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Machine Learning-Based Cognitive Assessment With The Autonomous Cognitive Examination: Randomized Controlled Trial.
Calvin Howard1,2,3,4, Amy Johnson5, Sheena Baratono1,2
1Center for Brain Circuit Therapeutics, Brigham & Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA, 02215, United States.
The Autonomous Cognitive Examination (ACoE) reliably assesses cognitive function and can screen patients, similar to traditional tests. This digital tool offers a scalable solution for cognitive phenotyping in clinical settings.
Area of Science:
- Neurology
- Cognitive Science
- Digital Health
Background:
- Rising dementia prevalence requires scalable cognitive assessment solutions.
- The Autonomous Cognitive Examination (ACoE) is a foundational tool for cognitive symptom phenotyping.
- External validation of ACoE in clinical populations and its screening utility are currently unknown.
Purpose of the Study:
- To externally validate the Autonomous Cognitive Examination (ACoE) for reliable cognitive phenotyping.
- To assess the ACoE's efficacy as a screening tool compared to standard paper-based cognitive tests.
Main Methods:
- A randomized crossover study involved 46 neurological patients comparing ACoE to paper-based tests (ACE-3 or MoCA).
- Evaluated interrater reliability for overall and domain-specific cognitive scores.
- Assessed ACoE's ability to classify patients similarly to established cognitive assessments.
Main Results:
- ACoE demonstrated high interrater reliability for overall cognitive scores (ICC=0.89) and specific domains (Attention: ICC=0.74, Language: ICC=0.89, Memory: ICC=0.91, Fluency: ICC=0.74, Visuospatial: ICC=0.78).
- ACoE effectively screened patients, achieving high diagnostic similarity to paper-based tests (AUC=0.96).
Conclusions:
- The Autonomous Cognitive Examination (ACoE) reliably phenotypes cognitive symptoms in patients.
- ACoE is effective for screening patients with cognitive impairments.
- Future applications may include diagnosing specific cognitive etiologies.
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