ICAI Lab

E/MTIC AI-Lab

The e/MTIC AI-Lab is aimed at improving personalized treatment by having AI work in close collaboration with the clinical staff and MedTech industries since AI is better able to make reliable decisions in a wide range of healthcare situations.

More information ICAI labs

Research Topics

Many e/MTIC researchers are working on analysis techniques and algorithms for improved (patient) monitoring and diagnosis to help optimize individual treatment. Due to the many complexity and heterogeneities in medical data, these approaches are further developed, implemented and automated through different projects in e/MTIC. The research focus is on robustness and improved stability of algorithms and methods.

In the e/MTIC AI-Lab, AI will be mainly used for the following application areas:

- Imaging: strongly enhanced Ultrasound, MRI and CT imaging by embedding task-adaptive AI across the imaging chain
- Patient monitoring: 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.

e/MTIC Research projects

Given that the main purpose of e/MTIC is to provide a “Fast track to clinical innovation”, Artificial Intelligence is an extremely important instrument to support this goal. Both in clinical decision support in general, and in-patient monitoring and image analysis in particular, novel AI techniques provide powerful approaches to identify patient deterioration at an earlier stage, diagnose conditions more accurately, better guide treatment, and improve secondary prevention.

ICAI is a Dutch network aimed at technology and talent development between knowledge institutes, industry, and government in the area of artificial intelligence.

e/MTIC AI Health projects

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.

Neuroscience

Spectralligence: Machine Learning for Spectroscopy Applications

Within the Spectralligence project, we are unleashing the power of artificial intelligence for cross-domain spectroscopic applications. By developing neural networks that can be applied across multiple domains, we're reducing the need for human intervention and taking spectroscopy to new heights.

Perinatal

Early prediction and detection of perinatal complications

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

CardioVascular

Artificial Intelligence in Percutaneous Coronary Interventions

Artificial Intelligence (AI) has the potential to benefit Percutaneous Coronary Interventions (PCI) procedures. This project focuses on enhancing PCI procedures, both in terms of clinical support and operational efficiency and workflow. We develop methods utilizing varying data modalities and state-of-the-art 3D reconstruction techniques, directly tailored to the clinical workflow.

Sleep

Deep Generative Learning for Uncertainty Estimation in Sleep Staging

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

CardioVascular

Advancing Cardiac Care through Interpretable AI (ACACIA)

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

From Bench to Bedside ->

COMBAT-VT ->

e/MTIC Research with focus on AI
SPICE: Self-Driving Ultrasound for Multiparametric Cardiac Tissue Characterization	Ben Luijten
PICASSO PerInatal, CArdiovascular and Sleep medtech SOlutions	Chenyan Huang
Combining cardiorespiratory signals and video-based actigraphy for classifying preterm infant sleep states	Dandan Zhang
Spectralligence	Dennis van de Sande
ACACIA: Advancing Cancer care And Cardiac care through Interpretable AI	Frederique de Raat
PICASSO SPS: Perlnatal, CArdiovascular and Sleep medtech Solutions	Hans van Gorp
TopZorg: Fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease	Ivar de Vries
PICASSO SPS: Perlnatal, CArdiovascular and Sleep medtech Solutions	Jaap van der Aar
Spectralligence: Spectral Analysis in life sciences and materials sciences through Artificial Intelligence	Julian Merkofer
MEDUSA: Medical Data Utilization Solutions Accelerator	Kirsten Maas
ACACIA: Advancing Cancer care And Cardiac care through Interpretable AI	Lotte Ewals
ACACIA: Advancing Cancer care And Cardiac care through Interpretable AI	Mark Ramaekers
ACACIA: Advancing Cancer care And Cardiac care through Interpretable AI	Roy van Mierlo
ACACIA: Advancing Cancer care And Cardiac care through Interpretable AI	Terese Hellström
CaRe-ON: CardiOvascular Research Opting for New Applications: continuous cardiac risk and lifestyle profiling	Tom Bakkes
PISANO: Perioperative Innovations, Sleep Apnea and Newborn Opportunities	Tristan Stevens
MEDEIA: Advancing cancer care with human-centered AI	Victoria Bruno
MEDEIA: MEDical Engineering, Innovations and Applications	Vincent van de Schaft
MEDEIA: Development, implementation and evaluation of innovations in a full healthcare chain	Wessel Nieuwenhuy
PICASSO: Advanced NICU sensing	Zheng Peng

e/MTIC Health Data Portal

The partners of e/MTIC joined hands to develop the Health Data Portal (HDP) to facilitate and enable joint research projects. The e/MTIC HDP is a scalable collaboration platform that builds on existing initiatives to provide an infrastructure where medical data from multiple institutions can be shared safely and researchers can collaborate on this data.

The construction of the e/MTIC HDP platform allows, for the first time, medical data from different types of healthcare institutions to be shared securely and anonymously, such as between hospitals, universities and industry. The HDP has an important role in the national network of the Health-RI project, financed by the National Growth Fund.

e/MTIC Health Data Portal

2022 ICAI Deep-Dive Data Series I: Medical data usage

What if we would remove many of the roadblocks, researchers and clinicians face while exchanging Medical Data from multiple sources? To drive value-based health care, we need to analyze massive amounts of data. Finding, accessing, and processing medical data while respecting privacy and security regulations is a complex task.

During the hybrid event ICAI e/MTIC of 12 May 2022, we addressed how data sharing and AI play an essential role within e/MTIC and share with you two research cases in which data and AI play a vital role.