Press Releases

A research consortium led by University Hospital Erlangen and Charité – Universitätsmedizin Berlin has, for the first time, systematically acquired and analyzed MRI scans of the uterus in both healthy and diseased states as part of the RACOON FADEN project. The image data were evaluated using CuraMate, a modular software platform developed by the Fraunhofer Institute for Digital Medicine MEVIS for the annotation, segmentation, and quantification of medical image features.

Regular follow-up monitoring is crucial when it comes to treating cancer successfully. In the SPIRABENE project, Fraunhofer Institute for Digital Medicine MEVIS developed a deep learning-based software program which makes it possible to identify disease- and treatment-related changes in tumors in CT images with even greater speed and accuracy, thus improving the chances of recovery and making day-to-day clinical practice easier.

In the 2025/26 school year, the new upper secondary specialization called “Digital Medicine” will launch in Bremen, developed by Fraunhofer MEVIS and the Oberschule am Waller Ring. On January 23 and 24, interested students can learn more about the new offering in the school auditorium at Lange Reihe 81 in Bremen.

Researchers from the Fraunhofer Institute for Digital Medicine MEVIS will present the latest technologies aimed at optimizing clinical workflows and improving patient care at the annual meeting of the Radiological Society of North America (RSNA 2024), held from December 1 to 5 in Chicago.

Researchers from the Fraunhofer Institute for Digital Medicine MEVIS, the University Medical Center Freiburg, and Stryker Leibinger GmbH & Co. KG have been awarded the Research Prize for Digitalization in Orthopedics and Trauma Surgery 2024 for their publication entitled “Validation of a Finite Element Simulation for Predicting Individual Knee Joint Kinematics.” The award ceremony took place on October 25, 2024, during the German Congress for Orthopedics and Trauma Surgery (DKOU 2024) in Berlin.

Heart valve reconstruction is a highly complex operation that requires a great deal of expertise from surgeons. On the occasion of World Heart Day on September 29, 2024, the Fraunhofer Institute for Digital Medicine MEVIS will present the MINIMAKI assistance system. Based on artificial intelligence methods, it enables interdisciplinary cardiac teams from surgery, cardiology and anesthesia to plan heart valve procedures quickly and optimally using mixed reality.

The use of Artificial Intelligence (AI) systems shows promise in medicine, where they can be used to detect diseases earlier, improve treatments, and ease staff workloads. But their performance depends on how well the AI is trained. A new multi-task approach to training AI makes it possible to train foundation models quicker and more cost-effectively, with less data. Researchers are turning to this approach to compensate for the shortage of data in medical imaging — and ultimately save lives.

Image-guided thermal ablation therapy is a patient-friendly, minimally invasive, and cost-effective tumor treatment method. This involves advancing a special needle directly into a tumor using CT and ultrasound to apply heat and destroy it. Until now, interventional radiologists could only estimate the extent to which the applied procedure destroys tumor cells. Hence, thermal ablation is associated with higher local tumor recurrence rates compared with treatment alternatives, causing a real barrier to wide adoption.

Reliable and feasible early detection of Alzheimer’s disease is the goal of DEBBIE, an international joint project under the EU Joint Programme – Neurodegenerative Disease Research (JPND). It is coordinated by the Fraunhofer Institute for Digital Medicine MEVIS in Bremen, which has received national funding from the Federal Ministry of Education and Research (BMBF) for its work. In this project, MRI images shall uncover the extent to which the blood-brain barrier loses function before the first symptoms of Alzheimer’s disease appear. To speed up development, Fraunhofer MEVIS has now enhanced their own MRI scanner to allow much more effective cooperation with clinical partners.

Complications can always occur during operations, sometimes resulting in death. A new project called KIPeriOP aims to minimize the risk of such complications. The project is based on digitized decision guidelines and self-learning algorithms intended to provide reliable risk assessment based on individual patient data. What is the probability that certain complications will occur, and how might they be avoided? The project is coordinated by Prof. Dr. Anja Hennemuth from the Fraunhofer Institute for Digital Medicine MEVIS and Prof. Dr. Patrick Meybohm from the University Hospital of Würzburg. Clinicians from the Asklepios Medical School GmbH, the University Hospital Frankfurt and the Charité – Universitätsmedizin Berlin are involved. They are working together with experts from the fields of AI, user guidance, ethics, and health economics.