AI sniffs earwax and detects Parkinson’s with 94% accuracy

Previous research has shown that changes in sebum, an oily substance secreted by the skin, could help identify people with PD. Specifically, sebum from people with PD may have a characteristic smell because volatile organic compounds (VOCs) released by sebum are altered by disease progression -- including neurodegeneration, systemic inflammation and oxidative stress. However, when sebum on the skin is exposed to environmental factors like air pollution and humidity, its composition can be altered, making it an unreliable testing medium. But the skin inside the ear canal is kept away from the elements. So, Hao Dong, Danhua Zhu and colleagues wanted to focus their PD screening efforts on ear wax, which mostly consists of sebum and is easily sampled.

To identify potential VOCs related to PD in ear wax, the researchers swabbed the ear canals of 209 human subjects (108 of whom were diagnosed with PD). They analyzed the collected secretions using gas chromatography and mass spectrometry techniques. Four of the VOCs the researchers found in ear wax from people with PD were significantly different than the ear wax from people without the disease. They concluded that these four VOCs, including ethylbenzene, 4-ethyltoluene, pentanal, and 2-pentadecyl-1,3-dioxolane, are potential biomarkers for PD.

Dong, Zhu and colleagues then trained an artificial intelligence olfactory (AIO) system with their ear wax VOC data. The resulting AIO-based screening model categorized with 94% accuracy ear wax samples from people with and without PD. The AIO system, the researchers say, could be used as a first-line screening tool for early PD detection and could pave the way for early medical intervention, thereby improving patient care.

"This method is a small-scale single-center experiment in China," says Dong. "The next step is to conduct further research at different stages of the disease, in multiple research centers and among multiple ethnic groups, in order to determine whether this method has greater practical application value."

The authors acknowledge funding from the National Natural Sciences Foundation of Science, Pioneer and Leading Goose R&D Program of Zhejiang Province, and the Fundamental Research Funds for the Central Universities.