New AI model from UCSF can detect Alzheimer's 6 years before diagnosis

An artificial intelligence model can predict a patient's likelihood of developing Alzheimer's disease more than six years before their final diagnosis, according to a study published in Radiology.

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.

More articles on artificial intelligence:
Philips launches AI incubator with 19 startups
Mayo Clinic partners with AI firm to screen patients for heart conditions
Scripps partners with Nvidia to apply AI to genomic, digital sensor data

© Copyright ASC COMMUNICATIONS 2019. Interested in LINKING to or REPRINTING this content? View our policies by clicking here.

 

Top 40 Articles from the Past 6 Months