Scientists Look to AI Monitoring in Hopes of Detecting Early Signs of Alzheimer’s
Although we are likely, at best, decades away from a plausible cure for Alzheimer’s, many researchers believe that AI and machine learning hold the keys to better understanding the disease’s progress- and potentially finding an effective treatment for the disease.
Scientists at MIT are working on a pilot study using AI, machine learning, and passive data collection to help care providers identify the early signs of Alzheimer’s disease and- potentially- diagnose the disease earlier. An early diagnosis is beneficial for a number of reasons. For example, treatments that seek to slow down the progress of the disease are more effective when started early on. Early diagnosis is also helpful for patients and families because it gives those affected by the disease more time to plan for the progression of the disease.
It’s not always obvious when patients are in the early stages of the disease. Alterations in the brain can cause subtle changes in behavior and sleep patterns years before people start experiencing confusion and memory loss. Researchers think artificial intelligence could recognize these changes early and identify patients at risk of developing the most severe forms of the disease.
Spotting the first indications of Alzheimer’s years before any obvious symptoms come on could help pinpoint people most likely to benefit from experimental drugs and allow family members to plan for eventual care. Devices equipped with such algorithms could be installed in people’s homes or in long-term care facilities to monitor those at risk. For patients who already have a diagnosis, such technology could help doctors make adjustments in their care.
Data gathered through the program could help providers make more accurate diagnoses, even before patients and caregivers have noticed symptoms.
Currently, there’s no easy way to diagnose Alzheimer’s. No single test exists, and brain scans alone can’t determine whether someone has the disease. Instead, physicians have to look at a variety of factors, including a patient’s medical history and observations reported by family members or health-care workers. So machine learning could pick up on patterns that otherwise would easily be missed.
In addition, because the device is able to sit quietly in the background and collect data, the burden of patient observation is shifted away from family, caregivers, and even the patient themselves.
Katabi says their intention was to monitor people without needing them to put on a wearable tracking device every day. “This is completely passive. A patient doesn’t need to put sensors on their body or do anything specific, and it’s far less intrusive than a video camera,” she says.
Graham hardly notices the white box hanging in his sunlit, tidy room. He’s most aware of it on days when Ipsit Vahia makes his rounds and tells him about the data it’s collecting. Vahia is a geriatric psychiatrist at McLean Hospital and Harvard Medical School, and he and the technology’s inventors at MIT are running a small pilot study of the device.
A wireless radio signal emitted by the device monitors a patient’s movements and is using machine learning to identify movement patterns, like pacing and sleep disruptions, that are consistent with the early symptoms of the disease.
Katabi and her team developed machine-learning algorithms that analyze all these minute reflections. They trained the system to recognize simple motions like walking and falling, and more complex movements like those associated with sleep disturbances. “As you teach it more and more, the machine learns, and the next time it sees a pattern, even if it’s too complex for a human to abstract that pattern, the machine recognizes that pattern,” Katabi says.
Over time, the device creates large readouts of data that show patterns of behavior. The AI is designed to pick out deviations from those patterns that might signify things like agitation, depression, and sleep disturbances. It could also pick up whether a person is repeating certain behaviors during the day. These are all classic symptoms of Alzheimer’s.
Another group of scientists are using AI to retroactively study PET scans of patients who went on to develop Alzheimer’s taken before they were diagnosed with the disease. The research was able to identify patterns common among those patients who developed the disease, and was able to correctly predict which patients would develop Alzheimer’s within two years of the scan in 84% of cases.
“When a radiologist reads a scan, it’s impossible to tell whether a person will progress to Alzheimer’s disease,” says Pedro Rosa-Neto, a neurologist at McGill University in Montreal.
Rosa-Neto and his colleague Sulantha Mathotaarachchi developed an algorithm that analyzed hundreds of positron-emission tomography (PET) scans from people who had been deemed at risk of developing Alzheimer’s. From medical records, the researchers knew which of these patients had gone on to develop the disease within two years of a scan, but they wanted to see if the AI system could identify them just by picking up patterns in the images.
Sure enough, the algorithm was able to spot patterns in clumps of amyloid—a protein often associated with the disease—in certain regions of the brain. Even trained radiologists would have had trouble noticing these issues on a brain scan. From the patterns, it was able to detect with 84 percent accuracy which patients ended up with Alzheimer’s.
Because Alzheimer’s progresses differently for all patients affected by the disease, Duke University researcher P. Murali Doraiswany is working on a machine learning algorithm to predict the severity and speed of progression for any given patient.
He worked with Dragan Gamberger, an artificial-intelligence expert at the Rudjer Boskovic Institute in Croatia, to develop a machine-learning algorithm that sorted through brain scans and medical records from 562 patients who had mild cognitive impairment at the beginning of a five-year period.
Two distinct groups emerged: those whose cognition declined significantly and those whose symptoms changed little or not at all over the five years. The system was able to pick up changes in the loss of brain tissue over time.
A third group was somewhere in the middle, between mild cognitive impairment and advanced Alzheimer’s. “We don’t know why these clusters exist yet,” Doraiswamy says.
Although experimental Alzheimer’s drugs have failed in clinical trials, AI could help researchers determine which patients are likely to do better on specific drugs, potentially increasing success rates if patients with shared characteristics were found to benefit from certain treatments.
“Once we have those people together with common genes, characteristics, and imaging scans, that’s going to make it much easier to test drugs,” says Marilyn Miller, who directs AI research in Alzheimer’s at the National Institute on Aging, part of the US National Institutes of Health.
Then, once patients are enrolled in a study, researchers could continuously monitor them to see if they’re benefiting from the medication.
“One of the biggest challenges in Alzheimer’s drug development is we haven’t had a good way of parsing out the right population to test the drug on,” says Vaibhav Narayan, a researcher on Johnson & Johnson’s neuroscience team.