AI Driven Digital Cognitive Twin for Brain Health Monitoring

Cognitive health is usually checked in clinics. That makes it hard to keep track of changes over the time. This paper introduces CogTwin, a web-based platform where a person can complete five cognitive tasks—reaction time, memory recall, pattern recognition, attention span and decision making—and see how their own results change over the time. Of comparing the user to population averages the platform creates the personal baseline using data from the user’s past sessions. This baseline acts like the cognitive twin. Each new session is compared to this baseline using a z-scores. A regression line shows the trend and a TensorFlow.js neural network predicts a what the session’s scores might be. A language model gives explanations in the everyday language following rules that keep it from making any diagnostic claims. The system is built using React, Node.js, Express and the MongoDB Atlas. User accounts are secured with the bcrypt and JWT. At this point CogTwin has only been tested as the software. No real users have been involved yet so the paper does not say that it measures the cognition accurately. The model and the rules for detecting an anomalies are described in full. A pilot study protocol is also included, covering test–retest reliability, comparison, with the MoCA and prediction error compared to the baseline. CogTwin is meant to help with monitoring a changes not to diagnose any condition.

digital twin, cognitive monitoring, brain health, TensorFlow.js, z-score, linear regression

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