Projects

Recent years have seen an explosion in health data generation from diverse sources. This data has the potential to help advance both research and patient care. However, accessing, integrating, and analyzing it is exceptionally challenging. IDERHA aims to set up an open platform that will facilitate the integration and analysis of diverse types of health data. The platform will link up multiple public and private data sources and implement interoperable tools and services to enable key groups, such as doctors, patients, and researchers, to use the data. To focus their efforts, the IDERHA team will use lung cancer as a use case to design the platform.

A further increase in the performance of supercomputers is expected over the next few years. So-called exascale computers will then be able to deliver more accurate simulation results. Fraunhofer SCAI is developing efficient data analysis methods for the much larger amounts of data generated in this way, which will also provide the engineer with detailed insights into the complex technical interrelationships.

Patients with severe psychiatric disorders such as schizophrenia, bipolar disorder and clinical depression often develop resistance to drug therapy. Even when the early signs of treatment resistance (TR) are detected, patients must undergo lengthy procedures before medical professionals can prescribe adequate pharmaceutical care. The EU-funded Psych-STRATA project intends to analyse extensive clinical, genetic and biological data of psychiatric patients to establish criteria for early detection of TR. Based on the findings, it will propose treatment strategies for patients at risk of TR. The project will further create machine learning models that can predict TR risk and patient response to treatment, thus assisting medical professionals in providing more personalised treatment.

REMEDi4ALL aims to make a significant leap forward in drug repurposing. This promising approach to drug development, consisting of identifying, testing, and validating new therapeutic indications for existing medications, is a developing field but faces numerous barriers and systemic inefficiencies. Still, its potential to significantly bring down times and costs of drug development - it focuses on already approved, discontinued, shelved or investigational therapeutics - makes this novel strategy attractive for rare and neglected conditions, cancer, emerging public health threats such as COVID-19 or new drug combinations. It also translates into more sustainable health systems.

ParKInsonPredict is a joint research project of Technische Universität Dresden and Fraunhofer SCAI aiming to predict disease progression patterns in Parkinson's disease.

ADIS stands for "Early Diagnosis of Alzheimer's Disease by Immune Profiling of Cytotoxic Lymphocytes and Recording of Sleep Disturbances." The project is funded by the EU Joint Programme for Neurodegenerative Diseases Research (JPND). JPND is the largest global research initiative to address neurodegenerative disease challenges. The ADIS project (start: July 2022) will run for three years and has a budget of 1.3 million euros, of which 300,000 euros will go to Fraunhofer SCAI.

The goal of the AIOLOS project (Artificial Intelligence Tools for Outbreak Detection and Response) is to develop a digital platform to allow for early detection of new respiratory pathogens epidemics, monitor their spread, and inform decision-makers on appropriate countermeasures. AIOLOS will provide insights for private and public decision-making in a web-based dashboard, which leverages real-time data from multiple data sources, advanced artificial intelligence (AI), and predictive modeling.

This laboratory (Fraunhofer SCAI, University of Applied Sciences Bonn-Rhein-Sieg, Dr. Reinold Hagen Foundation) aims to create new digital-twin concepts for SMEs.

The European project DIGIPD, funded with around 1.6 million euros and coordinated by the Fraunhofer Institute for Algorithms and Scientific Computing SCAI, is investigating the extent to which digital techniques (sensors, speech recognition, recognition of facial expressions) can be used to make a more precise and individualized diagnosis and prognosis of Parkinson's disease. The project is funded by the European network for personalized medicine, ERA PerMed, in the "Joint Transnational Call 2020". The German share of DIGIPD is funded by the Federal Ministry of Education and Research (BMBF).

The Fraunhofer project HABICHT aims at the development of a highly efficient electric motor for compressors in hydrogen fuel cells.