A novel deep learning-based brain age prediction framework for routine clinical MRI scans.

Journal: npj aging
Published Date:

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.

Authors

  • Hyunwoong Kim
    Clinical Trial Center, Inje University College of Medicine, Haeundae Paik Hospital, Busan, South Korea.
  • Seongbeom Park
    Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
  • Sang Won Seo
    Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea. sangwonseo@empal.com.
  • Duk L Na
    Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea.
  • Hyemin Jang
    Department of Neurology, Sungkyunkwan University of School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. hmjang57@gmail.com.
  • Jun Pyo Kim
    Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
  • Hee Jin Kim
    Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea.
  • Sung Hoon Kang
    Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Kichang Kwak
    Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.

Keywords

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