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AI projects

CAI-X is the framework surrounding the many projects with clinical AI at OUH and SDU.

All projects

PREMIO COLLAB

The PREMIO COLLAB project aims to prolong overall survival and to improve the quality of life for metastatic breast cancer patients by providing refined guidance for the management of treatment response monitoring.

AI-RAPTOR

The overall aim of AI-RAPTOR is to develop, train and validate an AI algorithm that can detect potentially malignant pulmonary findings on low-dose CT scans and thus aid clinicians in the diagnostic process. 

Venous thromboembolism risk assessment in hospitalised patients

The project aims to develop AI algorithms based on Natural Language Processing to identify previous incidents of venous thromboembolism in patients’ electronic health records for thrombosis risk assessment.

PEACE

The purpose of the PEACE project is to improve diagnostics of cervical cancer with artificial intelligence. The project will develop, test and implement an AI solution to guide the colposcopist in real time to find the best spots for biopsies.

DeLeMA CT

This experimental phantom study aimed to assess the effect of DLIR in combination with MAR and Dual energy CT.

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.

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.

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.

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.

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.

Project "Hjertero" (Peace of Heart)

The aim was to develop a data-driven prediction model capable of identifying individuals with Ischaemic Heart Disease at risk of developing anxiety or depression.

AICE - AI-supported picture analysis in large bowel camera capsule endoscopy

The goal of the AICE project is to create a complete and validated AI-assisted pathway that improves CCE diagnostics making the technology clinically viable for the good of patients, health care systems and society.

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.

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.

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.

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.

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.

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.

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.

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.

FAST-MRI

The FAST-MRI project aims to investigate the accuracy, workload changes and time-to-diagnosis when implementing an AI system for stroke MRI diagnostics in a Danish setting.

Screening of diabetic retinopathy

The study aims to develop a software system that utilises deep-learning by fully convolutional networks in order to perform automatic grading of retinal images to detect diabetic retinopathy.

Automatic audiogram classification

The project aims to use AI to recognise audiogram patterns. The aim is to recognise at least 8 to 10 of the most common audiogram subtypes.

AI for prediction of prostate cancer progression

The project used machine learning to analyse big data from previous medical history.

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.

AI for rheumatoid arthritis

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

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.

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.
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