Small changes in how a person talks could reveal Alzheimer’s earlier
Dave Arnold retired in his 60s and began spending time volunteering in local schools. But then he started misplacing items, forgetting appointments and losing his sense of direction. Eventually he was diagnosed with early stage Alzheimer’s.
“Hearing the diagnosis made me very emotional and tearful,” he said. “I immediately thought of all my mom had experienced.” His mother suffered with the condition for years before passing away. Over the last year, Arnold has worked for the Alzheimer’s Association as one of its early stage advisors, sharing his insights to help others in the initial stages of the disease.
Arnold was diagnosed sooner than many others. It's important to find out early, when interventions can make the most difference. One promising avenue is looking at how people talk. Research has shown that Alzheimer’s affects a part of the brain that controls speech, resulting in small changes before people show other signs of the disease.
Now, Canary Speech, a company based in Utah, is using AI to examine elements like the pitch of a person’s voice and their pauses. In an initial study, Canary analyzed speech recordings with AI and identified early stage Alzheimer’s with 96 percent accuracy.
Developing the AI model
Canary Speech’s CEO, Henry O’Connell, met cofounder Jeff Adams about 40 years before they started the company. Back when they first crossed paths, they were both living in Bethesda, Maryland; O’Connell was a research fellow at the National Institutes of Health studying rare neurological diseases, while Adams was working to decode spy messages. Later on, Adams would specialize in building mathematical models to analyze speech and sound as a team leader in developing Amazon's Alexa.
It wasn't until 2015 that they decided to make use of the fit between their backgrounds. ““We established Canary Speech in 2017 to build a product that could be used in multiple languages in clinical environments,” O'Connell says.
The need is growing. About 55 million people worldwide currently live with Alzheimer’s, a number that is expected to double by 2050. Some scientists think the disease results from a buildup of plaque in the brain. It causes mild memory loss at first and, over time, this issue get worse while other symptoms, such as disorientation and hallucinations, can develop. Treatment to manage the disease is more effective in the earlier stages, but detection is difficult since mild symptoms are often attributed to the normal aging process.
O’Connell and Adams specialize in the complex ways that Alzheimer’s effects how people speak. Using AI, their mathematical model analyzes 15 million data points every minute, focusing on certain features of speech such as pitch, pauses and elongation of words. It also pays attention to how the vibrations of vocal cords change in different stages of the disease.
To create their model, the team used a type of machine learning called deep neural nets, which looks at multiple layers of data - in this case, the multiple features of a person’s speech patterns.
“Deep neural nets allow us to look at much, much larger data sets built out of millions of elements,” O’Connell explained. “Through machine learning and AI, we’ve identified features that are very sensitive to an Alzheimer’s patient versus [people without the disease] and also very sensitive to mild cognitive impairment, early stage and moderate Alzheimer's.” Based on their learnings, Canary is able to classify the disease stage very quickly, O’Connell said.
“When we’re listening to sublanguage elements, we’re really analyzing the direct result of changes in the brain in the physical body,” O’Connell said. “The brain controls your vocal cords: how fast they vibrate, the expansion of them, the contraction.” These factors, along with where people put their tongues when talking, function subconsciously and result in subtle changes in the sounds of speech.
Further testing is needed
In an initial trial, Canary analyzed speech recordings from phone calls to a large U.S. health insurer. They looked at the audio recordings of 651 policyholders who had early stage Alzheimer’s and 1018 who did not have the condition, aiming for a representative sample of age, gender and race. They used this data to create their first diagnostic model and found that it was 96 percent accurate in identifying Alzheimer’s.
Christian Herff, an assistant professor of neuroscience at Maastricht University in the Netherlands, praised this approach while adding that further testing is needed to assess its effectiveness.
“I think the general idea of identifying increased risk for cognitive impairment based on speech characteristics is very feasible, particularly when change in a user’s voice is monitored, for example, by recording speech every year,” Herff said. He noted that this can only be a first indication, not a full diagnosis. The accuracy still needs to be validated in studies that follows individuals over a period of time, he said.
Toby Walsh, a professor of artificial intelligence at the University of New South Wales, also thinks Canary’s tool has potential but highlights that Canary could diagnose some people who don’t really have the disease. “This is an interesting and promising application of AI,” he said, “but these tools need to be used carefully. Imagine the anxiety of being misdiagnosed with Alzheimer’s.”
As with many other AI tools, privacy and bias are additional issues to monitor closely, Walsh said.
Other languages
A related issue is that not everyone is fluent in English. Mahnaz Arvaneh, a senior lecturer in automatic control and systems engineering at the University of Sheffield, said this could be a blind spot.
“The system may not be very accurate for those who have English as their second language as their speaking patterns would be different, and any issue might be because of language deficiency rather than cognitive issues,” Arvaneh said.
The team is expanding to multiple languages starting with Japanese and Spanish. The elements of the model that make up the algorithm are very similar, but they need to be validated and retrained in a different language, which will require access to more data.
Recently, Canary analyzed the phone calls of 233 Japanese patients who had mild cognitive impairment and 704 healthy people. Using an English model they were able to identify the Japanese patients who had mild cognitive impairment with 78 percent accuracy. They also developed a model in Japanese that was 45 percent accurate, and they’re continuing to train it with more data.
The future
Canary is using their model to look at other diseases like Huntington’s and Parkinson’s. They’re also collaborating with pharmaceuticals to validate potential therapies for Alzheimer’s. By looking at speech patterns over time, Canary can get an indication of how well these drugs are working.
Dave Arnold and his wife dance at his nephew’s wedding in Rochester, New York, ten years ago, before his Alzheimer's diagnosis.
Dave Arnold
Ultimately, they want to integrate their tool into everyday life. “We want it to be used in a smartphone, or a teleconference call so that individuals could be examined in their home,” O’Connell said. “We could follow them over time and work with clinical teams and hospitals to improve the evaluation of patients and contribute towards an accurate diagnosis.”
Arnold, the patient with early stage Alzheimer’s, sees great promise. “The process of getting a diagnosis is already filled with so much anxiety,” he said. “Anything that can be done to make it easier and less stressful would be a good thing, as long as it’s proven accurate.”
After his grandmother’s dementia diagnosis, one man invented a snack to keep her healthy and hydrated.
On a visit to his grandmother’s nursing home in 2016, college student Lewis Hornby made a shocking discovery: Dehydration is a common (and dangerous) problem among seniors—especially those that are diagnosed with dementia.
Hornby’s grandmother, Pat, had always had difficulty keeping up her water intake as she got older, a common issue with seniors. As we age, our body composition changes, and we naturally hold less water than younger adults or children, so it’s easier to become dehydrated quickly if those fluids aren’t replenished. What’s more, our thirst signals diminish naturally as we age as well—meaning our body is not as good as it once was in letting us know that we need to rehydrate. This often creates a perfect storm that commonly leads to dehydration. In Pat’s case, her dehydration was so severe she nearly died.
When Lewis Hornby visited his grandmother at her nursing home afterward, he learned that dehydration especially affects people with dementia, as they often don’t feel thirst cues at all, or may not recognize how to use cups correctly. But while dementia patients often don’t remember to drink water, it seemed to Hornby that they had less problem remembering to eat, particularly candy.
Where people with dementia often forget to drink water, they're more likely to pick up a colorful snack, Hornby found. alzheimers.org.uk
Hornby wanted to create a solution for elderly people who struggled keeping their fluid intake up. He spent the next eighteen months researching and designing a solution and securing funding for his project. In 2019, Hornby won a sizable grant from the Alzheimer’s Society, a UK-based care and research charity for people with dementia and their caregivers. Together, through the charity’s Accelerator Program, they created a bite-sized, sugar-free, edible jelly drop that looked and tasted like candy. The candy, called Jelly Drops, contained 95% water and electrolytes—important minerals that are often lost during dehydration. The final product launched in 2020—and was an immediate success. The drops were able to provide extra hydration to the elderly, as well as help keep dementia patients safe, since dehydration commonly leads to confusion, hospitalization, and sometimes even death.
Not only did Jelly Drops quickly become a favorite snack among dementia patients in the UK, but they were able to provide an additional boost of hydration to hospital workers during the pandemic. In NHS coronavirus hospital wards, patients infected with the virus were regularly given Jelly Drops to keep their fluid levels normal—and staff members snacked on them as well, since long shifts and personal protective equipment (PPE) they were required to wear often left them feeling parched.
In April 2022, Jelly Drops launched in the United States. The company continues to donate 1% of its profits to help fund Alzheimer’s research.
Last week, researchers at the University of Oxford announced that they have received funding to create a brand new way of preventing ovarian cancer: A vaccine. The vaccine, known as OvarianVax, will teach the immune system to recognize and destroy mutated cells—one of the earliest indicators of ovarian cancer.
Understanding Ovarian Cancer
Despite advancements in medical research and treatment protocols over the last few decades, ovarian cancer still poses a significant threat to women’s health. In the United States alone, more than 12,0000 women die of ovarian cancer each year, and only about half of women diagnosed with ovarian cancer survive five or more years past diagnosis. Unlike cervical cancer, there is no routine screening for ovarian cancer, so it often goes undetected until it has reached advanced stages. Additionally, the primary symptoms of ovarian cancer—frequent urination, bloating, loss of appetite, and abdominal pain—can often be mistaken for other non-cancerous conditions, delaying treatment.
An American woman has roughly a one percent chance of developing ovarian cancer throughout her lifetime. However, these odds increase significantly if she has inherited mutations in the BRCA1 or BRCA2 genes. Women who carry these mutations face a 46% lifetime risk for ovarian and breast cancers.
An Unlikely Solution
To address this escalating health concern, the organization Cancer Research UK has invested £600,000 over the next three years in research aimed at creating a vaccine, which would destroy cancerous cells before they have a chance to develop any further.
Researchers at the University of Oxford are at the forefront of this initiative. With funding from Cancer Research UK, scientists will use tissue samples from the ovaries and fallopian tubes of patients currently battling ovarian cancer. Using these samples, University of Oxford scientists will create a vaccine to recognize certain proteins on the surface of ovarian cancer cells known as tumor-associated antigens. The vaccine will then train that person’s immune system to recognize the cancer markers and destroy them.
The next step
Once developed, the vaccine will first be tested in patients with the disease, to see if their ovarian tumors will shrink or disappear. Then, the vaccine will be tested in women with the BRCA1 or BRCA2 mutations as well as women in the general population without genetic mutations, to see whether the vaccine can prevent the cancer altogether.
While the vaccine still has “a long way to go,” according to Professor Ahmed Ahmed, Director of Oxford University’s ovarian cancer cell laboratory, he is “optimistic” about the results.
“We need better strategies to prevent ovarian cancer,” said Ahmed in a press release from the University of Oxford. “Currently, women with BRCA1/2 mutations are offered surgery which prevents cancer but robs them of the chance to have children afterward.
Teaching the immune system to recognize the very early signs of cancer is a tough challenge. But we now have highly sophisticated tools which give us real insights into how the immune system recognizes ovarian cancer. OvarianVax could offer the solution.”