$GTCH from their news: "We are going to look deepe

$GTCH from their news: "We are going to look deeper into Kirilian electrophotography science, trying to identify the possibility of detecting early disease symptoms. Kirlian images of a living tissue during various intervals may exhibit some similarities. If we could graphically analyze these images using machine learning technology, reaching some consistent conclusions, then we may find a way to find possible early health issue identification" stated Danny Rittman, GBT’s CTO. "We intend to analyze and measure Kirilian images to find unique patterns that may be associated with early symptoms. We will look for full and partial similarities, repetitions, or atypical auras patterns. We will be using AI computing power to detect dynamic images changes as each image will be digitized using high resolution scanning. Here the power of huge data analysis will be extremely beneficial. We plan to implement interactive algorithms to analyze on-the-fly out-of-boundaries patterns to get a comparative representation between the images. The challenging part will be to associate the human body's various radiations graphical representation, with health related issues. For this purpose, we plan to use our AI, vast data analysis capabilities, trying to assemble a reliable algorithm to create an associative table that will relate patterns to a possible onset disease. Upon reaching conclusions we will evaluate the potential implementation of this technology within our qTerm device to further advice users about their health” continued Dr. Rittman.