AI Model Forge – Secure Training on Clinical-Grade Data
Service Description
Who Can Benefit: - MedTech & Digital Health Companies: For developing high-performance AI models on sensitive clinical data without cloud dependency or data sovereignty concerns. - Research Groups: For leveraging state-of-the-art computing infrastructure and ML expertise to accelerate model development. - Startups: For accessing enterprise-grade training infrastructure and clinical AI expertise without heavy upfront investment.
Key Features: - End-to-end ML/DL pipeline development: data preprocessing, feature engineering, model architecture design, training, and hyperparameter optimization - Support in developing RAG Systems and Agentic AI: on-premise infrastructure for deployment and limited fine-tuning. - Secure on-premises computing clusters at the highest data security standards – no cloud computing required - Access to the High-Performance Computing (HPC) cluster at FAU Erlangen-Nürnberg for large-scale training workloads - Rigorous testing methodology including cross-validation, holdout testing, and subgroup performance analysis - Clinical domain experts from affiliated clinics ensure models are grounded in medical reality and provide valuable feedback for algorithm improvements
Possible Applications: - Digital Biomarker Development (e.g. Cardiology): Training models to detect arrhythmias, atrial fibrillation, or heart failure decompensation from wearable ECG or PPG signals. - Biomechanical Movement Classification: Building AI models for automated gait pattern recognition, fall risk scoring, or joint load estimation from inertial sensor data collected in motion labs. - Neurological Disorder Detection: Developing classifiers for tremor subtypes, seizure prediction, or cognitive decline indicators based on neurophysiological signals. - Athletic Performance Optimization: Training models that quantify fatigue, predict overtraining, or recommend personalized load adjustments based on biomechanical and physiological sensor fusion. - Injury Risk Prediction & Rehabilitation Monitoring: Building predictive models that identify musculoskeletal injury risk factors or track recovery trajectories from sensor-based movement assessments. - Sports Science Analytics: Developing AI-driven analysis tools for technique evaluation, energy expenditure estimation, or real-time performance feedback during training sessions. - Predictive Patient Monitoring: Training early-warning models for clinical deterioration, therapy non-response, or adverse events from continuous physiological data streams. - Synthetization and Transfer of AI Model to Edge Device: Re-training and quantizing an ML/DL/AI algorithm, so it can be run on an edge device (e.g. wearable), while optimizing performance (inference accuracy, energy consumption per inference, latency etc.)
Who We Are: The Fraunhofer Insitute for Integrated Circuits (Fraunhofer IIS) has established the Center for Sensor Technology and Digital Medicine (CEMDIS) in cooperation with the Universitätsklinikum Erlangen and the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) to enhance modern healthcare through innovative sensor technology and digital solutions. This center focuses on integrating innovative medical technologies such as wearables and robotic systems to support medical diagnostics, patient monitoring and evaluating patient-specific therapies by providing digital health solutions für real-life healthcare. Located at the Universitätsklinikum Erlangen, it offers unique infrastructures for the development, integration, and validation of novel health technologies, providing companies opportunities for technological advancements. For more information, visit the Fraunhofer IIS website.
Provider & Contact
Pricing is available to registered users. SMEs receive significant state-aid reductions (GBER) — or, depending on the call, free services during the funded project. Sign in or register to see the price for your organisation.
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