AI Assesses Alzheimer’s Risk by Analyzing Word Usage
Artificial intelligence could soon help screen for Alzheimer’s disease by analyzing writing. A team from IBM and Pfizer says it has trained AI models to spot early signs of the notoriously stealthy illness by looking at linguistic patterns in word usage.
Other researchers have already trained various models to look for signs of cognitive impairments, including Alzheimer’s, by using different types of data, such as brain scans and clinical test results. But the latest work stands out because it used historical information from the multigenerational Framingham Heart Study, which has been tracking the health of more than 14,000 people from three generations since 1948. If the new models’ ability to pick up trends in such data holds up in forward-looking studies of bigger and more diverse populations, researchers say they could predict the development of Alzheimer’s a number of years before symptoms become severe enough for typical diagnostic methods to pick up. And such a screening tool would not require invasive tests or scans. The results of the Pfizer-funded and IBM-run study were published on Thursday in EClinicalMedicine.
The new AI models provide “an augmentation to expert practitioners in how you would see some subtle changes earlier in time, before the clinical diagnosis has been achieved,” says Ajay Royyuru, vice president of health care and life sciences research at IBM. “It might actually alert you to some changes that [indicate] you ought to then go do a more complete exam.”
To train these models, the researchers used digital transcriptions of handwritten responses from Framingham Heart Study participants who were asked to describe a picture of a woman who is apparently preoccupied with washing dishes while two kids raid a cookie jar behind her back. These descriptions did not preserve the handwriting from the original responses, says Rhoda Au, director of neuropsychology at the Framingham study and a professor at Boston University. (Her team was responsible for transcribing data for the new paper but did not participate beyond that.) Yet even without the physical handwriting, IBM says its main AI model was able to detect linguistic features that are sometimes related to early signs of cognitive impairment. They include certain misspellings, repeated words and the use of simplified phrases rather than grammatically complex sentences. This evidence is in line with clinicians’ understanding of how Alzheimer’s disease can impact language, Royyuru says.
The main model achieved 70 percent accuracy in predicting which of the Framingham participants eventually developed dementia associated with Alzheimer’s disease before the age of 85. This result was based on historical data rather than actually predicting future events, however—and there are other caveats to the new paper as well.
The AI focused on the oldest group of Framingham study participants, who mostly represent a non-Hispanic white population. This limits how much the results can be generalized to more diverse communities in the U.S.
