Currently, the division is working on potential research questions to be addressed in collaboration with the other divisions of KL. In parallel, two projects are ongoing in which the department is conducting research in the area of data-driven studies.
Plenophthalmic camera for mobile 3D retinal diagnostics: A common age-related eye disease is glaucoma. Damage to the retina often leads to blindness. A change in the optic nerve head is an important indication of this clinical picture. However, examination of the optic disc usually involves a subjective assessment by the physician based on a 2D image. Only in ophthalmology centers, in case of abnormalities, a diagnosis and control can be performed with large-scale equipment using 3D data and measurements. By means of the light field principle, also called the plenoptic principle, both 2D and 3D data of the anatomical structures of the retina are to be obtained. This is done for all pixels simultaneously with only one image acquisition. Since this eliminates the need for costly laser and scanning units, the result is a compact, portable plenophthalmoscope that can be used flexibly.This is expected to lead to a significant increase in effectiveness and economy in ophthalmic health care.
Individual medical device for static vascular analysis: Diseases of the cardiovascular system are among the most frequent causes of death worldwide. The changes in the blood vessels associated with these diseases first become apparent at an early stage in the smallest vessels up to 300 µm in diameter, which can be assigned to the microcirculation. Early detection of changes occurring there can be achieved with the aid of static retinal vessel analysis. The diameter of these blood vessels is measured optically at the back of the eye. Several epidemiological studies show a correlation between retinal vessel diameters and hypertension, stroke, diabetes and obesity. Thus, static vascular analysis can play an extremely important role in preventive medicine. Unavoidable individual temporal variability of vessel diameters and other biological artifacts lead to uncertainties in single measurements, which reduce the diagnostic power of static vessel analysis. By using multiple data series in image sequences, uncertainties should be significantly reduced. This approach is supported by the use of artificial intelligence to classify different vessel types.