A new algorithm significantly outperformed human clinicians in predicting which patients would later develop Alzheimer’s disease.

Powerful Algorithm Accurately Analyzes PET Scans

A powerful new deep learning algorithm has been developed that can study PET scan images and effectively detect the onset of Alzheimer’s disease up to six years earlier than current diagnostic methods. The research is part of a new wave of work using machine learning technology to identify subtle patterns in complex medical imaging data that human doctors are unable to see.

One of the clearer diagnostic tools that identify the onset of Alzheimer’s disease is a type of brain imaging scan called an 18-F-fluorodeoxyglucose PET scan (FDG-PET). It’s traditionally used to identify several types of cancers, but in recent years has proved itself useful in identifying Alzheimer’s disease, as well as several other types of dementia. While results are promising, many urge caution, since more work needs to be done to validate results before it moves into clinical applications.

“This is a tiny data set, only looking at forty people,” said John Hardy from University College, London. “It’s also a very selected data set and not representative of the whole population. So we can’t know yet whether this is relevant to most people.”

Also, these kinds of PET scans are not available to most patients. This kind of machine learning innovation is undeniably impressive in an academic context, but it doesn’t offer up a useful tool for doctors hoping to better diagnose patients en masse.

“Currently in the UK, the use of PET scanning is mainly limited to research studies and clinical trials, to ensure that potential new medicines are tested in the right people,” said Carol Routledge of Alzheimer’s Research UK. “PET scans are a powerful tool, but they are expensive and require specialist facilities and expertise.”

The new study was published in the journal Radiology, according to the story by Rich Hardy.