1. Automated remote speech-based testing of individuals with cognitive decline: Bayesian agreement of transcription accuracy.
- Author
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König A, Köhler S, Tröger J, Düzel E, Glanz W, Butryn M, Mallick E, Priller J, Altenstein S, Spottke A, Kimmich O, Falkenburger B, Osterrath A, Wiltfang J, Bartels C, Kilimann I, Laske C, Munk MH, Roeske S, Frommann I, Hoffmann DC, Jessen F, Wagner M, Linz N, and Teipel S
- Abstract
Introduction: We investigated the agreement between automated and gold-standard manual transcriptions of telephone chatbot-based semantic verbal fluency testing., Methods: We examined 78 cases from the Screening over Speech in Unselected Populations for Clinical Trials in AD (PROSPECT-AD) study, including cognitively normal individuals and individuals with subjective cognitive decline, mild cognitive impairment, and dementia. We used Bayesian Bland-Altman analysis of word count and the qualitative features of semantic cluster size, cluster switches, and word frequencies., Results: We found high levels of agreement for word count, with a 93% probability of a newly observed difference being below the minimally important difference. The qualitative features had fair levels of agreement. Word count reached high levels of discrimination between cognitively impaired and unimpaired individuals, regardless of transcription mode., Discussion: Our results support the use of automated speech recognition particularly for the assessment of quantitative speech features, even when using data from telephone calls with cognitively impaired individuals in their homes., Highlights: High levels of agreement were found between automated and gold-standard manual transcriptions of telephone chatbot-based semantic verbal fluency testing, particularly for word count.The qualitative features had fair levels of agreement.Word count reached high levels of discrimination between cognitively impaired and unimpaired individuals, regardless of transcription mode.Automated speech recognition for the assessment of quantitative and qualitative speech features, even when using data from telephone calls with cognitively impaired individuals in their homes, seems feasible and reliable., Competing Interests: A.K., J.T., E.M., and N.L. are employed by ki:elements. N.L. and J.T. hold shares in the company ki:elements. S.K. has received unrestricted funding from the Alzheimer Drug Discovery Foundation and lecture fees from Eisai. B.F. has received funding from the Deutsche Forschungsgemeinschaft. J.W. has received funding from the BMBF; consulting fees from Immungenetics, Noselab, and Roboscreen; and lecture fees from Beijing Yibai Science and Technology Ltd., Gloryren, Janssen Cilag, Pfizer, Med Update GmbH, Roche Pharma, and Lilly. J.W. participated on a data safety monitoring board or advisory board of Biogen, Abbott, Boehringer Ingelheim, Lilly, MSD Sharp & Dohme, and Roche. C.B. received funding from the German Alzheimer Association and lecture fees from Lilly, Roche Pharma, and Eisai. S.T. participated on scientific advisory boards of Roche Pharma AG, Biogen, Lilly, and Eisai, and received lecture fees from Lilly and Eisai. A.O., E.D., W.G., M.B., J.P., S.A., A.S., O.K., I.K., C.L., M.M., S.R., I.F., D.H., F.J., and M.W. have nothing to disclose. Author disclosures are available in the supporting information., (© 2024 The Author(s). Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)
- Published
- 2024
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