Joint ProjectImproving the Prognosis of People with Cardiac Insufficiency

9 January 2024

Carl Zeiss Foundation funds joint project in Heidelberg and Mainz

Scientists from Heidelberg and Mainz are collaborating in a joint research project to improve the frequently difficult prognosis of disease progression and hence the treatment options for people with cardiac insufficiency. The goal of the study is to use artificial intelligence methods and robotics to develop individualised therapies for such patients. The project is based jointly at the Medical Faculty Heidelberg of Heidelberg University and at the University Medical Center Mainz, the two lead partners, and it will receive five million euros in funding from the Carl Zeiss Foundation over a period of six years.

Portrait Sandy Engelhardt

Entitled “Multi-dimensionAI: linking scales of information to improve care for patients with heart failure”, the project focuses on a patient group suffering from a frequent form of chronic cardiac insufficiency. This involves a stiffening of the left ventricle although a sufficient volume of blood is ejected. According to the experts, there are no standard forms of treatment able to reverse the changes in the heart muscle and improve the prognosis of the patients. Left untreated, the result might eventually be heart failure, underlines Junior Professor Dr Sandy Engelhardt from the Medical Faculty Heidelberg. She is the project’s spokesperson and, with her working group “Artificial Intelligence in Cardiovascular Medicine”, does research in the clinical Departments of Cardiology, Angiology and Pneumology and Cardiac Surgery at Heidelberg University Hospital. Co-spokesperson of the interdisciplinary project team is Prof. Dr Philipp Wild, head of Preventive Cardiology and Medical Prevention at the Center for Cardiology of the University Medical Center Mainz.


In order to improve the care and treatment of cardiac insufficiency patients, the researchers involved want to train artificial intelligence (AI) multimodally with the health data of several thousand patients. Since many factors influence disease progression and response to treatment, they are looking for recurring patterns and possible correlations to identify patient groups with a disease progression as uniform as possible. For this purpose, patient data from different sources is to be pooled and used as training data. “We hope that the AI support will enable us to select therapies and evaluate their benefits in a much more targeted manner in future,” says Prof. Engelhardt.

As a practical example of an AI-based treatment recommendation, an exercise therapy study is to be conducted, developed and supervised by the sports medicine departments of Heidelberg University Hospital and Mainz University. The patients, who soon get out of breath due to their cardiac insufficiency, will be helped to exercise by wearing a personalised exosuit. It was designed by a team around Prof. Dr Lorenzo Masia, head of the Biorobotics and Medical Technology group at the Institute of Computer Engineering at Heidelberg University. Like an exterior skeleton, the wearable robotic elements are placed, for example, on arms, legs and torso, providing flexibly adjustable support and so increasing mobility. Improvements in quality of life will be repeatedly assessed during, and after, the exercise programme, along with effects ranging from the molecular to the macroscopic level, which will then flow into the AI system.

The Multi-dimensionAI project team will begin work in July 2024. Project partners are working groups at both university hospitals and both Heidelberg and Mainz universities, from the areas of cardiology, bioinformatics, sports medicine, epidemiology, medical technology, pathology and law. The project will be mainly implemented through cooperation arrangements arising from the German Centre for Cardiovascular Research. The Carl Zeiss Foundation is funding the research work as part of its programme “CZS Breakthroughs: AI in Health”. With the call, the foundation aims to support universities in implementing innovative, scientifically promising basic research in the field of AI-assisted health research.