summary: Researchers are developing a machine learning model aimed at early detection of Alzheimer’s disease. The model, potentially accessible via smartphone, can distinguish Alzheimer’s patients from healthy individuals with an accuracy of 70-75%.
By focusing on vocal patterns rather than content, this tool may provide valuable early indicators to initiate treatment earlier and slow disease progression.
It does not replace medical professionals, but it may enhance telemedicine services and help overcome geographic or language barriers.
Important facts:
- This machine learning model can identify Alzheimer’s patients from healthy people with 70-75% accuracy.
- The tool analyzes acoustic and verbal speech features rather than specific words to detect disease.
- An application of this model could be an easily accessible screening tool on a smartphone, providing an early indicator of Alzheimer’s disease.
sauce: University of Alberta
Researchers are working to enable early diagnosis of Alzheimer’s disease using machine learning (ML) models. This model could one day turn into a simple screening tool for anyone with a smartphone.
The model can distinguish Alzheimer’s patients from healthy controls with an accuracy of 70-75 percent, a promising figure for the more than 747,000 Canadians with Alzheimer’s disease or other forms of dementia. became.
Dementia of the Alzheimer’s type can be difficult to detect in its early stages because it often begins with very subtle symptoms that can be confused with memory-related problems that are typical of older people. . But, as the researchers point out, the earlier potential problems are detected, the sooner patients can take action.
“Previously, detecting changes in the brain required lab work and medical imaging. No,” says Eleni Straulia, a professor in the Department of Computing Sciences who helped create the model.
“If we could use mobile phones to see early signs, we would be able to understand the patient-physician relationship. You could start with and slow it down.”
Screening tools do not replace medical professionals. However, in addition to helping with early detection, telemedicine is a convenient way to identify potential concerns for patients who may face geographic or language barriers in accessing services within their community. Born, explains Zela Shah, a master’s student in the Department of Computing. Scientist and lead author of the paper.
“With this kind of technology, you can think about triaging patients based entirely on their voice,” says Shah.
While the research group has previously investigated the language used by people with Alzheimer’s disease, in this project they investigated language-independent acoustic and verbal speech characteristics rather than specific words.
“The original job was to listen and understand what they said, which is a computationally easier problem to solve,” says Stroulia. “Now we’re saying listen to the voice. There are some properties of the way people speak that transcend language.”
“This is much stronger than the version issues we were solving before,” Stroulia adds.
The researchers started with a language trait that doctors noted was common in people with Alzheimer’s disease. These patients tended to speak more slowly and had more pauses and breaks in speech.
They usually used shorter words and often had less clarity of speech. Researchers have found a way to transform these features into speech features that the model can screen.
The researchers focused on English and Greek speakers, but “the technology could be used in a variety of languages,” Shah says.
The model itself is complex, but the end user experience of the tool that incorporates it is even simpler.
“When a person talks to the tool, it analyzes it and makes a prediction. says Russ Greiner, a professor at . That information is provided to health care professionals so they can determine the best course of action for the person.
Greiner and Stroria both lead the Computational Psychiatry Research Group at the University of Australia, whose members have developed similar AI models for detecting mental illnesses such as PTSD, schizophrenia, depression and bipolar disorder. and tools.
“All that we can do to enhance the clinical process, inform treatment, and manage the disease faster and at less cost is great,” Stroulia said.
About this machine learning and Alzheimer’s research news
author: Adriana Macpherson
sauce: University of Alberta
contact: Adriana McPherson – University of Alberta
image: Image credited to Neuroscience News
Original research: The results of this research will be presented at the ICASSP 2023—2023 IEEE International Conference on Acoustics, Speech and Signal Processing.
