Inductive learning of the surgical workflow model through video annotations
Authors
Publication date
23 May 2017
Publisher
'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
partially_open5siSurgical 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
expert’s 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.openNakawala, HIRENKUMAR CHANDRAKANT; DE MOMI, Elena; Pescatori, Erica Laura; Morelli, Anna; Ferrigno, GiancarloNakawala, HIRENKUMAR CHANDRAKANT; DE MOMI, Elena; Pescatori, Erica Laura; Morelli, Anna; Ferrigno, Giancarl