Many of the e/MTIC researchers are currently working on and implementing analysis techniques and (prediction) algorithms for improved (patient) monitoring and diagnosis and to help optimize individual treatment strategies in collaboration with many medical specialists. Due to the many complexity and heterogeneities in medical data, these approaches and other innovations will be further developed, implemented and automated through projects in e/MTIC. The research focus is on robustness and improved stability of algorithms and methods. In the e/MTIC ICAI lab, AI will be mainly used for the following application areas:

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• Imaging: Strongly enhanced Ultrasound, MRI and CT imaging by embedding task-adaptive AI across the imaging chain.
• Patient monitoring: Strongly enhanced monitoring of vital signs both in clinical and in extramural settings.
• Clinical decision support systems: Use AI to combine various data streams (e.g. EMR, images, spot checks) to produce explainable and patient-specific advice, early warning and alarms.

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Project examples

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Spectralligence: Machine Learning for Spectroscopy Applications.

Within the Spectralligence project, we are unleashingthe power of artificial intelligence for cross-domain spectroscopicapplications. By developing neural networks that can be applied across multipledomains, we're reducing the need for human intervention and taking spectroscopyto new heights.

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Artificial Intelligence in Percutaneous Coronary Interventions

Artificial Intelligence (AI) has the potential tobenefit Percutaneous Coronary Interventions (PCI) procedures. This projectfocuses on enhancing PCI procedures, both in terms of clinical support andoperational efficiency and workflow. We develop methods utilizing varying datamodalities and state-of-the-art 3D reconstruction techniques, directly tailoredto the clinical workflow.

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Early prediction and detection of perinatal complications

This project is a close collaboration between the MMCand the TU/e to develop new methods for the objective detection of fetalmovement and the early and adequate prediction of imminent preterm birth.

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Deep Generative Learning for Uncertainty Estimation in Sleep Staging

Sleep staging is a time-series classification task inwhich the ground truth is uncertain, as it there is inter-rater disagreementbetween human scorers. We have developed an automatic scoring algorithm thatcan reflect this uncertainty by leveraging deep generative networks.

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Advancing Cardiac Care through Interpretable AI (ACACIA)

Early detection of patient deterioration andstreamlining of large dataflows are high-impact areas in the ICU to improvepatient outcomes and workflow. ACACIA aims to develop decision support systemsusing non-invasive advanced monitoring for personalized hemodynamic therapy inthe ICU.

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For more project information click here.