In an initial study, Canary analyzed speech recordings with AI and identified early stage Alzheimer’s with 96 percent accuracy.
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.”
Jessica Fitzjohn, a postdoctoral fellow at the University of Canterbury, demonstrates the novel breast cancer screening device.
Researchers at the University of Canterbury and startup Tiro Medical in Christchurch, New Zealand are hoping their new device—which doesn’t involve any radiation or compression of the breasts—could increase the accuracy of breast cancer screening, broaden access and encourage more women to get checked. They’re digging into clues from the way buildings move in an earthquake to help detect more cases of this disease.
Earthquake engineering inspires new breast cancer screening tech
What’s underneath a surface affects how it vibrates. Earthquake engineers look at the vibrations of swaying buildings to identify the underlying soil and tissue properties. “As the vibration wave travels, it reflects the stiffness of the material between that wave and the surface,” says Geoff Chase, professor of engineering at the University of Canterbury in Christchurch, New Zealand.
Chase is applying this same concept to breasts. Analyzing the surface motion of the breast as it vibrates could reveal the stiffness of the tissues underneath. Regions of high stiffness could point to cancer, given that cancerous breast tissue can be up to 20 times stiffer than normal tissue. “If in essence every woman’s breast is soft soil, then if you have some granite rocks in there, we’re going to see that on the surface,” explains Chase.
The earthquake-inspired device exceeds the 87 percent sensitivity of a 3D mammogram.
That notion underpins a new breast screening device, the brainchild of Chase. Women lie face down, with their breast being screened inside a circular hole and the nipple resting on a small disc called an actuator. The actuator moves up and down, between one and two millimeters, so there’s a small vibration, “almost like having your phone vibrate on your nipple,” says Jessica Fitzjohn, a postdoctoral fellow at the University of Canterbury who collaborated on the device design with Chase.
Cameras surrounding the device take photos of the breast surface motion as it vibrates. The photos are fed into image processing algorithms that convert them into data points. Then, diagnostic algorithms analyze those data points to find any differences in the breast tissue. “We’re looking for that stiffness contrast which could indicate a tumor,” Fitzjohn says.
A nascent yet promising technology
The device has been tested in a clinical trial of 14 women: one with healthy breasts and 13 with a tumor in one breast. The cohort was small but diverse, varying in age, breast volume and tumor size.
Results from the trial yielded a sensitivity rate, or the likelihood of correctly detecting breast cancer, of 85 percent. Meanwhile, the device’s specificity rate, or the probability of diagnosing healthy breasts, was 77 percent. By combining and optimizing certain diagnostic algorithms, the device reached between 92 and 100 percent sensitivity and between 80 and 86 percent specificity, which is comparable to the latest 3D mammogram technology. Called tomosynthesis, these 3D mammograms take a number of sharper, clearer and more detailed 3D images compared to the single 2D image of a conventional mammogram, and have a specificity score of 92 percent. Although the earthquake-inspired device’s specificity is lower, it exceeds the 87 percent sensitivity of a 3D mammogram.
The team hopes that cameras with better resolution can help improve the numbers. And with a limited amount of data in the first trial, the researchers are looking into funding for another clinical trial to validate their results on a larger cohort size.
Additionally, during the trial, the device correctly identified one woman’s breast as healthy, while her prior mammogram gave a false positive. The device correctly identified it as being healthy tissue. It was also able to capture the tiniest tumor at 7 millimeters—around a third of an inch or half as long as an aspirin tablet.
Diagnostic findings from the device are immediate.
When using the earthquake-inspired device, women lie face down, with their breast being screened inside circular holes.
University of Canterbury.
But more testing is needed to “prove the device’s ability to pick up small breast cancers less than 10 to 15 millimeters in size, as we know that finding cancers when they are small is the best way of improving outcomes,” says Richard Annand, a radiologist at Pacific Radiology in New Zealand. He explains that mammography already detects most precancerous lesions, so if the device will only be able to find large masses or lumps it won’t be particularly useful. While not directly involved in administering the clinical trial for the device, Annand was a director at the time for Canterbury Breastcare, where the trial occurred.
Meanwhile, Monique Gary, a breast surgical oncologist and medical director of the Grand View Health Cancer program in Pennsylvania, U.S., is excited to see new technologies advancing breast cancer screening and early detection. But she notes that the device may be challenging for “patients who are unable to lay prone, such as pregnant women as well as those who are differently abled, and this machine might exclude them.” She adds that it would also be interesting to explore how breast implants would impact the device’s vibrational frequency.
Diagnostic findings from the device are immediate, with the results available “before you put your clothes back on,” Chase says. The absence of any radiation is another benefit, though Annand considers it a minor edge “as we know the radiation dose used in mammography is minimal, and the advantages of having a mammogram far outweigh the potential risk of radiation.”
The researchers also conducted a separate ergonomic trial with 40 women to assess the device’s comfort, safety and ease of use. Angela was part of that trial and described the experience as “easy, quick, painless and required no manual intervention from an operator.” And if a person is uncomfortable being topless or having their breasts touched by someone else, “this type of device would make them more comfortable and less exposed,” she says.
While mammograms remain “the ‘gold standard’ in breast imaging, particularly screening, physicians need an option that can be used in combination with mammography.
Fitzjohn acknowledges that “at the moment, it’s quite a crude prototype—it’s just a block that you lie on.” The team prioritized function over form initially, but they’re now planning a few design improvements, including more cushioning for the breasts and the surface where the women lie on.
While mammograms remains “the ‘gold standard’ in breast imaging, particularly screening, physicians need an option that is good at excluding breast cancer when used in combination with mammography, has good availability, is easy to use and is affordable. There is the possibility that the device could fill this role,” Annand says.
Indeed, the researchers envision their new breast screening device as complementary to mammograms—a prescreening tool that could make breast cancer checks widely available. As the device is portable and doesn’t require specialized knowledge to operate, it can be used in clinics, pop-up screening facilities and rural communities. “If it was easily accessible, particularly as part of a checkup with a [general practitioner] or done in a practice the patient is familiar with, it may encourage more women to access this service,” Angela says. For those who find regular mammograms uncomfortable or can’t afford them, the earthquake-inspired device may be an option—and an even better one.
Broadening access could prompt more women to go for screenings, particularly younger women at higher risk of getting breast cancer because of a family history of the disease or specific gene mutations. “If we can provide an option for them then we can catch those cancers earlier,” Fitzjohn syas. “By taking screening to people, we’re increasing patient-centric care.”
With the team aiming to lower the device’s cost to somewhere between five and eight times less than mammography equipment, it would also be valuable for low-to-middle-income nations that are challenged to afford the infrastructure for mammograms or may not have enough skilled radiologists.
For Fitzjohn, the ultimate goal is to “increase equity in breast screening and catch cancer early so we have better outcomes for women who are diagnosed with breast cancer.”