Diagnosing Alzheimer’s with AI

This is pretty good news if you think Alzheimer’s can be slowed or halted in some way (unproven, but a good idea): researchers funded by NIH have developed a (so far) 100% accurate method of diagnosing Alzheimer’s before any clinical symptoms appear.  The study seems pretty bullet-proof, too: Prospective ¹⁸F-FDG PET brain images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (2109 imaging studies from 2005 to 2017, 1002 patients) and retrospective independent test set (40 imaging studies from 2006 to 2016, 40 patients) were collected. 90% of the images were used as training data and the rest used as test data.  The learning algorithm developed for early prediction of Alzheimer disease achieving 82% specificity at 100% sensitivity, an average of 75.8 months prior to the final diagnosis.

Example of fluorine 18 fluorodeoxyglucose PET images from Alzheimer’s Disease Neuroimaging Initiative set preprocessed with the grid method for patients with Alzheimer disease (AD). One representative zoomed-in section was provided for each of three example patients: A, 76-year-old man with AD, B, 83-year-old woman with mild cognitive impairment (MCI), and, C, 80-year-old man with non-AD/MCI. In this example, the patient with AD presented slightly less gray matter than did the patient with non-AD/MCI. The difference between the patient with MCI and the patient with non-AD/MCI appeared minimal to the naked eye.

I do recommend doing your homework (below), since the paper is pretty digestible for the alert layman, and the study itself well structured.

Homework:

1. Yiming Ding, Jae Ho Sohn, Michael G. Kawczynski, Hari Trivedi, Roy Harnish, Nathaniel W. Jenkins, Dmytro Lituiev, Timothy P. Copeland, Mariam S. Aboian, Carina Mari Aparici, Spencer C. Behr, Robert R. Flavell, Shih-Ying Huang, Kelly A. Zalocusky, Lorenzo Nardo, Youngho Seo, Randall A. Hawkins, Miguel Hernandez Pampaloni, Dexter Hadley, Benjamin L. Franc. A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain. Radiology, 2018; 180958 DOI: 10.1148/radiol.2018180958