Although Alzheimer's disease affects tens of millions of people around the world, it is still difficult to detect at an early stage. But researchers who deal with the possibilities of artificial intelligence in medicine have discovered that technology can help early diagnosis of treacherous diseases. The California team recently published a report on its study in the Radiology magazine and showed how once trained the neural network was able to properly diagnose Alzheimer's disease in a limited number of patients based on brain formation visualizations performed years before the actual patients are actually diagnosed by a physician.
The team uses brain imaging (FDG-PET imaging) to train and test their neural network. In FDG, images of the patient's bloodstream are injected with a radioactive type of glucose, and then his body tissue, including the brain, pushes it against the surface. Researchers and doctors can then use PET scanning to know the metabolic activity of this tissue, depending on how much FDG is taken.
The FDG-PET method is used to diagnose Alzheimer's, with patients who have the disease usually show lower levels of metabolic activity in certain parts of the brain. However, experts need to analyze these images to find evidence of the disease, and this becomes very difficult because moderate cognitive impairment and Alzheimer's disease can lead to similar results in scanning.
Therefore, the team uses 2,110 FDG-PET images from 1002 patients, trains their neural networks at 90% and tests it for the remaining 10%. She also runs tests with a single set of 40 patients scanned between 2006 and 2016, then compares the results of artificial intelligence with a group of specialists who analyze the same data.
With a separate set of test data, Artificial Intelligence can diagnose Alzheimer's patients with 100% accuracy and 82% accuracy those who do not suffer from treacherous diseases. He can also make forecasts on average for more than six years to come. In comparison, the group of doctors who looked at the same scanned images identified patients with Alzheimer's disease in 57% of cases and those without the disease – in 91%. However, the differences in machine and human performance are not so noticeable in the diagnosis of mild cognitive impairment that is not typical of Alzheimer's disease.
Researchers note that their research has several limitations, including a small amount of test data and limited types of exercise data.