Sumary of Deep neural network model can accurately predict the brain age of healthy patients:
- Epilepsy and seizure disorders, stroke, elevated markers of sleep-disordered breathing (i.e., apnea-hypopnea index and arousal index), and low sleep efficiency..
- The study also found that patients with diabetes, depression, severe excessive daytime sleepiness, hypertension, and/or memory and concentration problems showed, on average, an elevated Brain Age Index compared with the healthy population sample..
- According to the authors, the results demonstrate that these health conditions are associated with deviations of one’s predicted age from one’s chronological age..
- While clinicians can only grossly estimate or quantify the age of a patient based on their EEG, this study shows an artificial intelligence model can predict a patient’s age with high precision..
- The model’s precision enables shifts in the predicted age from the chronological age to express correlations with major disease families and comorbidities..
- This presents the potential for identifying novel clinical phenotypes that exist within physiological signals utilizing AI model deviations.”.
- The model was trained on 126,241 sleep studies, validated on 6,638 studies, and tested on a holdout set of 1,172 studies..
- Brain age was assessed by subtracting individuals’ chronological age from their EEG-predicted age (i.e., Brain Age Index), and then taking the absolute value of this variable (i.e., Absolute Brain Age Index)….