Google Health recently launched a new product, a microscope that uses artificial intelligence and augmented reality to help detect certain types of cancer, such as breast and prostate cancer, in real time, and can be retrofitted to many existing optical microscopes. The required component costs are also low and ready to use. Currently, the microscope is still a sample and needs to wait for further testing. But until now, the test that this microscope participated in showed the advantages of this technology for cancer detection. The augmented reality microscope platform is a widely modified optical microscope that supports real-time image analysis. Google released a video on how its technology works. From the video, the microscope analyzes these real-time data using machine learning algorithms and provides a range of visual feedback options, including text, arrows, outlines, heat maps, and animations. The goal of this process is to help medical professionals conduct cancer detection and use deep learning tools to help pathologists make key decisions. Currently, the method used to evaluate cancer tissue is through the use of traditional optical microscopy (composite optical microscopy), in which there are a few failures due to human causes. One of the reasons for this is that it is difficult to identify and detect more than 300 tumors, each with a typical biological characteristic. Augmented reality microscopes developed by Google can be added to many existing optical microscopes. In addition, Google said that the required components are low cost and ready to use. At the same time, the technique does not require a digital version of the entire slide that is analyzed by tissue slicing. When evaluating the technology, Google said: "Modern computing components and deep learning models, such as those based on TensorFlow, will allow a wide range of pre-trained models to run on this platform." Google researchers continue: “Like in traditional analog microscopes, the user looks at the sample through the eyepiece. The machine learning algorithm projects its output into the light path in real time. The digital projection is visually superimposed on the original (analog) of the sample. On the image, to help the observer locate or quantify the features that the observer needs to make a subtle observation."
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