Researchers out of UC San Francisco developed the algorithm using deep learning, a type of AI modeled on how the human brain processes information. To train the algorithm to identify early signs of Alzheimer’s disease, they supplied it with images from the positron emission tomography, or PET, scans of nearly 1,000 patients’ brains.
The deep learning algorithm achieved 82 percent specificity at 100 percent sensitivity at predicting patients’ final clinical diagnosis by radiologic readers. On average, the algorithm reached its conclusion 75.8 months — or more than six years — prior to a radiologic reader’s diagnosis.
“A deep learning algorithm can be used as an early prediction tool for Alzheimer disease, especially in conjunction with other biochemical and imaging tests, thereby providing an opportunity for early therapeutic intervention,” the study authors concluded.
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