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Related Experiment Video

Updated: Oct 23, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Can Deep Learning Algorithms Help Identify Surgical Workflow and Techniques?

Hossein Mohamadipanah1, LaDonna Kearse1, Anna Witt1

  • 1Department of Surgery, Stanford University School of Medicine, Stanford, California.

The Journal of Surgical Research
|August 16, 2021
PubMed
Summary
This summary is machine-generated.

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Artificial intelligence (AI) algorithms can efficiently analyze surgical videos to detect workflow phases and technical approaches, improving performance review and education. These AI tools accurately segment surgical procedures and identify techniques without impacting execution time or injury identification.

Area of Science:

  • Surgical education and performance analysis.
  • Application of artificial intelligence in medicine.
  • Computer vision for surgical workflow analysis.

Background:

  • Surgical video review is valuable for education and performance assessment but is time-consuming.
  • Artificial intelligence (AI) offers a potential solution to automate video analysis.
  • This study implemented AI to improve the efficiency of surgical video review.

Purpose of the Study:

  • To develop and evaluate AI algorithms for detecting surgical workflow phases and technical approaches in simulated bowel repair videos.
  • To assess the accuracy and efficiency of AI in analyzing surgical procedures.
  • To determine if different technical approaches impact surgical outcomes.

Main Methods:

  • Two hundred participants performed a simulated open bowel repair, including Injury Identification and Suture Repair phases.
Keywords:
Artificial intelligenceInjury identificationObject detectionOpen surgeryPhase detectionVideo-based assessment

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  • A phase detection algorithm (MobileNetV2+GRU) was used to identify the two main surgical phases from video data.
  • An object detection algorithm (YOLOv3) was employed to differentiate three technical approaches based on hand and tool usage during the "running the bowel" step.

    • Main Results:

    • The phase detection algorithm achieved high precision in segmenting Injury Identification (86 ± 9%) and Suture Repair (81 ± 6%).
    • The object detection algorithm demonstrated high average precision for identifying hand (99.32%) and tool (94.47%) usage.
    • No significant differences were found in execution time or injury identification rates among the three technical approaches.

    Conclusions:

    • AI algorithms accurately segment surgical workflow and identify technical approaches.
    • Automating surgical video analysis with AI can significantly enhance the efficiency of performance review.
    • AI holds great potential for improving surgical training and quality assessment through video analysis.