Automated diagnosis of heart valve degradation using novelty detection algorithms and machine learning - Vennemann, Bernhard; Obrist, Dominik; Rösgen, Thomas (PloS one, 2019)
Keywords: Algorithms; Aorta; Aortic valve; Artificial intelligence; Biodegradation; Biology and Life Sciences; Biomedical engineering; Bioprosthesis - adverse effects; Blood; Blood flow; Blood vessels; Cardiovascular disease; Complications; Computer and Information Sciences; Computer simulation; Datasets as Topic; Deep learning; Degradation; Diagnosis, Computer-Assisted - methods; Diagnostic systems; Diastole; Durability; Echocardiography, Doppler; Engineering and Technology; Engineering research; Flow; Fluid dynamics; Heart; Heart Valve Prosthesis - adverse effects; Heart valves; Heart Valves - physiology; Heart Valves - surgery; Hemodynamics; Humans; Identification methods; Inserts; Learning algorithms; Life expectancy; Life span; Machine Learning; Magnetic Resonance Angiography; Medical diagnosis; Medicine and Health Sciences; Methods; Models, Cardiovascular; Neural networks; Novelty; Physical Sciences; Physiology; Prostheses; Prosthesis Failure; Regional Blood Flow - physiology; Regurgitation; Research and Analysis Methods; Sensors; Telemedicine; Transplants & implants; Switzerland