Alexandra König1, Nicklas Linz2, Johannes Tröger2, Maria Wolters3, Jan Alexandersson2, Phillipe Robert1. 1. Memory Clinic, Association IA, CoBTek Lab, CHU Université Côte d'Azur, Nice, France. 2. German Research Center for Artificial Intelligence (DFKI), Saarbrücken, Germany. 3. School of Informatics, University of Edinburgh, Edinburgh, United Kingdom.
Abstract
BACKGROUND: Semantic verbal fluency (SVF) tests are routinely used in screening for mild cognitive impairment (MCI). In this task, participants name as many items as possible of a semantic category under a time constraint. Clinicians measure task performance manually by summing the number of correct words and errors. More fine-grained variables add valuable information to clinical assessment, but are time-consuming. Therefore, the aim of this study is to investigate whether automatic analysis of the SVF could provide these as accurate as manual and thus, support qualitative screening of neurocognitive impairment. METHODS: SVF data were collected from 95 older people with MCI (n = 47), Alzheimer's or related dementias (ADRD; n = 24), and healthy controls (HC; n = 24). All data were annotated manually and automatically with clusters and switches. The obtained metrics were validated using a classifier to distinguish HC, MCI, and ADRD. RESULTS: Automatically extracted clusters and switches were highly correlated (r = 0.9) with manually established values, and performed as well on the classification task separating HC from persons with ADRD (area under curve [AUC] = 0.939) and MCI (AUC = 0.758). CONCLUSION: The results show that it is possible to automate fine-grained analyses of SVF data for the assessment of cognitive decline.
BACKGROUND: Semantic verbal fluency (SVF) tests are routinely used in screening for mild cognitive impairment (MCI). In this task, participants name as many items as possible of a semantic category under a time constraint. Clinicians measure task performance manually by summing the number of correct words and errors. More fine-grained variables add valuable information to clinical assessment, but are time-consuming. Therefore, the aim of this study is to investigate whether automatic analysis of the SVF could provide these as accurate as manual and thus, support qualitative screening of neurocognitive impairment. METHODS: SVF data were collected from 95 older people with MCI (n = 47), Alzheimer's or related dementias (ADRD; n = 24), and healthy controls (HC; n = 24). All data were annotated manually and automatically with clusters and switches. The obtained metrics were validated using a classifier to distinguish HC, MCI, and ADRD. RESULTS: Automatically extracted clusters and switches were highly correlated (r = 0.9) with manually established values, and performed as well on the classification task separating HC from persons with ADRD (area under curve [AUC] = 0.939) and MCI (AUC = 0.758). CONCLUSION: The results show that it is possible to automate fine-grained analyses of SVF data for the assessment of cognitive decline.
Authors: Sarah Gregory; Nicklas Linz; Alexandra König; Kai Langel; Hannah Pullen; Saturnino Luz; John Harrison; Craig W Ritchie Journal: BMJ Open Date: 2022-03-15 Impact factor: 2.692
Authors: Stefan Teipel; Alexandra König; Jesse Hoey; Jeff Kaye; Frank Krüger; Julie M Robillard; Thomas Kirste; Claudio Babiloni Journal: Alzheimers Dement Date: 2018-06-21 Impact factor: 21.566