Chennai: In what could change the current disease diagnosis sytem, 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.
Scientists coupled this to a machine-learning process that can identify the distinctive characteristics of the disease-bearing samples.
Researchers say the findings could someday lead to an automated odour-detection system small enough to be incorporated into a cellphone.
The study has been published in the journal PLOS One, in a paper by Clare Guest of Medical Detection Dogs in the U.K., Research Scientist Andreas Mershin of MIT, and 18 others at Johns Hopkins University, the Prostate Cancer Foundation, and several other universities and organizations.
According to Mershin, “Dogs, for now 15 years or so, have been shown to be the earliest, most accurate disease detectors for anything that we’ve ever tried.”
He said and their performance in controlled tests has in some cases exceeded that of the best current lab tests. “So far, many different types of cancer have been detected earlier by dogs than any other technology.”
The dogs apparently pick up connections that have so far eluded human researchers: When trained to respond to samples from patients with one type of cancer, some dogs have then identified several other types of cancerâeven though the similarities between the samples weren’t evident to humans.
These dogs can identify cancers that don’t have any identical biomolecular signatures in common, nothing in the odorants, Mershin said. Using powerful analytical tools including gas chromatography mass spectrometry (GCMS) and microbial profiling, “if you analyze the samples from, let’s say, skin cancer and bladder cancer and breast cancer and lung cancerâall things that the dog has been shown to be able to detect — they have nothing in common.”
Yet the dog can somehow generaliae from one kind of cancer to be able to identify the others.
Over the last few years, Mershin and the team 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.
He envisions 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, he saysâand could even warn of smoke or a gas leak as well.