title: Automatic phase prediction from low-level surgical activities.
abstract: OBJECTIVE
  Analyzing surgical activities has received a growing interest in recent years. Several methods have been proposed to identify surgical activities and surgical phases from data acquired in operating rooms. These context-aware systems have multiple applications including: supporting the surgical team during the intervention, improving the automatic monitoring, designing new teaching paradigms.
  
  
  METHODS
  In this paper, we use low-level recordings of the activities that are performed by a surgeon to automatically predict the current (high-level) phase of the surgery. We augment a decision tree algorithm with the ability to consider the local context of the surgical activities and a hierarchical clustering algorithm.
  
  
  RESULTS
  Experiments were performed on 22 surgeries of lumbar disk herniation. We obtained an overall precision of 0.843 in detecting phases of 51,489 single activities. We also assess the robustness of the method with regard to noise.
  
  
  CONCLUSIONS
  We show that using the local context allows us to improve the results compared with methods only considering single activity. Experiments show that the use of the local context makes our method very robust to noise and that clustering the input data first improves the predictions.
authors.affiliation: MIPS, University of Haute-Alsace, Mulhouse, France, germain.forestier@uha.fr.
authors.forename: Germain
authors.initials: G
authors.lastname: Forestier
authors.forename: Laurent
authors.initials: L
authors.lastname: Riffaud
authors.forename: Pierre
authors.initials: P
authors.lastname: Jannin
mesh_terms: D000465:Algorithms; D000066491:Clinical Decision-Making; D016000:Cluster Analysis; D003663:Decision Trees; D005260:Female; D006801:Humans; D007405:Intervertebral Disc Displacement / Q000601:surgery*; D008297:Male; D008875:Middle Aged; D009873:Operating Rooms; D019637:Orthopedic Procedures / Q000379:methods*