Active learning for extracting surgomic features in robot-assisted minimally invasive esophagectomy: a prospective annotation study

Abstract

We presented ten surgomic features relevant for bleeding events in esophageal surgery automatically extracted from surgical video using ML. AL showed the potential to reduce annotation effort while keeping ML performance high for selected features. The source code and the trained models are published open source.