Visualization of an attention map (right hand in color) of a biomarker on an OCT Bscan (empty Bscan on the left). Attention maps or heat maps show the areas of greatest importance for the classification of different biomarkers in a BScan. Image: ARTORG Center, University of Bern
A fast and reliable machine learning tool, developed by the ARTORG Center, University of Bern and the Department of Ophthalmology, Inselspital brings Artificial Intelligence (AI) closer to clinical use in Ophthalmology. The novel method published in Nature Scientific Reports on September 19, 2019 presents a tool that reliably extracts meaning from extensive image data. Based on a convolutional neural network (CNN) the tool is able to provide results within seconds, thus supporting the doctor with comprehensive image analysis during a consultation with the patient. Modern medical imaging devices allow ophthalmologists to monitor chronic eye conditions in detail. Ophthalmologists mostly choose Optical Coherence Tomography (OCT), an imaging tool that generates 3D images of the eye at extremely high resolution. But without AI support the large amount of images and information exceeds the capacity of an individual expert. The challenge of this study was, to provide AI-tools, capable of analyzing a large amount of data at very high speed to facilitate the use of all available information from image analysis during patient consultations. The research team from Artificial Intelligence in Medical Imaging (AIMI) laboratory at the ARTORG Center for Biomedical Engineering Research, University of Bern, and the Department of Ophthalmology at Inselspital, Bern University Hospital now presents a machine learning method capable of identifying a wide range of biomarkers from OCT-scans of the retina virtually providing clinically relevant data support instantaneously. Artificial Intelligence spots biomarkers for each disease type
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