Deep Nanometry: AI-Powered Nanoparticle Detection Breakthrough for Early Disease Diagnosis

Deep Nanometry reveals hidden nanoparticles

Researchers including those from the University of Tokyo developed Deep Nanometry, an analytical technique combining advanced optical equipment with a noise removal algorithm based on unsupervised deep learning. Deep Nanometry can analyze nanoparticles in medical samples at high speed, making it possible to accurately detect even trace amounts of rare particles. This has proven its potential for detecting extracellular vesicles indicating early signs of colon cancer, and it is hoped that it can be applied to other medical and industrial fields. Did you know your body is full of microscopic particles smaller than cells? These include what are known as extracellular vesicles (EVs) which can be useful in early disease detection and also in drug delivery. However, EVs are very rare, and finding them among millions of other particles required time consuming and expensive pre-enrichment process. This has prompted researchers, including postdoctoral researcher Yuichiro Iwamoto from the Research Center for Advanced Science and Technology and his team, to find a means to detect EVs quickly and reliably. "Conventional measurement techniques often have limited throughput, making it difficult to reliably detect rare particles in a short space of time," said Iwamoto. "To address this, we developed Deep Nanometry (DNM), a new nanoparticle detection device and an unsupervised deep learning noise-reduction method to boost its sensitivity. This allows for high throughput, making it possible to detect rare particles such as EVs." At the heart of DNM is its ability to detect particles as small as 30 nanometers (billionths of a meter) in size, while also being able to detect more than 100,000 particles per second. With conventional high-speed detection tools, strong signals are detected but weak signals may be missed, while DNM is capable of catching them. This might be analogous to searching for a small boat on a turbulent ocean amidst crashing waves -- it becomes much easier if the waves would dissipate leaving a calm ocean to scout for the boat. The artificial intelligence (AI) component helps in this regard, by learning the characteristics of, and thus helping filter out, the behavior of the waves. This technology can be expanded to a wide range of clinical diagnoses that rely on particle detection, and it also has potential in fields such as vaccine development and environmental monitoring. Additionally, the AI-based signal denoising could be applied to electrical signals, amongst others. "The development of DNM has been a very personal journey for me," said Iwamoto. "It is not only a scientific advancement, but also a tribute to my late mother, who inspired me to research the early detection of cancer. Our dream is to make life-saving diagnostics faster and more accessible to everyone."

Deep Nanometry: Deep learning system detects disease-related nanoparticles

Researchers, including those from the University of Tokyo, developed Deep Nanometry, an analytical technique combining advanced optical equipment with a noise removal algorithm based on unsupervised deep learning. Deep Nanometry can analyze nanoparticles in medical samples at high speed, making it possible to accurately detect even trace amounts of rare particles. This has proven its potential for detecting extracellular vesicles indicating early signs of colon cancer, and it is hoped that it can be applied to other medical and industrial fields. The body is full of microscopic particles smaller than cells. These include extracellular vesicles (EVs), which can be useful in early disease detection and also in drug delivery. However, EVs are very rare, and finding them among millions of other particles requires a time-consuming and expensive pre-enrichment process. This has prompted researchers, including postdoctoral researcher Yuichiro Iwamoto from the Research Center for Advanced Science and Technology and his team, to find a means to detect EVs quickly and reliably. "Conventional measurement techniques often have limited throughput, making it difficult to reliably detect rare particles in a short space of time," said Iwamoto. "To address this, we developed Deep Nanometry (DNM), a new nanoparticle detection device and an unsupervised deep learning noise-reduction method to boost its sensitivity. This allows for high throughput, making it possible to detect rare particles such as EVs." The work has been published in Nature Communications. At the heart of DNM is its ability to detect particles as small as 30 nanometers (billionths of a meter) in size, while also being able to detect more than 100,000 particles per second. With conventional high-speed detection tools, strong signals are detected but weak signals may be missed, while DNM is capable of catching them. This might be analogous to searching for a small boat on a turbulent ocean amidst crashing waves -- it becomes much easier if the waves dissipate, leaving a calm ocean to scout for the boat. The artificial intelligence (AI) component helps in this regard, by learning the characteristics of, and thus helping filter out, the behavior of the waves. This technology can be expanded to a wide range of clinical diagnoses that rely on particle detection, and it also has potential in fields such as vaccine development and environmental monitoring. Additionally, AI-based signal denoising could be applied to electrical signals, among others. "The development of DNM has been a very personal journey for me," said Iwamoto. "It is not only a scientific advancement, but also a tribute to my late mother, who inspired me to research the early detection of cancer. Our dream is to make life-saving diagnostics faster and more accessible to everyone."