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056 Exploring the feasibility of automating verbal fluency tasks for cognitive assessment: data collection and analysis
  1. Ronan O’Malley1,
  2. Eric Brook1,
  3. Alice Lewis2,
  4. Isobel Sonksen2,
  5. Bahman Mirhedari3,
  6. Annalena Venneri4,
  7. Heidi Christensen3,
  8. Daniel Blackburn1
  1. 1Sheffield Institute for Translational Neuroscience
  2. 2University of Sheffield School of Medicine
  3. 3Centre for Assistive Technology and Connected Healthcare, University of Sheffield
  4. 4Academic Unit of Neurology, University of Sheffield


Introduction Fluency tests are widely employed in the assessment of cognition. We assessed the feasibility of integrating these tasks into an automated cognitive assessment tool; the ‘Digital Doctor’.

Methods 15 each of healthy controls (HC), Alzheimer’s disease (AD) and mild cognitive impairment (MCI) were recruited. Participants named as many words beginning with P (phonemic fluency) and as many animals (semantic fluency) as they could within one minute.

Results Manual Analysis: HC named the same number of words for both tasks compared to normative data. Patients with AD and MCI were able to complete the task and Z-scores showed significant decreases in category fluency between the AD and HC groups, -2.25.

Automated Analysis: Automatic Speech recognition (ASR) was 77.9% accurate for semantic testing and 64.8% for phonemic testing, ASR overestimated the mean scores in the AD and MCI groups. Comparison of phonemic fluency in AD compared to HC yielded a significant Z score, -2.17.

Conclusions Automated collection of fluency data is feasible and comparable to manually collected data. The over-estimation of the number of correct responses provided by the ASR will be improved with more data along with training of the Machine Learning algorithms to recognise certain, expected words.

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