A novel deep learning-based brain age prediction framework for routine clinical MRI scans.
Authors
Affiliations (9)
Affiliations (9)
- BeauBrain Healthcare, Inc, Seoul, South Korea.
- CHA University School of Medicine, Seongnam, South Korea.
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, South Korea.
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea.
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, South Korea.
- Department of Neurology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea.
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea.
- BeauBrain Healthcare, Inc, Seoul, South Korea. [email protected].
Abstract
Physiological brain aging is associated with cognitive impairment and neuroanatomical changes. Brain age prediction of routine clinical 2D brain MRI scans were understudied and often unsuccessful. We developed a novel brain age prediction framework for clinical 2D T1-weighted MRI scans using a deep learning-based model trained with research grade 3D MRI scans mostly from publicly available datasets (N = 8681; age = 51.76 ± 21.74). Our model showed accurate and fast brain age prediction on clinical 2D MRI scans from cognitively unimpaired (CU) subjects (N = 175) with MAE of 2.73 years after age bias correction (Pearson's r = 0.918). Brain age gap of Alzheimer's disease (AD) subjects was significantly greater than CU subjects (p < 0.001) and increase in brain age gap was associated with disease progression in both AD (p < 0.05) and Parkinson's disease (p < 0.01). Our framework can be extended to other MRI modalities and potentially applied to routine clinical examinations, enabling early detection of structural anomalies and improve patient outcome.