Researchers at Boston University say they have designed an artificial intelligence tool that can predict with nearly 80% accuracy whether someone is at risk for developing Alzheimer’s disease based on their speech patterns.
Using a natural language processing model, the researchers attempted to see if people with mild cognitive decline would develop Alzheimer’s within a six-year period. They focused on a cohort of 166 people — 107 women and 59 men — between the ages of 63 and 97 who had some level of cognitive complaints.
Each participant had been part of the Framingham Heart Study, which is led by Boston University, and as part of the study had been recorded for an hour-long interview. Those interviews formed the data that was analyzed by the AI tool the researchers developed.
Of the cohort, 90 people went on to have progressive declines in their cognitive function, while 76 remained stable. But what the Boston University researchers discovered was that by combining speech-recognition tools and machine learning, they could track connections between speech patterns and cognitive decline, based on biomarkers associated with cognitive decline. The model they developed, albeit in a small sample size, was able to predict significant cognitive decline with 78.5% accuracy, the researchers say.
The fact that they’re showing such high accuracy off of such a small data set is actually really promising and shows that if they were to use a much larger data set, like SpeechDx, one of the datasets the ADDF has been developing, then I think there is much more potential for growth here. Of course, it’s hard to say what the ceiling is there — it’s hard to put kind of an upper limit on how accurate it can be.
With a larger, more thoroughly characterized dataset, it could probably get quite accurate. Dementia directly affects more than 55 million people worldwide, and up to 70% of those people have Alzheimer’s disease, which is characterized by a loss of brain cells associated with the toxic buildup of two proteins, amyloid and tau.
The most common symptoms of Alzheimer’s disease are memory loss, cognitive deficits, problems with speaking, recognition, spatial awareness, reading, or writing, and significant changes in personality and behavior. Since Alzheimer’s is progressive, these symptoms are usually mild at first and tend to become more severe over time. With no cure for the disease, patients and caregivers must approach treatment with medication, lifestyle changes, and support groups.
The AI tool for predicting Alzheimer’s progression offers several significant advances: it enables early intervention with treatments to slow the disease, improves accessibility to cognitive assessments through automated and remote screening, and facilitates personalized care plans based on predicted disease trajectories. Additionally, it helps healthcare providers prioritize patients needing intensive monitoring, optimizing resource allocation, and provides valuable data for refining predictive models and developing new treatment strategies.
Researchers said that the current wave of drugs for Alzheimer’s work best when prescribed as early as possible, but they added that detection of potential cognitive decline is often based on the presence of amyloid in the brain. But she said that a large portion of people who test positive for amyloid in the brain don’t go on to develop cognitive symptoms — so the AI model can provide more accurate predictions. There will be cases where the tool incorrectly predicts disease progression or stability, potentially leading to undue stress or false reassurance.
While the use of AI has the capability to revolutionize certain areas of healthcare, there is a risk that clinicians might over-rely on AI predictions without considering the broader clinical context, leading to potential misdiagnoses.
Researchers also cautioned that while the accuracy rate is promising, the sample size was small, and ultimately the tool is not meant to be used on its own — now or in the future. It might, in cases when it might be wrong, it might raise a lot of alarm, cause a lot of stress to an individual unnecessarily. But they think this tool is very likely not meant to be used alone but meant to be used in conjunction with a whole bunch of other things like a blood test that can give you a more accurate picture or holistic image of how an individual is doing that would then kind of mitigate some of that risk.
