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Related Experiment Video

Updated: Jan 9, 2026

Optimizing Minimally Invasive Spine Surgery: A Fully 3D CT O-Arm Navigated Workflow in MIS TLIF
08:34

Optimizing Minimally Invasive Spine Surgery: A Fully 3D CT O-Arm Navigated Workflow in MIS TLIF

Published on: October 17, 2025

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Surgical Workflow Analysis: An Explainable Approach.

Christos Spiliadis, Yiheng Chang, Justin Dauwels

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary
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    This study presents an automated system for surgical workflow analysis in catheterization labs, improving efficiency and patient safety. The explainable two-stage model achieves high accuracy in real-time classification of workflow phases.

    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Machine Learning in Healthcare

    Background:

    • Manual surgical workflow analysis is inefficient and inconsistent.
    • Need for automated solutions in catheterization labs for improved efficiency and patient safety.
    • Existing methods lack interpretability and adaptability.

    Purpose of the Study:

    • To develop an explainable, automated two-stage model for surgical workflow analysis.
    • To enhance real-time classification of workflow phases using computer vision and machine learning.
    • To validate the model's generalizability and clinical implementation potential.

    Main Methods:

    • Utilized ceiling-mounted cameras for data acquisition.
    • Implemented a two-stage model combining YOLOv8 object detection and Gaussian Mixture Model - Hidden Markov Model (GMM-HMM).

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    Last Updated: Jan 9, 2026

    Optimizing Minimally Invasive Spine Surgery: A Fully 3D CT O-Arm Navigated Workflow in MIS TLIF
    08:34

    Optimizing Minimally Invasive Spine Surgery: A Fully 3D CT O-Arm Navigated Workflow in MIS TLIF

    Published on: October 17, 2025

    365
    Emergency Undocking in Robotic Surgery: A Simulation Curriculum
    06:48

    Emergency Undocking in Robotic Surgery: A Simulation Curriculum

    Published on: May 20, 2018

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  • GMM-HMM modeled spatial-temporal dynamics for real-time workflow phase classification.

    • Main Results:

    • Achieved high accuracy: 95.2% on RdGG dataset and 95.4% on HH Tampere dataset.
    • Demonstrated generalizability across different hospital environments.
    • Highlighted model explainability, robustness, and precise phase transition identification.

    Conclusions:

    • The developed model offers a precise, explainable, and adaptable solution for automated surgical workflow analysis.
    • Potential for clinical implementation due to real-time application and cross-hospital adaptability.
    • Future work includes improving occlusion robustness and integrating multi-modal data.