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Artificial intelligence can be a useful tool to predict Alzheimer

Reporter: Irina Robu, PhD

3.3.10   Artificial intelligence can be a useful tool to predict Alzheimer, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair

The Alzheimer’s Association estimate that around 5.7 million people live with Alzheimer’s disease in the United States which will rise to almost 14 million by 2050. Earlier diagnosis would not only benefit those affected, but it could also jointly save about $7.9 trillion in medical care and related costs over time. As Alzheimer’s disease progresses, it changes how brain cells use glucose. This alteration in glucose metabolism shows up in a type of PET imaging that tracks the uptake of a radioactive form of glucose called 18F-fluorodeoxyglucose. By giving instructions about what to look for, the scientists were able to train the deep learning algorithm to assess the PET images for early signs of Alzheimer’s.
The researchers from University of California San Francisco used positron-emission tomography images of 1002 people’s brain to train the deep learning algorithm they developed. They used 90 percent of images to teach the algorithm to spot features of Alzheimer’s disease and the remaining 10 percent to verify its performance. The researchers tested the algorithm on PET images of brains from 40 people, from which they were able to predict which individuals would receive a final diagnosis of Alzheimer’s. On average, the people who were tested were diagnosed with the disease more than 6 years after the scans.
According to the Radiology journal in which the research was published, the team describes how the algorithm “achieved 82 percent specificity at 100 percent sensitivity, an average of 75.8 months prior to the final diagnosis.” The researchers taught the algorithm with the help of more than 2,109 PET images of 1,002 individuals’ brains. The algorithm uses deep learning, which allows the algorithm to “teach itself” what to look for by spotting subtle differences among the thousands of images. The algorithm was as good as, if not better than, human experts at analyzing the FDG PET images.
Future advances will involve using larger data sets and additional images taken over time from people at various clinics and institutions. In the future, the algorithm could be a beneficial addition to the radiologist’s toolbox and advance opportunities for the early treatment of Alzheimer’s disease.

Source

https://www.medicalnewstoday.com/articles/323608.php