Portable device capable of identifying, in the air, by automatic learning, particles that cause allergies

Portable device capable of identifying, in the air, by automatic learning, particles that cause allergies https://i0.wp.com/www.eresviral.com/wp-content/uploads/2018/11/Dispositivo-portátil-capaz-de-identificar-en-el-aire-por-aprendizaje-automático-partículas-causantes-de-alergias.jpg?fit=260%2C130&ssl=1

Portable device capable of identifying, in the air, by automatic learning, particles that cause allergies





Portable device capable of identifying, in the air, by automatic learning, particles that cause allergies


Every minute that goes by, the typical human human usually breathes between 100 and 1,000 bioaerosols, including pollen, spores, toxins and microbes, the number being even greater, 1 million or more, in highly contaminated areas. These tiny biological particles can trigger allergies, asthma and other diseases.



Quantifying exposure to these biological particles is difficult, can be time consuming and expensive. Current methods for identifying bioaerosols are based on technology from 50 years ago: researchers collect samples using spore filters or traps, and then transport them to laboratories, where they are stained and inspected under microscopes by scientists. Another challenge is that there is only a small number of air sampling stations that have detection or measurement capabilities of bioaerosols.



Researchers at the University of California at Los Angeles (UCLA) in the United States have invented a portable device that uses holograms and machine learning to identify and measure aerial biological particles, or bioaerosols, that originate from living organisms such as fungi and plants. Trained to recognize five common allergens, in the experiments the system classified the samples with 94 percent accuracy using deep learning (a modality of machine learning). This was verified by the integrated team, among others, by Yichen Wu and Aydogan Ozcan of UCLA.







The portable device developed by scientists at UCLA identifies and measures aerial biological particles. (Image: Ozcan Research Group / UCLA)



The device filters air and traps particles in a sticky surface that is illuminated by a laser, which generates a hologram. An image sensor chip scans the hologram and sends the data to a remote server.



There, an artificial intelligence system powered by a neural network cleans the image, which is then processed by an algorithm that cuts it into sections that represent the particles. A second neural network classifies these particles from a series of types of pre-loaded allergens.



The device can be manufactured at a moderate cost. It weighs less than 600 grams (approximately the same as three smartphones) and measures about 14 centimeters wide, 17 long and 6 thick.



It is the first affordable portable device that detects and classifies aerial biological particles automatically and without needing their labeling using pigments. Since it is controlled wirelessly, it could be transported in unmanned vehicles such as drones, which would allow scientists to monitor locations that would otherwise be dangerous or difficult for humans to access. The technology could also be used in a network of sensors covering a wide area, which would allow scientists to create abundance maps of pollen, spores and microbes.



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