Microphone-equipped toilet will detect diseases and give you advice

There are many diseases that could potentially be detected through human waste. One such infection includes cholera. Cholera is a bacterial disease that causes diarrhea and affects millions of people each year. It results in approximately 150,000 deaths worldwide, each year.

Cholera is spread through contaminated food and water. Largeepidemics that spread the bacterium are related to fecal contamination of water
or food. It can sometimes be spread through undercooked shellfish and other
seafood-related infections, as well.   

Cholera is caused by the vibrio cholerae bacteria

Cholera is a diarrheal illness caused by an infection in the
intestine with Vibrio cholerae bacteria. Although the infection is often mild, it can sometimes be life-threatening. According to the Center for Disease
Control and Prevention (CDC),
about one in 10 people with cholera will experience severe symptoms, including thirst, restlessness, and diarrhea.

Signs of dehydration while a patient has diarrhea could also
be a warning that someone has cholera. The signs include rapid heart rate and
low blood pressure. People with cholera can experience extreme dehydration,
which can lead to kidney failure and death.

In order for patients to be treated for cholera, they must
know that they have the disease first. However, it can be a sensitive and difficult
task to monitor bowel diseases, such as cholera. Maia Gatlin, a research
engineer at the Georgia Institute of Technology, created a way to use artificial
intelligence to detect diarrhea. She calls her presentation The Feces
Thesis: Using Machine Learning to Detect Diarrhea.

A noninvasive microphone sensor can detect disease in bowels

Gatlin will be presenting her thesis and the sensor tool
today, Dec. 5, at the annual Meeting of the Acoustical Society of America, explaining
her findings on how machine learning can be used to detect diseases in the
bowel. She uses a noninvasive microphone sensor to identify bowel diseases,
without necessarily collecting identifiable information, meaning the AI can determine
the infection without having to be examined in a medical facility to collect additional
data.

The method involves using the microphone and machine
learning to detect diarrhea. Gatlin and her research team tested the sensor
technique on audio files from online resources. Each single audio sample of an excretion, or
bowel movement, was converted into a spectrogram, which captures sound in an
image. A spectrogram
is a visual way of representing the sound of a signal over time, representing a
visual of sound.

The different types of excretion create different features in
the audio and the spectrogram. The diarrheal tone produced more of a random
sounding audio to the researchers. The spectrogram images were then used as input
and were put into a machine learning algorithm. The algorithm’s performance was
then tested against data with and without background noises to make sure it was
gaining the information to interpret the sounds using the sensor, regardless of
the environment.

The sensor can be used in places with persistent cholera outbreaks

Gatlin wants to use the AI sensor in locations where bowel
infections such as cholera are prevalent. “The hope is that this sensor, which
is small in footprint and noninvasive in approach, could be deployed to areas
where cholera outbreaks are a persistent risk,” said Gatlin.

 “The sensor could
also be used in disaster zones (where water contamination leads to spread of
waterborne pathogens), or even in nursing/hospice care facilities to
automatically monitor bowel movements of patients.” Gatlin can also see the future
usage of the sensory as being utilized in homes for individuals to test their own
wellbeing through their bowel movements. She stated that “perhaps someday, our
algorithm can be used with existing in-home smart devices to monitor one’s own
bowel movements and health!”