Sciences, The University of Sheffield, Sheffield, Sheffield, UK
  1. Correspondence to Dr Dan Blackburn, Neuroscience, The University of Sheffield, Sheffield S10 2TN, UK; d.blackburn{at}sheffield.ac.uk

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.

  • alzheimer's disease
  • cognition
  • dementia
  • speech
  • telemetry
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Footnotes

  • Contributors RPDO responsible for data collection and analysis as well as preparation and revision of manuscript. BM responsible for data analysis and review of manuscript. KH, MR, AV and TW responsible for contributing towards the direction of the research and making significant contributions towards manuscript. HC responsible for overseeing machine learning and artificial intelligence aspects of data analysis and reviewing/contributing towards the manuscript. DB responsible for oversight of project, dictating direction of research and reviewing/amending the manuscript.

  • Funding This work was supported in part by MRC CiC grant.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Ethics approval Ethical permission was granted by the NRES Committee South West-Central Bristol (Rec number 16/LO/0737) in May 2016.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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