AI Algorithm Can Detect Alzheimer’s Earlier Than Doctors
A machine learning algorithm programmed by Dr. Jae Ho Sohn can look at PET scans of human brains and spot indicators of Alzheimer’s disease with a high level of accuracy an average of 6 years before the patients would receive a final clinical diagnosis from a doctor.
To train the algorithm, Sohn fed it images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), a massive public dataset of PET scans from patients who were eventually diagnosed with either Alzheimer’s disease, mild cognitive impairment or no disorder. Eventually, the algorithm began to learn on its own which features are important for predicting the diagnosis of Alzheimer’s disease and which are not.
Once the algorithm was trained on 1,921 scans, the scientists tested it on two novel datasets to evaluate its performance. The first were 188 images that came from the same ADNI database but had not been presented to the algorithm yet. The second was an entirely novel set of scans from 40 patients who had presented to the UCSF Memory and Aging Center with possible cognitive impairment.
The algorithm performed with flying colors. It correctly identified 92 percent of patients who developed Alzheimer’s disease in the first test set and 98 percent in the second test set. What’s more, it made these correct predictions on average 75.8 months — a little more than six years — before the patient received their final diagnosis.
This is the stuff where AI is going to be totally useful…provided the programs aren’t cheating somehow.
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