Researchers have developed a portable, artificially intelligent olfactory system, or e-nose, that could someday diagnose Parkinson’s disease (PD) in a doctor’s office.
People with PD secrete increased sebum, along with increased production of yeast, enzymes, and hormones, which combine to produce certain odors. Researchers have used gas chromatography (GC)-mass spectrometry to analyze odor compounds in the sebum of people with PD.
The researchers developed an e-nose, combining GC with a surface acoustic wave sensor and machine learning algorithms. The team collected sebum samples from 31 PD patients and 32 healthy controls by swabbing their upper backs with gauze. They analyzed volatile organic compounds emanating from the gauze with the e-nose, finding three odor compounds (octanal, hexyl acetate, and perillic aldehyde) that were significantly different between the two groups, which they used to build a model for PD diagnosis.
Next, they analyzed sebum from an additional 12 PD patients and 12 healthy controls, finding that the model had an accuracy of 70.8 percent in predicting PD. The model was 91.7 percent sensitive in identifying true PD patients, but its specificity was only 50 percent, indicating a high rate of false positives. When machine learning algorithms were used to analyze the entire odor profile, the accuracy of diagnosis improved to 79.2 percent. The team needs to test the e-nose on many more people to improve the accuracy of the models. The researchers say they also need to consider factors such as race.
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