Laser coherent Raman scattering microscopyIt is a CRS coherent Raman scattering spectroscope specially designed for the spectral analysis of diseased tissues, which is very suitable for spectral analysis and imaging of tumor chemicals by combining Coherent Raman Scattering coherent Raman scattering technology with traditional microscopy.
Laser coherent Raman scattering microscopy is usedTwo synchronized femtosecond ultrafast laser beams that are easy to use and small in size compared to the solid-state lasers used in currently available coherent Raman scattering spectroscopy systems. This ultrafast femtosecond laser is unique in that it produces two highly synchronized laser beams, made possible by graphene or carbon nanotube components.
Laser coherent Raman scattering microscopyWith unique tumor recognition capabilities, AI-based automatic diagnostic technology, our CRS coherent Raman scattering spectroscopy microscope will achieve imaging with higher resolution and 100 to 100,000 times faster than coherent Raman scattering spectroscopy systems and other systems*.
CRS coherent Raman scattering spectroscopy provides chemically informative images that facilitate the identification of tumors and other highly prevalent diseases using AI classifiers. Cutting-edge digital imaging technology based on coherent Raman scattering spectroscopy for molecular signatures to enable AI-based automated diagnosis and personalized medicine.
Laser coherent Raman scattering microscopy can:Generate AI-friendly tissue images without the need for tissue staining, and reuse AI technology to standardize results across sites. It is very convenient when used on a set of slides made in the same laboratory, but different laboratories have differences in tissue processing, staining reagents, and methods, which can lead to changes in the generation of the final digital image.
Advanced Artificial Intelligence Methods - Chemometric Data for Deep Convolutional Neural Network Analysis.
Laser coherent Raman scattering microscopySpatially accurately identify the chemical signatures of tissue components, such as lipids, proteins, and DNA at each point in a sample, and provide another dimension to AI classification, thereby improving diagnostic accuracy. In fact, the resulting data lends itself to advanced AI methods such as deep convolutional neural networks.