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Workflow mining for visualization and analysis of surgeries

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

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.

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Authors and Affiliations

  1. Computer Aided Medical Procedures (CAMP), Technische Universität München, Munich, Germany

    Tobias Blum, Nicolas Padoy & Nassir Navab

  2. Department of Surgery, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany

    Hubertus Feußner

  3. LORIA-INRIA Lorraine, Nancy, France

    Nicolas Padoy

Authors
  1. Tobias Blum
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  2. Nicolas Padoy
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  3. Hubertus Feußner
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  4. Nassir Navab
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Corresponding author

Correspondence to Tobias Blum.

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Blum, T., Padoy, N., Feußner, H. et al. Workflow mining for visualization and analysis of surgeries. Int J CARS 3, 379–386 (2008). https://doi.org/10.1007/s11548-008-0239-0

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  • DOI: https://doi.org/10.1007/s11548-008-0239-0

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