What is it about?
A new computer model detects Alzheimer's/dementia status in tens of thousands of routinely collected clinical brain MRIs. It uses advanced deep learning methods to both determine the uncertainty of diagnostic predictions, which aids in effective cross-institutional applications, as well as regression of confounding factors, such as patient age, from the prediction process.
Photo by National Cancer Institute on Unsplash
Why is it important?
While prior work has been able to detect Alzheimer's/dementia in public benchmark MRI datasets, this shows the ability of advanced deep learning models to perform this same task on low-quality, heterogeneous clinical MRI, while minimizing the bias of the machine learning model and maximizing its ability to perform across institutions.
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This page is a summary of: Adversarial confound regression and uncertainty measurements to classify heterogeneous clinical MRI in Mass General Brigham, PLoS ONE, March 2023, PLOS, DOI: 10.1371/journal.pone.0277572.
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