How long should a patient stay hospitalized after surgery, what conditions affect the recovery and how can we care for our patients in an optimal way?
With the help of Artificial Intelligence and a specially programmed algorithm, our assistant physician, Dr. Bruna Gomes, addresses the questions in her current research project on patients with heart valve diseases after a Transfemoral Catheter Assisted Aortic Valve Replacement (TAVI).
Computer programs with artificial intelligence (AI) are initially “fed” with numerous patient data sets and search this mass of digital data for predictive factors in order to predict certain events (e.g. complications or mortality).
While traditional statistical methods and classical risk scores take into account only about 10 characteristics of a patient in this probability calculation, the algorithm developed by Dr. Gomes – through Machine Learning – captures about 80 characteristics, learns continuously and gives a much more differentiated picture of the patient. “You can compare it to a photo that traditionally has 500 pixels, but our algorithm now allows us to display this image with 4000 pixels, giving us a much higher resolution image,” Dr. Bruna Gomes explains.
With this highly-differentiated image of the patient, Gomes and her colleagues in Prof. Benjamin Meder’s research team hope to improve the assessment of each individual patient in order to enable the best possible prediction of the course of our patients’ disease according to TAVI.
Currently, the algorithm is based on the already evaluated findings of a patient, but the goal is to evaluate imaging diagnostics (CT, MRI, Echocardiography) also by AI, to integrate them into the algorithm and to combine all available information.