Dec 23 2020
GBT Technologies Inc. ("GBT”, or the “Company”), is developing an AI based graphic analytics system, targeted for medical imaging. As part of the company's plan to move forward towards the medical analytics field, we started research and development activities to seek and improve detection of diseases and symptoms using computer vision analysis.
The efforts are invested into AI applications to detect and interpret medical images, like MRI, X-Ray, CT, PET and Ultrasound, with the goal of helping guiding physicians in capturing possible issues within scans of human organs.
The project's internal name is "Imaging Predictive Analytics" (IPA) and it targets to analyze, alert and predict potential issues based on graphical data. We are focusing on getting the most value of our AI analytics power for images and scans of high resolution.
The project is targeting the development of analysis technologies that can be used for biomedical purposes. For example, the aim of the IPA system is to be an added system to assist physicians with real time ultrasound scan to alert of potential suspicious findings that may be too small or unclear for the human eye.
Another potential implementation would be to analyze MRI, CT and PET images to search and alert physicians for abnormalities.
Medical images include high resolution data and the IPA system aims to automate the detection of abnormalities in commonly-ordered imaging in order to lead to quicker decision-making with the goal of reducing diagnostic errors in a wide variety of medical fields.
One of these potential implementations is the cardiovascular field. Analyzing images to measure various structures of the heart can reveal risks for onset cardiovascular diseases or identify other abnormalities that may need to be addressed through surgery or medications.
Another field is the respiratory area. This domain requires fast medical response in order to assist with respiratory therapy for patients with cardiopulmonary (heart/lung) illnesses and breathing difficulties, for example pneumonia and pneumothorax (Collapsed lung).
It is our goal to use the IPA artificial intelligence based algorithms to provide for quick imaging analysis pointing physicians to provide the necessary treatment. The system is designed to incorporate deep learning methods and concepts that were initially developed within the company's Avant!-AI and Hipocrates system.
"According to our R&D plans 2021, we are moving forward to develop more advanced AI systems for the medical domain. Today's medical imaging consists of ultra high resolution scans that include numerous details.
We started the development of our Imaging Predicting Analytics system, internal code name IPA, which we believe will be especially useful if fully developed when quick response is necessary. For example, in case of cardiovascular or respiratory conditions, a rapid imaging analytics may save lives by quickly finding anomalies that may be urgently addressed.
The system is planned to have 2D and 3D simulation including rotations, cross sections and mapping. We already developed graphic analytics techniques within our Avant! AI system and now plan to further develop advanced deep learning imaging algorithms to analyze modern images of CT, MRI, X-RAY, PET and Ultrasound. We believe we will be able to target the system to assess images for evidence of abnormalities and alert providers to the potential diagnoses, with the goal of pointing subjects-of-interest to physicians, enabling faster treatment.
The system is planned to use AI algorithms to learn the image parameters and train a neural network for pixels recognition and analytics. We intend for the the system to provide risk scores for areas of concern. We target to improve diagnostic's accuracy and potentially alert for risky factors, eliminating unnecessary tests and benign biopsies. We believe that medical imaging data is one of the richest sources of information, and one of the most complex ones.
Artificial intelligence science has already proven to be an efficient assistant for radiologists and health professionals for the past decade, pursuing faster, more accurate, and comprehensive patient care. But our challenges are growing exponentially and so are our needs.
2D data is rapidly moving to 3D form, enabling better information representation in many medical fields. Today's imaging analytics complexity requires advanced computerized disciplines and a quantum leap approach in order to handle vast data, especially when real-time factor has become crucial. Our goal is to develop native graphic-rich disciplines to augment image analytics algorithms and flows, enabling better, and more accurate medicine and diagnostics."
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