The talented Ph.D. student, Tobias Nissen from Steno Diabetes Center Nordjylland/Aalborg University Hospital, defended his groundbreaking thesis on diabetic retinopathy screening. His research offers vital insights into the use of AI for detecting diabetic retinal changes and the importance of regular screening.
The world is emerging from an unprecedented airborne viral pandemic, akin in scale and lethality to the Spanish Influenza of 1918-1920. Simultaneously, several diseases have reached pandemic proportions globally, with diabetes being a notable example. Recent data indicates that diabetes affects a staggering 537 million people worldwide, with its prevalence increasing in societies adopting Westernized lifestyles.
Access to healthcare and diabetes treatment is generally available in the Western world, but challenges like medication non-compliance and the high cost of prescribed drugs persist. In contrast, in the developing world, limited healthcare resources and expensive medications hinder effective diabetes management, and an optimal treatment approach remains elusive. Regardless of treatment adequacy, diabetes complications eventually manifest, necessitating intervention.
Medication compliance and achieving optimal HbA1c blood levels are closely linked in diabetes management. Prolonged diabetes duration and elevatedHbA1c levels predict the risk of costly complications that can profoundly impact patients and society. Ongoing research and development aim to enhance medication formulations for easier administration and improved glycemic control.
Despite the availability of effective and affordable medications, comprehensive screening for diabetes-related complications remains pivotal. Timely detection is essential to prevent progression to disabling or life-threatening stages. Among these complications, diabetic retinopathy (DR)stands out, with screening programs proving cost-effective in identifying and treating retinal abnormalities promptly.
In summary, while medication compliance and optimal HbA1c levels are pivotal in diabetes management, other factors, such as effective blood pressure control, significantly influence the progression of diabetic retinopathy. Vigilant management of blood pressure and addressing the multifactorial nature of the metabolic syndrome are essential in preventing or mitigating retinal complications. Regular DR screening programs play a pivotal role in early detection and intervention, reducing the risk of visual impairment and disability.
The objectives of this thesis encompassed investigating DR screening attendance in the Danish national screening program within a regional cohort(Paper I), evaluating current software for automated diabetic retinopathy detection in clinical screening settings (Paper II), and providing an overview of ophthalmological biological treatment options for diabetic retinopathy and macular edema, focusing on drug safety and adverse effects (Paper III).
🔍 7% of diabetes patients in Northern Denmark missed retinal screenings over a decade.
🤖 AI shows potential as an assistive tool, but isn't ready to replace eye specialists yet.
👁️ Effective treatments exist to preserve vision in diabetic retinopathy.
Main-supervisor: Professor Henrik Vorum, MD, PhD, R, DrMedSc - Department of ophthalmology, Aalborg University Hospital
Co-supervisor: Professor Peter Vestergaard, MD, PhD, DrMedSc - Departmentof Endocrinology, Aalborg University Hospital, Steno Diabetes Center, Aalborg University
Co-supervisor: Kristian Aasbjerg, MD, PhD, specialist, Himmerland Eye Clinic
Professor: Morten Carstens Moe, University of Oslo, Norway
Physician: Søren Leer Blindbæk PhD, Region of Southern Denmark
Clinical Professor Henrik Nielsen, MD, DMSCI, DTMH, Aalborg University
A cohort follow-up study for diabetic retinopathy screening incidence in the North Denmark Region: https://link.springer.com/article/10.1007/s00592-023-02146-4
Performance of a Support Vector Machine Learning Tool for Diagnosing Diabetic Retinopathy in Clinical Practice: https://vbn.aau.dk/da/publications/performance-of-a-support-vector-machine-learning-tool-for-diagnos
Positive Prediction Value of Retinal Artery Occlusion Diagnoses in the Danish National Patient Registry: A Validation Study: https://vbn.aau.dk/da/publications/positive-prediction-value-of-retinal-artery-occlusion-diagnoses-i
