Wearing masks has become a daily norm due to the infectious disease pandemic. Masks that block viruses are evolving into sensors and electronic noses that detect disease symptoms occurring within the body. While this is still at the proof-of-concept stage, its accuracy is similar to tests conducted in hospitals, and it is expected that commercializing this technology will aid in early diagnosis and management of diseases. The day is approaching when breath tests, similar to alcohol tests, will become an essential step in disease diagnosis following imaging and blood tests.
◇Detection of ammonia using conductive plastics
Professor Corrado Di Natale from the University of Rome and his research team noted on the 7th (local time) that they have succeeded in integrating special breathing sensors into mask fabric to detect metabolic products related to chronic kidney disease, as published in the international journal 'ACS Sensors.'
Chronic kidney disease is a chronic illness characterized by a reduction or damage to kidney function lasting more than three months. The kidneys filter waste products from the blood and excrete them as urine. For example, when proteins are broken down in the liver, urea (CO(NH₂)₂) is produced as a byproduct. The kidneys filter blood to excrete urea in urine. When kidney function declines, urea accumulates in saliva, and the exhalation increases ammonia (NH₃), which is a breakdown product of urea.
The research team coated silver electrodes with conductive polymer materials. By attaching porphyrin, which is sensitive to volatile compounds, to the polymer material, sensitivity was enhanced. The electrodes were placed between medical mask materials and connected to an external reading device via wires. When specific gases reacted with the polymer material, the electric resistance changed. The researchers confirmed through initial experiments in the air that the sensor effectively detected metabolites related to chronic kidney disease, such as ammonia, ethanol, propanol, and acetone.
The research team tested the mask on 100 participants. Half of the participants were diagnosed with chronic kidney disease. The test results showed that the mask sensor identified patients with chronic kidney disease with a sensitivity of 84%, while individuals without the disease had a specificity of 88%. Additionally, through sensor detection data, it was also possible to estimate the stage of the disease.
The U.S. Centers for Disease Control and Prevention (CDC) estimates that there are 35 million chronic kidney disease patients in the country. The medical community believes that there are more patients than that, as many are unaware in the early stages. Currently, chronic kidney disease is diagnosed through blood and urine tests in hospitals. The research team stated that using masks equipped with sensors could more easily filter out a larger number of patients, which could also help evaluate the effectiveness of treatments.
◇Diagnosis of COVID-19, cancer, and Parkinson's disease using masks
Professor Weo Gao's research team at the California Institute of Technology (Caltech) also announced a smart mask in August last year that can be used to manage various respiratory diseases such as asthma, chronic obstructive pulmonary disease (COPD), and chronic kidney disease. This mask can analyze chemical substances present in exhalation in real-time. The research team stated that it could detect nitrite levels, which indicate airway inflammation in asthma patients, and manage ammonia levels in patients with chronic kidney disease.
Chemical sensors are better at detecting liquids. The research team cooled the volatile chemicals present in exhalation into liquids. Due to space limitations in the mask, a separate cooler could not be placed. Instead, they cooled the exhalation components by evaporating a moisture-rich, soft material known as hydrogel, which absorbed heat. Once the exhalation components became liquid, they were transported to the sensor by capillaries, similar to how plant roots absorb water. The research team reported that the smart mask also detects pathogens in aerosol (fine particulate) form.
Exhaled breath contains over 3,500 types of volatile organic compounds. Scientists believe that when an organ is diseased, the cells in that organ release unique volatile compounds that are later expelled through the lungs. Bio companies are developing fingerprint sensors to identify disease-specific compound characteristics and conducting clinical trials. They have detected diseases such as liver and kidney diseases, esophageal cancer, lung cancer, colorectal cancer, and even Parkinson's disease and COVID-19 through breath tests.
British medical device company Olston Medical is conducting clinical trials to diagnose lung cancer by analyzing breath samples from over 4,000 patients using a mask-like electronic nose called 'ReCIVA' in collaboration with the University of Cambridge. In 2023, Olston Medical and the University of Cambridge research team discovered that patients with cirrhosis had high levels of limonene, a compound found in citrus peels in breath samples analyzed with the ReCIVA electronic nose.
In 2021, researchers at Leiden University in the Netherlands reported that they had confirmed COVID-19 infection using the electronic nose 'SpiroNose' from medical device company Breathomics. The research team utilized artificial intelligence (AI) to compare breath tests of infected and non-infected individuals to identify the unique characteristics of the volatile organic compounds in infected individuals. In actual tests, the AI electronic nose identified 33 out of 34 infected individuals, with one case being someone with a viral load that was unlikely to be contagious.
Whereas electronic nose masks were previously cumbersome for daily use, they have now evolved to combine regular medical masks with sensors. This means health management can be integrated into everyday life.
References
ACS Sensors (2025), DOI: https://doi.org/10.1021/acssensors.4c03227
Science (2024), DOI: https://doi.org/10.1126/science.adn6471
Biomedicines (2023), DOI: https://doi.org/10.3390/biomedicines11112957
medRxiv (2021), DOI: https://doi.org/10.1101/2021.02.14.21251712