Similarity metrics for surgical process models

  • Authors:

  • Thomas Neumuth;Frank Loebe;Pierre Jannin

  • Affiliations:

  • Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, Semmelweisstr. 14, D-04103 Leipzig, Germany;Department of Computer Science, University of Leipzig, Johannisgasse 26, D-04103 Leipzig, Germany and Institute of Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, ...;INSERM, U746, Faculté de Médecine, 2, Avenue du Pr. Léon Bernard, CS 34317, 35043 Rennes Cedex, France and INRIA, VisAGeS Unit/Project, 2, Avenue du Pr. Léon Bernard, CS 34317, ...

  • Venue:
  • Artificial Intelligence in Medicine
  • Year:
  • 2012

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Abstract

Objective: The objective of this work is to introduce a set of similarity metrics for comparing surgical process models (SPMs). SPMs are progression models of surgical interventions that support quantitative analyses of surgical activities, supporting systems engineering or process optimization. Methods and materials: Five different similarity metrics are presented and proven. These metrics deal with several dimensions of process compliance in surgery, including granularity, content, time, order, and frequency of surgical activities. The metrics were experimentally validated using 20 clinical data sets each for cataract interventions, craniotomy interventions, and supratentorial tumor resections. The clinical data sets were controllably modified in simulations, which were iterated ten times, resulting in a total of 600 simulated data sets. The simulated data sets were subsequently compared to the original data sets to empirically assess the predictive validity of the metrics. Results: We show that the results of the metrics for the surgical process models correlate significantly (p