Current projects

cards

Procurement of algorithms for mammography

The project is to develop two comprehensive guidelines for the procurement and rapid validation of AI in clinical settings, the first focusing on a quick validation process for AI models in clinical practice, while the second will cover the entire AI procurement process.
AI-supported treatment for patients with chronic illnesses

The project aims to transform the care of chronic patients by shifting from a reactive to a proactive model by integrating advanced artificial intelligence (AI) into an existing telemedicine programme.
AI-driven automation of cross-sectoral communication

This project aims to develop and test an AI-based Danish language model designed to automate the creation of cross-sectoral communication reports and discharge letters using data from electronic health records.
Intelligent Patient Monitoring

This project aims to test an intelligent patient monitoring solution to improve both the safety and well-being of patients who are vulnerable to falling and being seriously injured.
EUCanScreen

The aim of the EUCanScreen project is to ensure a sustainable implementation of high quality screening programmes for breast, cervical and colorectal cancers in the EU, as well as implementation of the recently recommended screening programmes for lung, prostate and gastric cancers.
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.
ALIVE - analysis of lymph nodes with AI

ALIVE will explore the potential of using AI in the area of pathology by proposing an AI-based system that can screen digital lymph node tissue samples for signs of metastatic cancer and thus help improve the efficiency and accuracy of dignosis.
Implementation of clinical artificial intelligence in healthcare in Denmark

The PhD project by Sabine Morris Delhez aims to clarify whether AI can improve patient care, alleviate staff burdens, and reduce waiting times in the emergency department at Odense University Hospital (OUH).
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.
The Danish Skin Cancer Model (DaiSiCaM)

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
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.
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.
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.
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.
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.

Centre for Clinical Artificial Intelligence

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

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