CAI-X is the framework surrounding the many projects with clinical AI at OUH and SDU.
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.
The purpose of the PEACE project is to improve diagnostics of cervical cancer with artificial intelligence.
This experimental phantom study aims to assess the effect of DLIR in combination with MAR and Dual energy CT.
The DETECT Acute project is 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.
The Danish Skin Cancer Model (DanSkin)
The aim is to provide efficient, fast and cheap diagnostics of patients with suspected skin cancer through the use of a telemedical solution called teledermoscopy
FibroBot - diagnosing fatty liver disease
The goal of this project is to develop a prototype of FibroBot, a robot that will be able to measure the level of scar tissue in the liver with a Fibroscan probe.
The MERIS algorithm, which is based on EHR data, is a validated and evidence-based tool developed to identify patients in high risk of receiving unsuitable medication through medication reviews.
This project will examine the potential of AI-based chatbots in the healthcare sector, starting with patients with endometriosis who are 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 is to aid clinical decision making using artificial intelligence (AI) to find critical information in electronic health records.
Project "Hjertero" (Peace of Heart)
The aim is 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 aims to develop new ways of detecting epileptic seizures by analysing involuntary movements of eyes and face.
The aim is to test and validate the effect of a sentinel algorithm (PATINA) applied on health care data for early recognition of elderly community-dwelling adults at risk of acute hospital admission.
The RELIP project will develop an algorithm to use existing information in the patient recordto ensure early identification of a problematic alcohol consumption among hospitalised patients.
The LiverTrail project will develop and validate a decision aid for use in primary health care to assess the risk of advanced fibrosis in patients with alcoholic- and non-alcoholic fatty liver disease.
The project developed the model MAS-AI which is an HTA-based framework to support the introduction of novel AI technologies into healthcare.
MAGIC - AI for Breast Cancer Diagnostics
The objectives of the MAGIC project are 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, we have developed a machine-learning algorithm 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 aims to reduce the number of unforeseen deteriorations of patients in medical emergency departments.
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 uses machine learning to analyse big data from previous medical history.
AI for rheumatoid arthritis
The project is a branch of the automated ultrasound scanning robot RoPCA which will ensure systematic and uniform method to perform ultrasound scans, thereby ensuring physicians the best possible starting point for the determination of treatment. Such systematic scanning is not possible today due to inaccuracy in human performance.
Machine learning algorithms for prediction of cancer
Full title: Development of machine learning algorithms for prediction of cancer and other serious conditions