Medical AI

Colorectal Cancer

Aim: To improve diagnostic accuracy and acceptability of colorectal cancer screening by developing and using a new generation of wireless capsule endoscopy featuring onboard intelligence (AI)

Collaborators: Department of Surgery at Svendborg hospital, Department of Medical Gastroenterology and Biomedical Lab at Odense University Hospital (OUH), and Department of Gastroenterology at Vejle-Lillebaelt hospital

Prostate Cancer

Aim: To identify indicators in the past medical history of prostate cancer patients, that can better predict prognosis and can illuminate the reason for the emergence of metastases and subsequent death.

Collaborators: Department of Clinical Biochemistry and Pharmacology (OUH), Department of Urology (OUH)

Breast Cancer

Aim: To provide reproducible, observer-independent measures of breast cancer disease and its extent in the body, by applying artificial intelligence methods to medical imaging such as FDG-PET/CT.

Collaborators: Department of Clinical Physiology and Nuclear Medicine (OUH)


Early PET/CT In Suspected Serious Disease

Aim: To evaluate the use of PET/CT in the diagnosis of patients in whom occult cancer (mistanke om alvorlig sygdom, MAS) is suspected based on vague, non-specific symptoms from several organ systems (e.g. fatigue and large unintended weight loss).

Collaborators
: Department of Nuclear medicine (OUH)

Diabetes

Aim: To generate a high-performance AI predictive model able to detect the very first signs of diabetic complications (e.g. peripheral and autonomic neuropathy), based on facial videos captured at home with patient’s smartphone.

Collaborators: Medical Endocrinology (OUH) and Steno Diabetes Centre Odense.


Liver Diseases

Aim: To develop and validate a automatic home-based technology based on a novel clinical decision model for: 1) early detection and risk stratification of liver diseases among asymptomatic at-risk patients, and 2) monitoring disease progression and predicting the risk of imminent readmission to hospital, in patients with existing chronic liver disease.

Collaborators: Elite Research Centre (FLASH) and Department of Hepatology (OUH)


The Great Obstetrical Syndromes

Aim: To improve the understanding of the aetiology, prediction, prevention, and interventions to prevent the mortality and morbidity of both mother and child, associated with the "Great Obstetrical Syndromes": preterm birth, pre-eclampsia, intrauterine growth restriction, fetal death and gestational diabetes mellitus.

Collaborators: Department of Obstetrics and Gynaecology (OUH), Department of Public Health, University of Copenhagen (KU)


Cardiac Imaging

Aim: To analyze the impact of coronary artery calcification score and 15 prognostic biomarkers on cardiovascular events in a 10-years follow-up analysis in a cardiovascular healthy population, to improve prediction of cardiovascular events in these asymptomatic subjects.

Collaborators: Department of Cardiology (OUH)


Electrophysiology

Aim: To identify specific subtle patterns on the normal sinus rhythm electrocardiograph (ECG) that can be used as predictors of the presence of atrial fibrillation, increasing the risk of strokes, heart failure and other heart-related complications.

Collaborators: Department of Cardiology (OUH)


Epilepsy

Aim: To develop a feasible and non-invasive long-term solution for home-monitoring of patients with generalized epilepsy paroxystic discharges, and to predict their response to different pharmaceutical treatments.

Collaborators: Department of Neurology (OUH), Danish Epilepsy Society


Dementia And Hearing Loss

Aim: To investigate the relationship between hearing loss and risk for dementia and to provide a better understanding of the mechanisms underlying this relationship.

Collaborators: Department of Environment and Cancer (The Danish Cancer Society), Department of ORL Head and Neck surgery and Audiology; and Department of Neurology (OUH), Department of Public Health (KU), Institute of Clinical Research (SDU) and William Demant Fonden


Inflammatory Bowel Disease

Aim: To provide a novel imaging solution, consisting in AI-wireless capsule endoscopy, for early diagnosis, reassessment of disease extent and activity, and management, of inflammatory bowel disease (Crohn’s disease and ulcerative colitis)

Collaborators: Department of Surgery at Svendborg hospital, Department of Medical Gastroenterology and Biomedical Lab at Odense University Hospital (OUH), and Department of Gastroenterology at Vejle-Lillebaelt hospital