Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification

Alzheimer's disease is the primary cause of dementia worldwide, with an increasing morbidity burden that may outstrip diagnosis and management capacity as the population ages. Current methods integrate patient history, neuropsychological testing and MRI to identify likely cases, yet effective practices remain variably applied and lacking in sensitivity and specificity. Here we report an interpretable deep learning strategy that delineates unique Alzheimer's disease signatures from multimodal inputs of MRI, age, gender, and Mini-Mental State Examination score. Our framework linked a fully convolutional network, which constructs high resolution maps of disease probability from local brain structure to a…

• AH
  American Heart Association

  Awards: N01-HC-25195, 17SDG33670323, HHSN268201500001I
• NI
  National Institutes of Health

  Awards: AG013846, 1UL1TR001430, R01-AG016495, R56-AG062109, AG008122, R01-AG033040, N01-HC-25195, P30-AG013846, HHSN268201500001I
• CA
  Clinical and Translational Science Institute, Boston University

  Award: 1UL1TR001430
• NC
  National Center for Advancing Translational Sciences

  Award: 1UL1TR001430