Towards Scalable Remote Assessment of Mild Cognitive Impairment Via Multimodal Dialog

Roesler, Oliver; Liscombe, Jackson; Neumann, Michael; Kothare, Hardik; Hosamath, Abhishek; Arbatti, Lakshmi; Habberstad, Doug; Suendermann-Oeft, Christiane; Bartlett, Meredith; Zhang, Cathy; Sukhdev, Nikhil; Wilms, Kolja; Badathala, Anusha; Istas, Sandrine; Ruhmel, Steve; Hansen, Bryan; Hannan, Madeline; Henley, David; Wallace, Arthur; Shoulson, Ira; Suendermann-Oeft, David; Ramanarayanan, Vikram

doi:10.21437/Interspeech.2024-1541

Towards Scalable Remote Assessment of Mild Cognitive Impairment Via Multimodal Dialog

Oliver Roesler, Jackson Liscombe, Michael Neumann, Hardik Kothare, Abhishek Hosamath, Lakshmi Arbatti, Doug Habberstad, Christiane Suendermann-Oeft, Meredith Bartlett, Cathy Zhang, Nikhil Sukhdev, Kolja Wilms, Anusha Badathala, Sandrine Istas, Steve Ruhmel, Bryan Hansen, Madeline Hannan, David Henley, Arthur Wallace, Ira Shoulson, David Suendermann-Oeft, Vikram Ramanarayanan

Early assessment of mild cognitive impairment (MCI) has the potential to expedite interventions and slow disease progress for people at risk of developing dementia. We investigate the feasibility of administering remote assessments of speech, orofacial and cognitive function to an elderly population with MCI via a cloud-based conversational remote monitoring platform, and the utility of automatically extracted multimodal biomarkers and self-reported problems in identifying MCI patients. We analyzed data from 90 MCI patients and 91 controls who each completed two assessments. 90% of participants reported excellent engagement and liked their overall user experience. Furthermore, combining multiple facial, speech and cognitive markers performed best at distinguishing MCI patients from controls with an AUC of 0.75 using a support vector machine classifier. Finally, we found that MCI patients reported significantly more problems related to memory, falls, anxiety and speech than controls.