This AI Tool Can Track Ones Location By Using Microorganisms In Their Body (image @Pixabay)
Reportedly, scientists have developed a new AI tool which is capable of finding out someone’s recent location using a sample of microorganisms they may have collected on their travels. The study was published in the journal, Genome Biology, and Evolution. The scientists from Lund University in Sweden has developed a Microbiome Geographic Population Structure (mGPS), which enables the process of pinpointing a person’s locations.
According to the researchers, the microorganisms act like microscopic fingerprints. Like human being, the microbial communities display geographical traces. This knowledge has led to the development of the AI tool. The new technology allows the scientists to identify whether someone recently visited the beach, deboarded at a nearby train station or took a walk through the park.
Departed from the traditional navigation system that uses GPS, the mGPS uses ground-breaking AI technology to localise the environments one may have visited by identifying the microbiome associated with that area. The word microbiome is used to describe all the microorganisms (bacteria, fungi, algae) in a particular environment.
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“In contrast to human DNA, the human microbiome changes constantly when we come into contact with different environments,” Eran Elhaik, a researcher at Lund University and the study’s co-author told The Atlas.
“By tracing where your microorganisms have been recently, we can understand the spread of disease, identify potential sources of infection and localise the emergence of microbial resistance. This tracing also provides forensic keys that can be used in criminal investigations”, he further added.
To train the AI, the researchers fed a huge quantity of microbiome data from different environments to its AI model. Microbe genomes collected from subways and urban environments in 53 cities, 237 soil samples from 18 countries, and 131 marine samples from nine bodies of water were used for the training.
“We analyzed extensive datasets of microbiome samples from urban environments, soil and marine ecosystems and trained an AI model to identify the unique proportions of these fingerprints and link them to geographical coordinates,” Elhaik said. “The results turned out to be a very powerful tool that can pinpoint the source site of a microbiome sample with impressive precision”.
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According to the study, the mGPS was successful in pinpointing the city source for 92 per cent of city samples. In order to further challenge the system’s accuracy, it was trained on data from the three most extensively sampled cities: New York, Hong Kong, and London. In Hong Kong, the mGPS was able to distinguish between two subway stations, which are just 564 ft apart. At the same time in New York City, it differentiated a kiosk from a handrail, which were less than a meter away.
At the same time, in London, the accuracy took a hit as only half the samples were correctly assigned to their geographical cluster. The reason for low efficiency in the experiment were attributed to the unkempt condition of London underground stations.
The new study opens up new doors of possibilities within medicine, epidemiology and forensics, and adding microbiome data as it is collected will only further improve the tool, said the researchers.
(With inputs from agencies)
