MRI quantified perivascular space metrics as imaging biomarkers for assessing the severity of cognitive impairment and sleep disturbance in young adults with long-time mobile phone use through machine learning approaches

Abstract

Emerging evidence has linked long-time mobile phone use (LTMPU) with cognitive impairment and sleep issues, with MRI-detected enlarged perivascular spaces (EPVSs) serving as markers for these conditions. Our study seeks to develop predictive model using MRI-based PVS measurements and machine learning to assess cognitive impairment, subjective sleep quality, and excessive daytime sleepiness in young adults with LTMPU. Eighty-two participants were included, deep learning algorithms were used to segment EPVS lesions and extract quantitative metrics. Training and testing datasets were randomly assigned to perform radiomics analysis, where EPVS metrics combined with sex and age were used to select the most valuable features for model construction. Finally, a Gaussian process model was constructed based on six features for assessing cognitive impairment, yielding an AUC of 0.818 (95% confidence interval [CI] 0.610-1) in the testing dataset. For sleep quality and sleepiness, two decision tree (DT) models using six features achieved an AUC value of 0.826 (95% CI 0.616-1) and 0.875 (95% CI 0.718-1) in the testing dataset respectively. Our study leveraged MRI-based PVS metrics and machine learning to assess the severity of cognitive impairment and sleep problems in young adults with LTMPU, and sheds light on a potential link between PVS and sleepiness.