Disease-Smelling Machine May Revolutionise Early Diagnosis
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Medical detection dogs can smell many kinds of diseases like lung, breast, ovarian, bladder, and prostate cancers, and possibly Covid-19. In some cases, involving prostate cancer for example, the dogs had a 99% success rate in detecting the disease by sniffing patients' urine samples.

But since it takes long time to train such dogs, and their availability is limited, scientists have been hunting for ways of automating the amazing olfactory capabilities of the canine nose and brain, in a compact device.

A team of researchers at MIT and other institutions has come up with a system that can detect the chemical and microbial content of an air sample with even greater sensitivity than a dog's nose. They coupled this to a machine-learning process that can identify the distinctive characteristics of the disease-bearing samples.

Over the last few years, they have developed, and continued to improve on, a miniaturized detector system that incorporates mammalian olfactory receptors stabilized to act as sensors, whose data streams can be handled in real-time by a typical smartphone's capabilities. They envision a day when every phone will have a scent detector built in, just as cameras are now ubiquitous in phones. Such detectors, equipped with advanced algorithms developed through machine learning, could potentially pick up early signs of disease far sooner than typical screening regimes, researchers say—and could even warn of smoke or a gas leak as well.

In the latest tests, the team tested 50 samples of urine from confirmed cases of prostate cancer and controls known to be free of the disease, using both dogs trained and handled by Medical Detection Dogs and the miniaturized detection system. They then applied a machine-learning program to tease out any similarities and differences between the samples that could help the sensor-based system to identify the disease. In testing the same samples, the artificial system was able to match the success rates of the dogs, with both methods scoring more than 70%.

“The miniaturised detection system is actually 200 times more sensitive than a dog's nose in terms of being able to detect and identify tiny traces of different molecules, as confirmed through controlled tests,” the researchers mentioned. But in terms of interpreting those molecules, "it's 100 percent dumber." “That's where the machine learning comes in, to try to find the elusive patterns that dogs can infer from the scent, but humans haven't been able to grasp from a chemical analysis,” they added.

Source:
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0245530
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