Abstract: As an important biomarker of neural aging, the brain age reflects the integrity and health of the human brain. Accurate prediction of brain age could help to understand the underlying mechanism of neural aging. In this study, a cross-stratified ensemble learning algorithm with staking strategy was proposed to obtain brain age and the derived predicted age difference (PAD) using T1-weighted magnetic resonance imaging (MRI) data. The approach was characterized as by implementing two modules: one was three base learners of 3D-DenseNet, 3D-ResNeXt, 3D-Inception-v4; another was 14 secondary learners of liner regressions. To evaluate performance, our method was compared with single base learners, regular ensemble learning algorithms, and state-of-the-art (SOTA) methods. The results demonstrated that our proposed model outperformed others models, with three metrics of mean absolute error (MAE), root mean-squared error (RMSE), and coefficient of determination (R2) of 2.9405 years, 3.9458 years, and 0.9597, respectively. Furthermore, there existed significant differences in PAD among the three groups of normal control (NC), mild cognitive impairment (MCI) and Alzheimer’s disease (AD), with an increased trend across NC, MCI, and AD. It was concluded that the proposed algorithm could be effectively used in computing brain aging and PAD, and offering potential for early diagnosis and assessment of normal brain aging and AD.

Biography: Dr. Xufeng Yao works as a professor, doctoral supervisor, vice dean of the school of medical imaging, Shanghai University of Medicine and Health Sciences, China. Once, he received his bachelor in medical imaging from Shandong First Medical University, and graduated from Fudan University for his PhD in biomedical engineering, and was a post-doctoral student in optical engineering at University of Shanghai for Science and Technology, China. He was also appointed as a permanent member of the committee of life electronics branch of China institute of electronics society. His current research interest focuses on artificial intelligence in imaging and omics for medicine. He won about 10 funds of national science foundation of China, Shanghai natural science foundation, China postdoctoral foundation, innovation fund of Shanghai Education Commission, etc. Recently, he has published more than 40 academic papers and reviewed for some SCI journals and international conferences. Till now, he has trained above 20 graduate students.