AI-Powered Ear Wax Analysis: A Breakthrough in Early Parkinson's Disease Detection

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

Most treatments for Parkinson's disease (PD) only slow disease progression. Early intervention for the neurological disease that worsens over time is therefore critical to optimize care, but that requires early diagnosis. Current tests, like clinical rating scales and neural imaging, can be subjective and costly. Now, researchers in ACS' Analytical Chemistry report the initial development of a system that inexpensively screens for PD from the odors in a person's earwax. 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."

Ear Wax Reveals Parkinson's Disease Biomarkers - Neuroscience News

Summary: A novel screening method for Parkinson's disease (PD) analyzes the volatile compounds in ear wax to detect early signs of the condition. Researchers found that four specific volatile organic compounds (VOCs) are significantly different in people with PD. Using this information, they developed an AI-powered olfactory system that distinguished between PD and non-PD samples with 94% accuracy. This inexpensive, non-invasive technique could revolutionize early detection and treatment strategies. Most treatments for Parkinson's disease (PD) only slow disease progression. Early intervention for the neurological disease that worsens over time is therefore critical to optimize care, but that requires early diagnosis. Current tests, like clinical rating scales and neural imaging, can be subjective and costly. Now, researchers in ACS' Analytical Chemistry report the initial development of a system that inexpensively screens for PD from the odors in a person's ear wax. 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." Funding: 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. Author: Emily Abbott Source: ACS Contact: Emily Abbott - ACS Image: The image is credited to Neuroscience News Original Research: Open access. "An Artificial Intelligence Olfactory-Based Diagnostic Model for Parkinson's Disease Using Volatile Organic Compounds from Ear Canal Secretions" by Danhua Zhu et al. Analytical Chemistry Abstract An Artificial Intelligence Olfactory-Based Diagnostic Model for Parkinson's Disease Using Volatile Organic Compounds from Ear Canal Secretions Parkinson's Disease (PD), a frequently diagnosed neurodegenerative condition, poses a major global challenge. Early diagnosis and intervention are crucial for PD treatment. This study proposes a diagnostic model for PD that analyzes volatile organic compounds (VOCs) from ear canal secretions (ECS). Using gas chromatography-mass spectrometry (GC-MS) to examine ECS samples from patients, four VOC components (ethylbenzene, 4-ethyltoluene, pentanal, and 2-pentadecyl-1,3-dioxolane) were identified as biomarkers with statistically significant differences between PD and non-PD patients. Diagnostic models based on these VOC components demonstrate strong capability in identifying and classifying PD patients. To enhance the accuracy and efficiency of the PD diagnostic model, this study introduces a protocol for extracting features from chromatographic data. By integrating gas chromatography-surface acoustic wave sensors (GC-SAW) with a convolutional neural network (CNN) model, the system achieves an accuracy of up to 94.4%. Further enhancements to the diagnostic model could pave the way for a promising new PD diagnostic solution and the clinical use of a bedside PD diagnostic device.

New screening method for Parkinson's analyzes odors in ear wax

American Chemical SocietyJun 18 2025 Most treatments for Parkinson's disease (PD) only slow disease progression. Early intervention for the neurological disease that worsens over time is therefore critical to optimize care, but that requires early diagnosis. Current tests, like clinical rating scales and neural imaging, can be subjective and costly. Now, researchers in ACS' Analytical Chemistry report the initial development of a system that inexpensively screens for PD from the odors in a person's ear wax. 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. 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." Hao Dong American Chemical Society Journal reference: Chen, X., et al. (2025). An Artificial Intelligence Olfactory-Based Diagnostic Model for Parkinson's Disease Using Volatile Organic Compounds from Ear Canal Secretions. Analytical Chemistry. doi.org/10.1021/acs.analchem.5c00908.

Ear wax composition shown to be early warning sign of Parkinson's

Analysis of ear wax to detect Parkinson's Disease would be a cheap, non-invasive way to detect the condition in its early stages Catching the onset of Parkinson's Disease early can be critical to slowing the disease's progression and improving a patient's life. A new test that uses nothing more than a sample of someone's ear wax is set to do exactly that. Despite the fact that an estimated 10 million people live with Parkinson's Disease, there is currently no definitive test to spot the condition in its early stages. To identify someone with Parkinson's, doctors rely on cognitive and motor skill testing, which can be inexact and have a degree of bias involved. Other tests include imaging, which can rule out other conditions rather than confirm a Parkinson's diagnosis, and a response to Parkinson's medication - in which people who might not have the disease take a prescription designed to combat its effects. Progress in diagnosing the disease is happening, however. Earlier this year, we even reported on how a simple eye test might help spot the condition early. One of the more promising ways to spot Parkinson's early has to do with the way in which the disease impacts the oily layer of our skin known as sebum. In 2021, a team of scientists found 10 biomarkers that were heightened or lowered in patients with Parkinson's by analyzing sebum collected with non-invasive skin swabs. Using that data, the researchers were able to distinguish between those who had the condition and those who didn't with an 85% rate of accuracy. Understanding that our skin is exposed to environmental conditions that can alter its composition but seeing the value in using sebum to detect Parkinson's, researchers led by scientists from a range of universities and research institutes in China turned to a different source of sebum: ear wax. Unlike our skin secretions, ear wax is more protected and therefore offers a more pure read on our sebum composition. To conduct their study, the researchers swabbed the ears of 209 adults, 108 of whom had Parkinson's disease. After analyzing the wax using gas chromatography and mass spectrometry, they identified four different volatile organic compounds (VOC) that were significantly lower in Parkinson's patients than in those without the condition. Next, the team trained a sniff-enabled AI system on the data. Once the training was complete, the system was able to distinguish Parkinson's patients from non-Parkinson's patients with an accuracy rate of 94%. Such a successful test, say the researchers, could provide doctors with an inexpensive, non-invasive early diagnostic tool in identifying Parkinson's Disease. However, they say more research is needed. "This method is a small-scale single-center experiment in China," says study co-author Hao 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."

Ear Wax as a Possible Screening Medium for Parkinson's Disease | Newswise

Newswise -- Most treatments for Parkinson's disease (PD) only slow disease progression. Early intervention for the neurological disease that worsens over time is therefore critical to optimize care, but that requires early diagnosis. Current tests, like clinical rating scales and neural imaging, can be subjective and costly. Now, researchers in ACS' Analytical Chemistry report the initial development of a system that inexpensively screens for PD from the odors in a person's ear wax. 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. ### The American Chemical Society (ACS) is a nonprofit organization founded in 1876 and chartered by the U.S. Congress. ACS is committed to improving all lives through the transforming power of chemistry. Its mission is to advance scientific knowledge, empower a global community and champion scientific integrity, and its vision is a world built on science. The Society is a global leader in promoting excellence in science education and providing access to chemistry-related information and research through its multiple research solutions, peer-reviewed journals, scientific conferences, e-books and weekly news periodical Chemical & Engineering News. ACS journals are among the most cited, most trusted and most read within the scientific literature; however, ACS itself does not conduct chemical research. As a leader in scientific information solutions, its CAS division partners with global innovators to accelerate breakthroughs by curating, connecting and analyzing the world's scientific knowledge. ACS' main offices are in Washington, D.C., and Columbus, Ohio. Registered journalists can subscribe to the ACS journalist news portal on EurekAlert! to access embargoed and public science press releases. For media inquiries, contact [email protected].

Ear wax may hold the key to early Parkinson's diagnosis

An unlikely body byproduct may be able to help doctors diagnose Parkinson's Disease early. According to a new Chinese study, which was published in Analytical Chemistry, ear canal secretion, or ear wax, contain chemical compounds which can be telltale signs of Parkinson's. During the study, researchers examined ear canal secretions from 209 people. About half (108 of the participants) had Parkinson's. In their examinations, scientists were able to identify four volatile organic compounds (VOC) that were notably different in those with the disease. Those compounds, or biomarkers, were ethylbenzene, 4-ethyltoluene, pentanal, and 2-pentadecyl-1,3-dioxolane. The scientists then trained an artificial intelligence olfactory (AIO) system on the biomarker data. And once training was complete, the AIO system was able to successfully determine which patients had Parkinson's and which did not. The system was accurate 94% of the time.

Scientists Use AI -- and Earwax -- for Early Detection of Parkinson's Disease With 94 Percent Accuracy

A groundbreaking study from researchers in China analyzing earwax with the help of artificial intelligence offers a glimmer of hope in the fight against Parkinson's disease. Using an innovative diagnostic method that could transform early detection, scientists at Zhejiang University achieved a remarkable 94.4 percent accuracy rate in identifying Parkinson's cases, according to findings published in Analytical Chemistry. The method harnesses the power of machine learning to analyze volatile organic compounds found in ear canal secretions, potentially offering a quick, non-invasive, and cost-effective alternative to current diagnostic techniques, which often catch the disease too late for optimal early intervention. "Early diagnosis and intervention are crucial for PD treatment," the authors of the study wrote. Traditionally, Parkinson's is diagnosed based on physical symptoms such as tremors or muscle stiffness observed by neurologists. Such methods often lead to a diagnosis only after significant damage has already occurred to dopamine-producing brain cells. The study involved collecting earwax samples from 209 participants, 108 diagnosed with Parkinson's and 101 without. The samples were analyzed using two chemical detection techniques: gas chromatography-mass spectrometry and gas chromatography-surface acoustic wave. Earwax was chosen specifically because of its stable environment, which protects it from contamination by external factors like lotions or air pollutants. The study noted, "Ear canal secretions exist in a more stable environment, simplifying sample collection and significantly enhancing the accuracy of analysis." Researchers then employed a convolutional neural network, an advanced machine learning algorithm, to process chromatographic data from the samples. The network classified each sample as either Parkinson's-positive or Parkinson's-negative based on patterns in the profiles of the organic compounds. By converting chemical data into structured visual representations, the AI system goes beyond measuring chemical concentrations to detect nuanced patterns. The potential advantage of this method lies in its simplicity and accessibility, particularly compared to conventional diagnostic tools that often require expensive imaging or extended symptom observation. The implications are particularly compelling when considering the scale of the impact of Parkinson's. The World Health Organization estimates that by 2030, some 9 million people globally will be living with the degenerative brain disease, which progressively affects movement, coordination, and cognitive functions. While retrospective data produced high accuracy rates, the test has not yet been validated in prospective clinical trials. Real-world performance in clinical settings remains a key unknown. "While further validation is required, this test could redefine how we approach Parkinson's screening," the study's authors conclude.