Trying to figure out whether someone has Alzheimer’s disease usually involves a battery of assessments-;interviews, brain imaging, blood and cerebrospinal fluid tests. But, by then, it’s probably already too late: memories have started slipping away, long established personality traits have begun subtly shifting. If caught early, new pioneering treatments can slow the disease’s remorseless progression, but there’s no surefire way to predict who will develop the dementia associated with Alzheimer’s.
Now, Boston University researchers say they have designed a promising new artificial intelligence computer program, or model, that could one day help change that-;just by analyzing a patient’s speech.
Their model can predict, with an accuracy rate of 78.5 percent, whether someone with mild cognitive impairment is likely to remain stable over the next six years-;or fall into the dementia associated with Alzheimer’s disease. While allowing clinicians to peer into the future and make earlier diagnoses, the researchers say their work could also help make cognitive impairment screening more accessible by automating parts of the process-;no expensive lab tests, imaging exams, or even office visits required. The model is powered by machine learning, a subset of AI where computer scientists teach a program to independently analyze data. We wanted to predict what would happen in the next six years-;and we found we can reasonably make that prediction with relatively good confidence and accuracy. It shows the power of AI." Ioannis (Yannis) Paschalidis, Director of the BU Rafik B. Hariri Institute for Computing and Computational Science & Engineering The multidisciplinary team of engineers, neurobiologists, and computer and data scientists published their findings in Alzheimer’s & Dementia , the journal of the Alzheimer’s Association.
"We hope, as everyone does, that there will be more and more Alzheimer’s treatments made available," says Paschalidis, a BU College of Engineering Distinguished Professor of Engineering and founding member of the Faculty of Computing & Data Sciences. "If you can predict what will happen, you have more of an opportunity and time window to intervene with drugs, and at least try to maintain the stability of the condition and prevent the transition to more severe forms of dementia." Calculating […]
Automated prediction of Alzheimer’s disease progression using speech and machine learning
