Deep Learning Tools for Early Detection of Alzheimer's Disease

Principal author

Jae Ho Sohn, MD, MS
co-Director Education & Internships



Combined deep learning techniques with brain imaging to discover changes in brain metabolism predictive of Alzheimer’s disease (AD). Previous research had demonstrated a link between patterns of glucose uptake in the brain and the disease process, but biomarker discovery was lacking. Collaborating with Benjamin Franc, MD, Dr. Sohn trained a DL algorithm using more than 2,100 18-F-fluorodeoxyglucose PET scans from 1,002 patients – a dataset originating from the Alzheimer’s Disease Neuroimaging Initiative. After testing on an independent set of 40 scans from 40 patients never studied, the algorithm was able to predict every case that advanced to AD. Dr. Sohn’s next steps could include a larger multi-institutional prospective study using the algorithm, as well as training the tool further to spot patterns associated with the accumulation of beta-amyloid and tau proteins – a known AD biomarker.

Saliency map of deep learning model
Saliency map of deep learning model Inception V3 on the classification of Alzheimer disease. (a) A representative saliency map with anatomic overlay in 77-year-old man. (b) Average saliency map over 10 percent of Alzheimer’s Disease Neuroimaging Initiative set. (c) Average saliency map over independent test set. The closer a pixel color is to the "High" end of the color bar in the image, the more influence it has on the prediction of Alzheimer disease.


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