Inductive Learning of the Surgical Workflow Model through Video Annotations

Surgical workflow modeling is becoming increasingly useful to train surgical residents for complex surgical procedures. Rule-based surgical workflows have shown to be useful to create context-aware systems. However, manually constructing production rules is a time-intensive and laborious task. With the expansion of new technologies, large video archive can be created and annotated exploiting and storing the experts knowledge. This paper presents a prototypical study of automatic generation of production rules, in the Horn-clause, using the First Order Inductive Learner (FOIL) algorithm applied to annotated surgical videos of Thoracentesis procedure and of its feasibility to use in context-aware system framework. The algorithm was able to learn 18 rules for surgical workflow model with 0.88 precision, and 0.94 F1 score on the standard video annotation data, representing entities of the surgical workflow, which was used to retrieve contextual information on Thoracentesis workflow for its application to surgical training.