slides

Our focus lies on a user-friendly display of ASL evaluation results to support efficient study workflows and reliable results. We provide interactive reports for quick overview of a participant as well as pdf reports for structured archiving.

for instant display of evaluation results – anytime, anywhere.

An aortic aneurysm or even an aortic dissection is often discovered as an incidental finding on abdominal CT scans. In case of doubt, however, the patient must be treated as quickly as possible. We are developing an automatic, AI-supported evaluation of all abdominal CT images in the cloud, which alerts the physician on duty directly in the event of a critical finding, so that the patient can be prioritised.

The likelihood of breast cancer recurrence is still difficult to predict. Together with nine partners in the EU, we are the first to combine radiological, histo-pathological and clinical data of patients in one model to predict relapse of distant metastases.

Structural changes caused by inflammatory processes offer signs of the future course of multiple sclerosis and also provide new information for other disease patterns. The tried and tested VGM algorithm visualises these changes and creates a “map” of the brain. The evaluation determines tissue changes with 100 million degrees of freedom.

Image: KI4KMU – Example of an AI-supported VGM evaluation, which was developed as part of a project funded by the Baden-Württemberg Ministry of Economics together with the University Medical Center Mannheim and MedicalSyn GmbH in Stuttgart.

We are working on identifying early-stage Alzheimer’s disease on MR images. Artificial intelligence helps us to recognise and assess specific patterns in the perfusion of the brain.

In the past this has been seen as a time-consuming process previously only used in academic settings. In the KI4MS project, we bring this valuable technology to the radiological practice. To do this, we use a neural network that has been specially trained for the structural changes. This artificial intelligence needs less than ten minutes to calculate a 3D map of the tissue changes.

Image: KI4KMU – Example of an AI-supported VGM evaluation, which was developed as part of a project funded by the Baden-Württemberg Ministry of Economics together with the University Medical Center Mannheim and MedicalSyn GmbH in Stuttgart.

An important imaging biomarker for MS is smouldering lesions. Their AI-aided detection on MR images also makes disease activity visible that cannot be seen with the naked eye.

Image: KI4KMU – Example of an AI-supported VGM evaluation, which was developed as part of a project funded by the Baden-Württemberg Ministry of Economics together with the University Medical Center Mannheim and MedicalSyn GmbH in Stuttgart.

We develop and refine structural analysis methods to support the diagnosis and follow-up of multiple sclerosis.

… we establish a standardised workflow that facilitates the evaluation and development of this first ASL-based Alzheimer’s imaging biomarker.

Image source: „ExploreASL: An image processing pipeline for multi-center ASL perfusion MRI studies” by Henk J.M.M. Mutsaerts et al. in NeuroImage (2020)