The team’s machine learning–powered computational model is claimed to detect cognitive impairment from audio recordings of neuropsychological tests. Their findings have been published in Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association.
“This approach brings us one step closer to early intervention,” said Ioannis Paschalidis, a co-author on the paper and a BU College of Engineering Distinguished Professor of Engineering. He said faster and earlier detection of Alzheimer’s could drive larger clinical trials that focus on individuals in early stages of the disease and potentially enable clinical interventions that slow cognitive decline.
“It can form the basis of an online tool that could reach everyone and could increase the number of people who get screened early,” Paschalidis said in a statement.
The research team trained their model using audio recordings of neuropsychological interviews from over 1,000 individuals. Using automated online speech recognition tools and a machine learning technique called natural language processing, they had their program transcribe the interviews, then encode them into numbers. A final model was trained to assess the likelihood and severity of an individual’s cognitive impairment using demographic data, the text encodings, and real diagnoses from neurologists and neuropsychologists.
Paschalidis said the model was able to accurately distinguish between healthy individuals and those with dementia, and also detect differences between those with mild cognitive impairment and dementia. The quality of the recordings and how people spoke were found to be less important than the content of what they were saying.
“It surprised us that speech flow or other audio features are not that critical; you can automatically transcribe interviews reasonably well, and rely on text analysis through AI to assess cognitive impairment,” said Paschalidis. The team needs to validate its results against other sources of data, but the findings suggest their tool could support clinicians in diagnosing cognitive impairment using audio recordings, including those from virtual or telehealth appointments.
The model also provides insight into what parts of the neuropsychological exam might be more important than others in determining whether an individual has impaired cognition. The researchers’ model splits the exam transcripts into different sections based on the clinical tests performed. They discovered that the Boston Naming Test—during which clinicians ask individuals to label a picture using one word—is most informative for an accurate dementia diagnosis.
“This might enable clinicians to allocate resources in a way that allows them to do more screening, even before symptom onset,” said Paschalidis.
Early diagnosis of dementia is important for patients and their caregivers to be able to create an effective plan for treatment and support. It is crucial also for researchers working on therapies to slow and prevent the progression of Alzheimer’s.
“Our models can help clinicians assess patients in terms of their chances of cognitive decline, and then best tailor resources to them by doing further testing on those that have a higher likelihood of dementia,” said,” Paschalidis.
The research team is looking for volunteers to take an online survey and submit an anonymous cognitive test. The results will be used to provide personalised cognitive assessments and will also help the team refine their AI model.