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Workflow mining for visualization and analysis of surgeries

  • Tobias BlumEmail author
  • Nicolas Padoy
  • Hubertus Feußner
  • Nassir Navab
Original Article

Abstract

Objective

Modeling the workflow of a surgery is a topic of growing interest. Workflow models can be used to analyze statistical properties of a surgery, for intuitive visualization, evaluation and other applications. In most cases, workflow models are created manually, which is a time consuming process that might suffer from a personal bias. In this work, an approach for automatic workflow mining is presented.

Materials and methods

Ten process logs, each describing a single instance of a laparoscopic cholecystectomy, are used to build a Hidden Markov Model (HMM). Using a merging approach, models at different levels of detail are generated. These embody statistical information concerning aspects like duration of actions or tool usage during the surgery.

Results

A Graphical User Interface (GUI) is presented, that uses a graph representation of the HMM to intuitively visualize surgical workflow. It allows changing the level of detail by expanding and merging nodes. The GUI can also be used to compare videos of surgeries which are synchronized to the model.

Conclusions

The proposed method allows automatic generation and visualization of a statistical model describing the workflow of a surgery.

Keywords

Workflow mining Surgical workflow analysis Information visualization Cholecystectomy Hidden Markov models 

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Copyright information

© CARS 2008

Authors and Affiliations

  • Tobias Blum
    • 1
    Email author
  • Nicolas Padoy
    • 1
    • 3
  • Hubertus Feußner
    • 2
  • Nassir Navab
    • 1
  1. 1.Computer Aided Medical Procedures (CAMP)Technische Universität MünchenMunichGermany
  2. 2.Department of Surgery, Klinikum Rechts der IsarTechnische Universität MünchenMunichGermany
  3. 3.LORIA-INRIA LorraineNancyFrance

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