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AI that listens for CANCER in farts: Scientists develop device they hope will spot deadly diseases 

The prototype of the sensor set includes a microphone through which bathroom sounds could be recorded.  Different sounds produced by urination, flatulence, solid defecation, and diarrhea are all influenced by your body's pathways and can indicate that something is wrong.

Scientists have developed a device that they hope can diagnose cancer by listening to sounds from your bathroom.

It works by using artificial intelligence to look for subtle changes in sound when someone defecates, urinates or passes gas.

The researchers built a database with hours of audio and video samples of healthy and sick patient excreta to establish a baseline to bootstrap the machine learning algorithm.

David Ancalle, the Georgia Tech University lead researcher who helped build the device, said: “We’re trying to find a non-invasive way for people to get a notification about whether or not they should get checked. Like, “Hey, your urine isn’t flowing at the rate it should. Your farts aren’t sounding like they should. You should check it out.”‘

It comes as experts explore new ways to diagnose cancer without invasive biopsies. Japanese experts are using tiny worms to detect pancreatic tumors, which are notoriously hard to catch and deadly.

The prototype of the sensor set includes a microphone through which bathroom sounds could be recorded.  Different sounds produced by urination, flatulence, solid defecation, and diarrhea are all influenced by your body's pathways and can indicate that something is wrong.

The prototype of the sensor set includes a microphone through which bathroom sounds could be recorded. Different sounds produced by urination, flatulence, solid defecation, and diarrhea are all influenced by your body’s pathways and can indicate that something is wrong.

Changes in pathways like the urethra and rectum can be caused by diseases like colorectal cancer, and the sound waves can reflect that.

Engineers assembled a mechanical device outfitted with pumps, nozzles and tubes that recreate the physics behind every bathroom sound, which they determined by sifting through publicly available audio and video samples of excreta.

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After analyzing the sound waves and frequency spectrum for each, the team fed the information into a machine learning algorithm. Their artificial intelligence technology was able to learn from the mountain of collected sound data.

Armed with the sound wave analyzes of different bathroom noises, the researchers primed the mechanical device which, aptly, is called the Synthetic Human Acoustic Reproduction Test. Yes SHART.

They pumped SHART with water, recreating those sound waves.

Their algorithm identified the source of the sounds – urination, flatulence, solid defecation and diarrhea – 98% of the time.

The ultimate goal is to use an inexpensive sensor to pick up sounds and feed them into AI technology.

Maia Gatlin, an aerospace engineer at Georgia Tech Research Institute who worked on the device, said: “Each of the sounds has been a lot of thought put into it. There was a subsystem for every sound on this little machine.

The SHART machine works by mimicking the physics behind different bathroom sounds using its plethora of nozzles and tubes.  Researchers pump it with water and sounds that reflect reality are produced

The SHART machine works by mimicking the physics behind different bathroom sounds using its plethora of nozzles and tubes. Researchers pump it with water and sounds that reflect reality are produced

How will the device work?

A sensor will pick up the sound of bathroom excretions – poo, pee, etc.

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The researchers behind the device explored the placement of the sensors in the toilet.

The synthetic human acoustic reproduction test machine is currently preparing an AI algorithm to one day detect deadly diseases using sound waves.

The machine recreates Researchers pump the SHART machine with water and it is able to recreate the physics behind the sounds that are picked up by the sensor.

The distinct sounds result from changes in pathways like the urethra and rectum, which can be caused by diseases such as colorectal cancer

The resulting sound waves are fed into an AI device for analysis

The machine learning algorithm, which was bootstrapped with publicly available bathroom sound samples, could potentially detect discrepancies in sounds indicating health issues.

The device would have a huge impact globally by detecting outbreaks early and preventing them from spreading further. Cholera, for example, is a diarrheal disease that the device would be able to detect, potentially preventing thousands of deaths.

“And as we classify these events, we can start collecting this data. He may say, “Hey, we’re seeing an outbreak of a lot of diarrhea.” Then we can start quickly diagnosing what’s going on in an area,” Ms Gatlin said.

It is estimated that 1.3 to 4 million cases of cholera are diagnosed worldwide each year and the disease kills between 20,000 and 143,000 per year.

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Diarrheal diseases like cholera are a particularly acute problem in parts of the world where people are more likely to be undernourished and deprived of accessible, quality health care, such as in sub-Saharan Africa and rural areas of South Asia. This class of diseases is the third leading cause of infant mortality in the world, just behind pneumonia and complications of premature delivery.

The Georgia Tech team’s research findings, presented last week at the annual meeting of the American Physical Society’s Division of Fluid Dynamics, represent just the latest example of sewer materials being used for the good for public health.

Sewage monitoring has proven to be an important tool for monitoring Covid-19 outbreaks days before cases appear, as the genetic material of the virus can be detected in feces.

Mr. Ancalle stressed that his team is on a mission to find an affordable and widely accessible sensor device so that the technology can be scaled up to cover wider populations.

He said: “We’re not trying to come up with a million dollar piece of equipment. We try to ensure that everyone can afford it, especially since the project focuses on urban areas where health systems are weak. The affordability aspect is very important to us.

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