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

Hirenkumar Nakawala, Roberto Bianchi, Laura Erica Pescatori, Ottavio De Cobelli, Giancarlo Ferrigno, Elena De Momi

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)685-696
Number of pages12
JournalInternational journal of computer assisted radiology and surgery
Volume14
Issue number4
DOIs
Publication statusPublished - Apr 1 2019

Keywords

  • Deep learning
  • Knowledge representation
  • Robot-assisted partial nephrectomy
  • Surgical workflow

ASJC Scopus subject areas

  • Surgery
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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