Previous projects

cards

DeLeMA CT

This experimental phantom study aimed to assess the effect of DLIR in combination with MAR and Dual energy CT.
AI for prediction of prostate cancer progression

The project used machine learning to analyse big data from previous medical history.
MAGIC - AI for Breast Cancer Diagnostics

The objectives of the MAGIC project were to validate and test an AI system in an existing radiology workflow and analyse the impact on cancer detection and recall rates as well as workload.
DETECT Acute

The DETECT Acute project was a clinical two-centre study aiming to assess the diagnostic performance of low-dose computed tomography (CT) with a novel Deep Learning Image Reconstruction algorithm (DLIR) in patients with acute abdominal conditions.
VR training for heart patients using an AI solution

The project sought to develop an AI algorithm to be built into SYNCSENSE's VR solution that could identify training equipment and adapt to the physical capabilities of the individual heart patient, thus making the VR training intelligent and efficient.
Chatbots

This project examined the potential of AI-based chatbots in the healthcare sector, starting with patients with endometriosis who were in contact with the Department of Gynaecology and Obstetrics at Odense University Hospital.
FibroBot - diagnosing fatty liver disease

The goal of this project was to develop a prototype of FibroBot, a robot that would be able to measure the level of scar tissue in the liver with a Fibroscan probe.
PATINA Algorithm

The aim was to test and validate the effect of a sentinel algorithm (PATINA) applied on healthcare data for early recognition of elderly community-dwelling adults at risk of acute hospital admission.
AutoDok

In the AutoDok project, the Patient Deterioration Warning System was extended with functionality for handling clinical documentation for patients who were extensively monitored, and patients who lacked monitoring during periods of their admission.
Non-invasive detection, monitoring and prediction of epileptic seizures

This project aimed to develop new ways of detecting epileptic seizures by analysing involuntary movements of eyes and face.
Screening of sentinel lymph nodes for metastasis

The project aimed to develop and test an AI application to detect metastasis in sentinel lymph node in breast cancer to support decision making.
LiverTrail

The LiverTrail project aimed to develop and validate a decision aid for use in primary healthcare to assess the risk of advanced fibrosis in patients with alcoholic and non-alcoholic fatty liver disease.
MERIS

The MERIS algorithm, which was based on EHR data, was a validated and evidence-based tool developed to identify patients in high risk of receiving unsuitable medication through medication reviews.
Detection and presentation of bleeding events in the electronic health record

The aim was to aid clinical decision making using artificial intelligence (AI) to find critical information in electronic health records.
MAS-AI

The project developed the model MAS-AI which is an HTA-based framework to support the introduction of novel AI technologies into healthcare.
Identifying patients at risk (PDWS)

The project aimed to reduce the number of unforeseen deteriorations of patients in medical emergency departments by looking at how data from the information systems that hospitals already use could be utilised in novel ways through machine learning based models.
RELIP

The RELIP project aimed to develop an algorithm to use existing information in the patient recordto ensure early identification of a problematic alcohol consumption among hospitalised patients.
AI and SWE in liver disease

In this project, a machine-learning algorithm was developed with the ability to improve the diagnostic accuracy of SWE pictures in patients with chronic fatty liver disease.
Machine learning algorithms for prediction of cancer

This project focused on developing computerised tools to identify significant displacements in patterns of health data, so general practitioners could be alerted that a specific patient must be examined for possible early cancer or serious disease.
AI for rheumatoid arthritis

The project aimed to develop a robot system for early detection and monitoring of rheumatoid arthritis.
Early detection and monitoring of diabetes and complications

The goal is to explore whether cutaneous variables that are not apparent to the medical professional naked eye (i.e. invisible facial redness and the variation of redness due to heart pulsation) could be markers of diabetes and in particular diabetic neuropathy.

Centre for Clinical Artificial Intelligence

Odense University Hospital
Kløvervænget 8C, Entrance 101, 4th floor
5000 Odense C
Denmark

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