Künstliche Intelligenz recognizes Herzprobleme – ingen.de

Wissenschaftler has found a new method, a Herz-Kreislauf-Erkrankungen frühzeitig zu recognised. © PantherMedia / SergeyNivens

Scientists and scientists of the Technical University of Graz have found a new method to recognize heart-cycle symptoms early. Aided by Machine Learning and a digital Zwilling analyzes these electrical signals, a representation of the cardiovascular system is performed – nor can the symptom be solved.

The innovative technology focuses on a fast and cost-effective alternative diagnosis with MRI or CT. This technology can support the diagnosis and treatment of the diseases, increasing the quality of the problems, while the legend is translated. For a long time, this Leiden would often first be discovered in fortified stages, when the symptoms are taken away and surgical interventions were unavoidable.

Entlastung fürs Gesundheitsysteem

The research work of Sascha Ranftl and Vahid Badeli, who in the Rahmen of the TU-Graz-Leadprojekts “Mechanics, Modeling and Simulation of Aortic Dissection” under the leadership of Gerhard Holzapfel went through life, now spreads, this situation is improved. There are no new processes that are easy a fast and cost-saving early recognition, but also one of the best processes with MRT or CT that goes to müssen. By using a digital Zwillings you can analyze and analyze the Researchers’ illnesses in more detail.

This method is not suitable for patients and patients who provide care and care, but the common health care system is burdened. The widely disseminated technology would be available for patents and is now being expanded in the TU Graz Spin-off arterioscope on the market.

Künstliche Intelligenz entschlüsselt electric Felder des Körpers

The principles of the new method are based on the knowledge, the cardiovascular development of the mechanism of the heart-circulation systems transmission and the execution of external electric field influences. These diseases are subject to various disease pictures with arteriosclerosis, aortic dissection, aneurysms and heart failures observed. The researchers’ benefits are electrical, optical or bioimpedance signals, which enable an ECG or smartwatches. A self-study of the machine learning model analyzes these signals and identifies potential diseases. View the model of the probability for the fact that a best disease can produce.