Automated phase recognition in the cardiac catheterization laboratory with deep learning

Background

An efficient workflow in the cardiac catheterization laboratory is important to guarantee quality of care and keep healthcare affordable. Automated surgical process monitoring of diagnostic coronary angiograms (CAGs) has the potential to increase workflow efficiency, reduce non-clinical workload, and optimize scheduling. The aim of this study is to automatically classify workflow phases of CAGs using deep learning models trained on a real-world dataset.