CONVOCALS

We are happy to announce that we received a University Utrecht Special Interest Group seeding grant of €5,000 for our proposal entitled CONVOCALS: a CONVOlutional neural network to predict symptoms and major secondary CArdiovascuLar events based on high-resolution scanned histological Slides in collaboration with Tim G.M. van den Kerkhof and Dr. Ayoub Bagheri.

Executive summary

Despite tremendous medical progress, cardiovascular diseases (CVD) are still topping global charts of morbidity and mortality. Atherosclerosis is the major underlying cause of CVD and results in atherosclerotic plaque formation. The extent and type of atherosclerosis is manually assessed through histological analysis, and histological characteristics are linked to major acute cardiovascular events (MACE).

However, conventional means of assessing plaque characteristics suffer major limitations directly impacting their predictive power. CONVOCALS will use a machine learning technique, convolutional neural network (CNN), to develop an internal representation of the 2-dimensional plaque images, allowing the model to learn position and scale in variant structures in the data. A CNN is a subset of deep learning which has established as a powerful class of models for image recognition problems such as analysis of x-ray medical images.

The aim of CONVOCALS is to build a CNN to process high-resolution images from scanned histological slides of plaques in order to predict MACE.