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“Deep-Onto” network for surgical workflow and context recognition

  • Hirenkumar Nakawala
  • Roberto Bianchi
  • Laura Erica Pescatori
  • Ottavio De Cobelli
  • Giancarlo Ferrigno
  • Elena De Momi
Original Article
  • 85 Downloads

Abstract

Purpose

Surgical workflow recognition and context-aware systems could allow better decision making and surgical planning by providing the focused information, which may eventually enhance surgical outcomes. While current developments in computer-assisted surgical systems are mostly focused on recognizing surgical phases, they lack recognition of surgical workflow sequence and other contextual element, e.g., “Instruments.” Our study proposes a hybrid approach, i.e., using deep learning and knowledge representation, to facilitate recognition of the surgical workflow.

Methods

We implemented “Deep-Onto” network, which is an ensemble of deep learning models and knowledge management tools, ontology and production rules. As a prototypical scenario, we chose robot-assisted partial nephrectomy (RAPN). We annotated RAPN videos with surgical entities, e.g., “Step” and so forth. We performed different experiments, including the inter-subject variability, to recognize surgical steps. The corresponding subsequent steps along with other surgical contexts, i.e., “Actions,” “Phase” and “Instruments,” were also recognized.

Results

The system was able to recognize 10 RAPN steps with the prevalence-weighted macro-average (PWMA) recall of 0.83, PWMA precision of 0.74, PWMA F1 score of 0.76, and the accuracy of 74.29% on 9 videos of RAPN.

Conclusion

We found that the combined use of deep learning and knowledge representation techniques is a promising approach for the multi-level recognition of RAPN surgical workflow.

Keywords

Deep learning Knowledge representation Robot-assisted partial nephrectomy Surgical workflow 

Notes

Acknowledgements

This project has received funding from the European Unions Horizon 2020 research and innovation programme under Grant Agreement No. H2020-ICT-2016-732515. The Titan Xp used for this research was donated by the NVIDIA Corporation.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standard

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© CARS 2018

Authors and Affiliations

  • Hirenkumar Nakawala
    • 1
  • Roberto Bianchi
    • 2
  • Laura Erica Pescatori
    • 1
  • Ottavio De Cobelli
    • 2
  • Giancarlo Ferrigno
    • 1
  • Elena De Momi
    • 1
  1. 1.Department of Electronics, Information and Bioengineering (DEIB)Politecnico di MilanoMilanItaly
  2. 2.Department of UrologyEuropean Institute of Oncology (IEO)MilanItaly

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