The Autonomous Cognitive Examination: Machine-Learning Based Cognitive Examination

Structured Abstract INTRODUCTION The rising prevalence of dementia necessitates a scalable solution to cognitive screening and diagnosis. Digital cognitive assessments offer a solution but lack the extensive validation of older paper-based tests. Creating a digital cognitive assessment which recreates a paper-based assessment could have the strengths of both tests. METHODS We developed the Autonomous Cognitive Examination (ACoE), a fully remote and automated digital cognitive assessment which recreates the assessments of paper-based tests. We assessed its ability to reproduce entire cognitive screens in a comparison cohort (n = 35), and the ability to reproduce overall diagnoses with an additional validation cohort (n = 11). RESULTS The ACoE reproduced overall cognitive assessments with excellent agreement (intraclass correlation coefficient = 0.89) and reproduced overall diagnoses with excellent fidelity (area under the curve = 0.96). DISCUSSION The ACoE may reliably reproduce the evaluations of the ACE-3, which may help in accessible evaluation of patient cognition. Assessment in larger population of patients with specific diseases will be necessary to determine usefulness.