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Could an AI-enabled pen change how we diagnose the brain?
For all of the advances in medical technology, many of the world’s most widely-used diagnostic tools essentially involve just two things: pen and paper.
Tests such as the Montreal Cognitive Assessment (MoCA) and the Clock Drawing Test (CDT) are used to detect cognitive change arising from a wide range of causes, from strokes and concussions to dementias such as Alzheimer’s disease.
What’s disconcerting, though, is that, with dementia and other disorders growing in prevalence, most current diagnostic methods detect cognitive impairment only after it starts affecting people’s lives. In Alzheimer’s, for example, changes in the brain may occur 10 or more years before the cognitive change becomes noticeable, and no easily administered test can detect these changes at the very earliest stage.
At least, not yet.
This month CSAIL researchers were part of a team that published a paper demonstrating a predictive model that, coupled with existing hardware, opens up the possibility of detecting disorders such as dementia earlier than ever before.
But the test has limitations, because its benchmarks rely on doctors’ subjective judgments, such as determining whether a clock circle has “only minor distortion.”
CSAIL researchers were particularly struck by the fact that CDT analysis was typically based on the person’s final drawing rather than on the process as a whole.
Enter the Anoto Live Pen, a digitizing ballpoint pen that measures its position on the paper upwards of 80 times a second, using a camera built into the pen. The pen provides data that are far more precise than can be measured on an ordinary drawing, and captures timing information that allows the system to analyze each and every one of a subject’s movements and hesitations.
Research at Lahey Hospital and Medical Center and CSAIL produced novel software for analyzing this version of the test, producing what the team calls the digital Clock Drawing Test (dCDT).
Predictive power of drawings
They tested their models against standard methods used by physicians and found that the machine learning models were significantly more accurate.
“We’ve improved the analysis so that it is automated and objective,” says CSAIL principal investigator Cynthia Rudin, a professor at the Sloan School of Management and co-author of the paper. “With the right equipment, you can get results wherever you want, quickly, and with higher accuracy.”
Some of the machine learning techniques they used were designed to produce “transparent” classifiers, which provide insights into what factors are important for screening and diagnosis.
“These examples help calibrate the predictive power of each part of the drawing,” says first author William Souillard-Mandar, a graduate student at CSAIL. “They allow us to extract thousands of features from the drawing process that give hints about the subject’s cognitive state, and our algorithms help determine which ones can make the most accurate prediction.”
Souillard-Mandar and Rudin co-wrote the paper with MIT Professor Randall Davis and researchers Dana Penney of Lahey Hospital, Rhoda Au of Boston University, David Libon of Drexel University, Catherine Price of the University of Florida, Melissa Lamar of the University of Illinois Chicago, and Rod Swenson of the University of North Dakota Medical School.
Different disorders reveal themselves in different ways on the CDT, which asks people to draw a clock showing 10 minutes after 11, and then asks them to copy a pre-drawn clock showing that time.
For example, while healthy adults spend more time on the dCDT thinking (with the pen off the paper) than “inking,” memory-impaired subjects spend even more time than that thinking rather than inking. Parkinson’s subjects, meanwhile, took longer to draw clocks that tended to be smaller, suggesting that they are working harder, but producing less — an insight not detectable with previous analysis systems.
“Neurologists see dozens of patients every day, and so the amount of time they spend sifting through databases and hand-coding their observations adds up very quickly,” says Phil Cohen, a vice president at VoiceBox Technologies who has done extensive research involving digital-pen technologies. “The work is still in a relatively early state, but this has the potential to not just better detect disease, but save clinicians a lot of time.”
Now that the team has proven the dCDT’s effectiveness, they are working to develop an interface that would allow neurologists and non-specialists alike to more easily use the technology in hospitals.
“We’re eager to see how well our model will work with other screening tools we’re developing,” Davis says. “As researchers, we’re just beginning to investigate all of the ways that your subtle behaviors reveal things about your brain.”