$GTCH GBT/Tokenize encourages public participation in its research. Scientists and researchers that are interested to participate in our research are welcome to send us their material for evaluation at kirlian_research@qterm.me
The human body emits various radiations such as infrared, electromagnetic radiation, low level visible light and ultraviolet radiation. This human biofield carries unique information which can potentially be useful for diagnosing and predicting diseases. The various aspects of this biofield can be measured in order to identify organ and/or tissue dysfunctions and therefore potentially detect early stages of possible diseases. Early detection is essential to ensure proper treatment and care.
Kirlian Electrophotography technique is a photographic method to capture the phenomenon of electrical coronal discharges which can be used to produce a human's organ biofield. The technique is based on shooting a high voltage charge through an object that is connected to a photographic plate. The resulting image typically includes a colored aura around the object. When performed on a human organ the aura is its biofield. The research will be focusing on using Kirlian imaging techniques for early disease diagnostics. GBT/Tokenize's Machine Learning system is aimed to analyze the biofield data and possible detection of onset disease. Based on Kirlian images and patterns, GBT/Tokenize's AI seeks to observe, study, analyze and ultimately alert physicians about possible underlying illnesses or symptoms. If developed and commercialized, such a system can be efficient for remote telemedicine diagnostics and medical advice.
"We are excited to start new research for our qTerm device" stated Danny Rittman, GBT's CTO. "This research is about the analysis of Kirlian electrophotography data in order to detect an onset of possible illnesses. Kirlian imaging can produce a bio-energy of human's organs and can perform as a diagnostic tool. Using Kirlian imaging, we believe it is possible to produce a measure of human energy levels and examine changes in the energy distribution throughout the organ(s). We are attempting to determine if we can implement Kirlian imaging for qTerm by means of producing a human's finger aura (i.e. its biofield). Along with qTerm vitals measurement, the goal is to potentially provide a Kirilian image. In turn, using our Machine learning technology, we are seeking to develop an analysis of the biofield and, in turn, provide a diagnostic and potential medical issues classification. We believe this type of system, if fully developed and commercialized, could be very efficient as additional diagnostic system. We commenced our research and we encourage public participation in this research and we will share our findings on our website qterm.me" continued Dr. Rittman.
The human body emits various radiations such as infrared, electromagnetic radiation, low level visible light and ultraviolet radiation. This human biofield carries unique information which can potentially be useful for diagnosing and predicting diseases. The various aspects of this biofield can be measured in order to identify organ and/or tissue dysfunctions and therefore potentially detect early stages of possible diseases. Early detection is essential to ensure proper treatment and care.
Kirlian Electrophotography technique is a photographic method to capture the phenomenon of electrical coronal discharges which can be used to produce a human's organ biofield. The technique is based on shooting a high voltage charge through an object that is connected to a photographic plate. The resulting image typically includes a colored aura around the object. When performed on a human organ the aura is its biofield. The research will be focusing on using Kirlian imaging techniques for early disease diagnostics. GBT/Tokenize's Machine Learning system is aimed to analyze the biofield data and possible detection of onset disease. Based on Kirlian images and patterns, GBT/Tokenize's AI seeks to observe, study, analyze and ultimately alert physicians about possible underlying illnesses or symptoms. If developed and commercialized, such a system can be efficient for remote telemedicine diagnostics and medical advice.
"We are excited to start new research for our qTerm device" stated Danny Rittman, GBT's CTO. "This research is about the analysis of Kirlian electrophotography data in order to detect an onset of possible illnesses. Kirlian imaging can produce a bio-energy of human's organs and can perform as a diagnostic tool. Using Kirlian imaging, we believe it is possible to produce a measure of human energy levels and examine changes in the energy distribution throughout the organ(s). We are attempting to determine if we can implement Kirlian imaging for qTerm by means of producing a human's finger aura (i.e. its biofield). Along with qTerm vitals measurement, the goal is to potentially provide a Kirilian image. In turn, using our Machine learning technology, we are seeking to develop an analysis of the biofield and, in turn, provide a diagnostic and potential medical issues classification. We believe this type of system, if fully developed and commercialized, could be very efficient as additional diagnostic system. We commenced our research and we encourage public participation in this research and we will share our findings on our website qterm.me" continued Dr. Rittman.
