Cough analysis app could detect COVID-19 by sound alone
Researchers at MIT have used machine learning to create software capable of detecting whether a person has caught COVID-19 by analyzing their cough, a development that could eventually result in an iPhone app for daily checks.
The UK's NHS COVID-19 app for iPhone uses the Apple-Google exposure notification API to help combat the coronavirus.
So far, the iPhone has lent itself to helping users determine if they are at risk of the coronavirus by coming into close proximity to someone carrying the virus. If a new discovery by MIT researchers is developed further, the iPhone may end up being able to do more to dampen the spread of the virus further.
A paper from the team published in the IEEE Journal of Engineering in Medicine and Biology claims an AI model was created that could tell the difference between asymptomatic people and those who are healthy, via analysis of recordings of forced coughs. The model is claimed to be accurate 98.5% of the time when listening to recordings of people confirmed to have had COVID-19, as well as 100 percent of asymptomatic cough recordings.
The team collected more than 70,000 recordings via a website where the public could record a series of coughs via their smartphone or other devices, at the same time as filling out a survey about their symptoms, if they were confirmed to have the virus, and other details. The recordings resulted in around 200,000 forced-cough samples, including 2,500 of those confirmed to have COVID-19 or were asymptomatic.
Combining the 2,500 confirmed samples with another 2,500 randomly selected from the data set, the AI model was trained, then tested. The researchers claim the results revealed "a striking similarity between Alzheimer's and COVID discrimination."
The AI framework was based on one that existed for Alzheimer's research, and determined it could pick up four biomarkers relating to vocal cord strength, sentiment, lung and respiratory response, and muscular degradation specific to COVID-19.
The team are now working to create a pre-screening app it intends to distribute for free, based on the AI model, as well as working with a number of hospitals to enlarge the cough recording pool, for further training.
It is suggested by the team that such cough analysis could be implemented into smart speakers and digital assistants to perform daily assessments. This naturally would depend on the devices involved having sufficiently good quality microphones as well as handling the necessary privacy issues, not to mention the assistance of companies like Apple and Amazon to implement, making it unlikely albeit altruistic.
The UK's NHS COVID-19 app for iPhone uses the Apple-Google exposure notification API to help combat the coronavirus.
So far, the iPhone has lent itself to helping users determine if they are at risk of the coronavirus by coming into close proximity to someone carrying the virus. If a new discovery by MIT researchers is developed further, the iPhone may end up being able to do more to dampen the spread of the virus further.
A paper from the team published in the IEEE Journal of Engineering in Medicine and Biology claims an AI model was created that could tell the difference between asymptomatic people and those who are healthy, via analysis of recordings of forced coughs. The model is claimed to be accurate 98.5% of the time when listening to recordings of people confirmed to have had COVID-19, as well as 100 percent of asymptomatic cough recordings.
The team collected more than 70,000 recordings via a website where the public could record a series of coughs via their smartphone or other devices, at the same time as filling out a survey about their symptoms, if they were confirmed to have the virus, and other details. The recordings resulted in around 200,000 forced-cough samples, including 2,500 of those confirmed to have COVID-19 or were asymptomatic.
Combining the 2,500 confirmed samples with another 2,500 randomly selected from the data set, the AI model was trained, then tested. The researchers claim the results revealed "a striking similarity between Alzheimer's and COVID discrimination."
The AI framework was based on one that existed for Alzheimer's research, and determined it could pick up four biomarkers relating to vocal cord strength, sentiment, lung and respiratory response, and muscular degradation specific to COVID-19.
The team are now working to create a pre-screening app it intends to distribute for free, based on the AI model, as well as working with a number of hospitals to enlarge the cough recording pool, for further training.
It is suggested by the team that such cough analysis could be implemented into smart speakers and digital assistants to perform daily assessments. This naturally would depend on the devices involved having sufficiently good quality microphones as well as handling the necessary privacy issues, not to mention the assistance of companies like Apple and Amazon to implement, making it unlikely albeit altruistic.
Comments
Use BT, UW, LiDAR, NFC, TOF, AirDrop proximity detection and all other high-profile technologies to their extreme.
How hard can it be now that the Covid API already uses a form of proximity-detection and the Measurement app has sub-millimeter precision ?
I can see that an app could determine that a cough was NOT due to Covid. But to determine that it IS due to Covid when a dozen other things could be causing it is not reasonable.
https://www.snopes.com/fact-check/medicare-hospitals-covid-patients/
Not only that, but note the big drop in influenza cases, because hospitals have a financial incentive to pretend it is actually COVID-19. Further, most heart conditions are being classified as COVID-19 deaths, this is the problem when government gets involved in healthcare!
Snopes is HARDLY a reliable site for facts. Did you read about its founders? *rolls eyes*
LOL.... Nice spin!
Like most propaganda, it has a grain of truth to it. In this case, yes, those who get seriously ill or die from the virus tend to have other conditions as well. So they spin that to try and convince the fools that the virus really is a Democratic hoax.
This app (if it ships as one) could result in a lot of needless testing, or false negatives, hard to say either way with accuracy.
So, four or five "pre-existing conditions," zero of which would have had to do with COVID.
Funnily, if I got hit by a car tomorrow, four or five would be on my death certificate as pre-existing conditions, despite the car being what did me in. No matter what finally kills me, be it being decapitated in a sword battle in a warehouse with lightning all around, or just keeling over from one too many Old Bay potato chips, all these things will be on my death certificate.
Other common pre-existing conditions include such winners as "ever pregnant," "ever had an ear infection," "ever had a broken bone," "ex-smoker" and so many more. Ever had your tonsils out? That counts. So does an appendix removal, a vasectomy, a tubal ligation, and so, so much more. All counted by the CDC.
Both of you are misinterpreting what the CDC said by accident, not having read the original materials and going by what you've heard, or by believing what a venue has told you because I'd rather not consider the ramifications of either of you willfully misinterpreting what the study says.
Dial it back a bit. There's a lot in this thread that is over the commenting guidelines, including the use of the term "fake news" or a derivative.
AI will not be your political battleground.