e/MTIC AI-Lab

E/MTIC AI-LAB

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 鈥淔ast track to clinical innovation鈥�, 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 黑料福利网 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 ->

e/MTIC Research with focus on AI
	Ben Luijten
	Chenyan Huang
	Dandan Zhang
	Dennis van de Sande
	Frederique de Raat
	Hans van Gorp
	Ivar de Vries
	Jaap van der Aar
	Julian Merkofer
	Kirsten Maas
	Lotte Ewals
	Mark Ramaekers
	Roy van Mierlo
	Terese Hellstr枚m
	Tom Bakkes
	Tristan Stevens
	Victoria Bruno
	Vincent van de Schaft
	Wessel Nieuwenhuy
	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 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.