Artificial Intelligence May Detect Alzheimer’s Disease from Brain Scan

An accurate method for Alzheimer’s disease detection that relies on routinely collected clinical brain images has been developed by a team of researchers at Massachusetts General Hospital (MGH). This advanced discovery could lead to more accurate diagnoses. The discovery which is published in PLOS ONE used deep learning—a type of machine learning and artificial intelligence that uses large amounts of data and complex algorithms to train models.

In this case, the scientists developed a model for Alzheimer’s disease detection based on data from brain magnetic resonance images (MRIs) collected from patients with and without Alzheimer’s disease who were seen at MGH before 2019.

Next, the group tested the model across five datasets—MGH post-2019, Brigham and Women’s Hospital pre- and post-2019, and outside systems pre- and post-2019—to see if it could accurately detect Alzheimer’s disease based on real-world clinical data, regardless of hospital and time.

Making Strides in Detecting Alzheimer’s Disease Signs

Overall, the research involved 11,103 images from 2,348 patients at risk for Alzheimer’s disease and 26,892 images from 8,456 patients without Alzheimer’s disease. Across all five datasets, the model detected Alzheimer’s disease risk with 90.2% accuracy.

The main innovation of this work was its ability to detect Alzheimer's disease regardless of other variables, such as age. Alzheimer's disease typically occurs in older adults, and so deep learning models often have difficulty detecting the rarer early-onset cases.

Researchers have addressed this by making the deep learning model ‘blind’ to features of the brain that it finds to be overly associated with the patient's listed age.

Another common challenge in disease detection, especially in real-world settings, is dealing with data that are very different from the training set. The model used an uncertainty metric to determine whether patient data were too different from what it had been trained on for it to be able to make a successful prediction.

These results—with cross-site, cross-time, and cross-population generalizability—make a strong case for the clinical use of this diagnostic technology.



Source-Eurekalert