Deep Nanometry: AI-Powered Tech Enables Rapid Detection of Rare Nanoparticles

Researchers at the University of Tokyo have unveiled Deep Nanometry (DNM), a novel analytical technique poised to revolutionize nanoparticle detection. This innovative approach combines advanced optical equipment with an unsupervised deep learning algorithm for noise reduction, enabling high-speed analysis of medical samples. DNM can detect particles as small as 30 nanometers at a rate exceeding 100,000 particles per second.

DNM's enhanced sensitivity allows for the accurate detection of trace amounts of rare particles, such as extracellular vesicles (EVs), which are crucial for early disease detection and drug delivery. The technology has demonstrated potential in identifying early signs of colon cancer, overcoming the limitations of conventional methods that require time-consuming pre-enrichment processes.

Yuichiro Iwamoto, a postdoctoral researcher at the Research Center for Advanced Science and Technology, emphasized DNM's ability to reliably detect rare particles in a short timeframe. The AI component filters out background noise, analogous to calming a turbulent ocean to spot a small boat. This advancement holds promise for various clinical diagnoses, vaccine development, environmental monitoring, and even signal denoising in electrical systems. Iwamoto dedicated this work to his late mother, hoping to make life-saving diagnostics faster and more accessible.