Collaboration 

TUH wants to remove barriers and strengthen collaboration across healthcare and technology. We build bridges between clinical practice and new solutions – for the benefit of both patients and staff.

Read more about Matchmaking or see a selection of cases below that provide insight into various collaborations within the TUH framework.

Researchers from DTU and Amager Hvidovre Hospital have been given access to the Gefion supercomputer, where they are loading data from 3,000 uterine images in 3D to use the computer to come up with new forms of diagnosis and treatment for, among other things, the disease endometriosis, where the uterine lining continues to grow into the uterus, causing pain and potentially leading to involuntary childlessness.
CASE:

AI supercomputer boosts research into women's health

Researchers from DTU and Amager and Hvidovre Hospital will have access to the Gefion supercomputer in a series of projects on women's diseases in a new collaboration with the Danish Centre for AI Innovation, DCAI.

 “We have one of the world's largest and most unique data sets on women's health—and now, with Gefion's computing power, we are ready to exploit its full potential,” says David Westergaard, associate professor at DTU and head of Data Science at the Department of Gynecology and Obstetrics at Amager and Hvidovre Hospital, which is part of TUH.

Read more

 

AI models for analysing ultrasound images from the spinout company Prenaital can detect up to 35 % more high-risk pregnancies. From left: CEO and founder of Prenaital, Tanja Danner; DTU professor and co-founder, Aasa Feragen; and chief physician at Rigshospitalet and co-founder, Martin G. Tolsgaard.
CASE:

Artificial intelligence identifies significantly more high-risk pregnancies

AI models for analysing ultrasound images can detect up to 35 % more high-risk pregnancies, thereby helping to prevent preterm birth or birth complications.

The use of artificial intelligence to analyse ultrasound scans can detect up to 35 % more high-risk pregnancies than scans performed by healthcare professionals without AI decision support. This is shown by results from a newly established spinout company, Prenaital, from DTU and the University of Copenhagen, founded by engineers, computer scientists, and doctors following several years of collaboration, most recently within the framework of the Technical University Hospital of Greater Copenhagen (TUH). Read more
CASE:

Algorithms support early diagnosis of Parkinson’s

A research team at DTU is collaborating with clinicians at Rigshospitalet to develop a digital tool capable of identifying and analysing Parkinson’s symptoms based on real patient data.

The technology can identify signs of Parkinson's earlier than standard methods currently used in the clinic. The research group has develop a new protocol for data collection of the patient's movements through non-invasive monitoring, which together with machine learning algorithms and muscoskeletal twins of the research subjects will be used for identifying early motor symptoms of Parkinson's. Read more

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