Trained dogs can detect many kinds of disease — including lung, breast, bladder, and prostate cancers and possibly COVID-19 — simply through smell. But it takes time to train such dogs and their availability and time is limited. Researchers have 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.
Trained dogs can identify cancers that don't have any identical biomolecular signatures in common. Using powerful analytical tools — including gas chromatography mass spectrometry (GCMS) and microbial profiling — if samples from skin cancer, bladder cancer, breast cancer, and lung cancer are analyzed, they have nothing in common, yet dogs can generalize from one kind of cancer to be able to identify the others.
A miniaturized detector system incorporates mammalian olfactory receptors stabilized to act as sensors, whose data streams can be handled in real time by a typical smartphone's capabilities. Such detectors, equipped with advanced algorithms developed through machine learning, could potentially pick up early signs of disease far sooner than typical screening regimes and could even warn of smoke or a gas leak.
The team tested 50 samples of urine from confirmed cases of prostate cancer and controls known to be free of the disease, using both trained dogs and the miniaturized detection system. They applied a machine-learning program to tease out any similarities and differences between the samples that could help the sensor-based system identify the disease. In testing the same samples, the artificial system was able to match the success rates of the dogs.
The miniaturized 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. But in terms of interpreting those molecules, it is much less effective. That's where machine learning is used to find the elusive patterns that dogs can infer from the scent but humans haven't been able to grasp from a chemical analysis.
While the physical apparatus for detecting and analyzing the molecules in air has been under development for several years — with much of the focus on reducing its size — until now, the analysis was lacking. This achievement provides a solid framework for further research to develop the technology to a level suitable for clinical use.
The researchers hope to test a far larger set of samples — perhaps 5,000 — to pinpoint in greater detail the significant indicators of disease.