A machine-learning algorithm for predicting brain age using Rey-Osterrieth complex figure tests of healthy participants

Abstract

Objective: Neuropsychologists widely use the Rey-Osterrieth complex figure test (RCFT) as part of neuropsychological test batteries to evaluate cognitive function and assess constructional ability, with age being the most significant factor. Our study investigated a supervised machine learning (ML) algorithm to predict brain age gap using RCFT drawings from the healthy elderly community for early dementia detection. Participants and Methods: RCFT drawings from 1,970 healthy subjects (ages 45-90 years) were collected from the Korean elderly community. We recorded subject demographic information including: age, gender, and education level. We trained the ML model with RCFT copies, immediate recall, delayed recall, and education level of the healthy subjects using CNN regression algorithm from Keras (https://keras.io/) with the Tensorflow library. Results: The performance was evaluated by the mean absolute error (MAE) and root mean squared error (RMSE) between the predicted age and the chronological age based on a test dataset of 300 healthy subjects. The CNN regression model achieved an MAE of 7.2 years in predicting the brain age gap of the subjects, with an RMSE of 8.9 years. Conclusion: The MAE and RMSE accuracies of the CNN regression model predicting the brain age gap showed the model could be a potential biomarker for individual brain aging and a cost-effective method for early dementia detection.