Scalable diagnostic screening of mild cognitive impairment using AI dialogue agent.

Journal: Scientific reports
Published Date:

Abstract

The search for early biomarkers of mild cognitive impairment (MCI) has been central to the Alzheimer's Disease (AD) and dementia research community in recent years. To identify MCI status at the earliest possible point, recent studies have shown that linguistic markers such as word choice, utterance and sentence structures can potentially serve as preclinical behavioral markers. Here we present an adaptive dialogue algorithm (an AI-enabled dialogue agent) to identify sequences of questions (a dialogue policy) that distinguish MCI from normal (NL) cognitive status. Our AI agent adapts its questioning strategy based on the user's previous responses to reach an individualized conversational strategy per user. Because the AI agent is adaptive and scales favorably with additional data, our method provides a potential avenue for large-scale preclinical screening of neurocognitive decline as a new digital biomarker, as well as longitudinal tracking of aging patterns in the outpatient setting.

Authors

  • Fengyi Tang
    Department of Computer Science and Engineering, Michigan State University College of Engineering, East Lansing, USA.
  • Ikechukwu Uchendu
    Department of Computer Science and Engineering, Michigan State University College of Engineering, East Lansing, USA.
  • Fei Wang
    Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, United States.
  • Hiroko H Dodge
    Department Of Neurology at Harvard Medical School, Harvard University, Massachusetts General Hospital, 55 Fruit St, Boston, Massachusetts, 02114, United States of America.
  • Jiayu Zhou
    Department of Computer Science and Engineering, Michigan State University, Michigan, USA.