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Endoscopic Studies I: Bronchoscopy and Thoracoscopy01:30

Endoscopic Studies I: Bronchoscopy and Thoracoscopy

Endoscopy is a non-surgical medical technique used to examine a person's internal organs and vessels. This lesson will focus on two types of endoscopic studies: bronchoscopy and thoracoscopy.
Bronchoscopy
Description
Bronchoscopy is a procedure that involves direct visualization of the larynx, trachea, and bronchi for diagnostic and therapeutic purposes. A flexible fiber optic or rigid bronchoscope is used to carry out the procedure. The fiber-optic bronchoscope is more frequently used due to...

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Collaborative Human-Computer Vision Operative Video Analysis Algorithm for Analyzing Surgical Fluency and Surgical

Chia-En Wong1, Pei-Wen Chen2,3, Heng-Jui Hsu4

  • 1Division of Neurosurgery, Department of Surgery, National Cheng Kung University Hospital, Tainan, Taiwan.

Journal of Medical Internet Research
|July 4, 2024
PubMed
Summary

Computer vision (CV) analysis of endoscopic endonasal approach (EEA) videos significantly reduces review time and improves accuracy in detecting surgical interruptions. This technology aids surgeon training and enhances surgical fluency assessment in pituitary adenoma resection.

Keywords:
algorithmcomputer visionendonasal endoscopic approachpituitarytranssphenoidal surgery

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Area of Science:

  • Neurosurgery
  • Medical Imaging
  • Computer Vision

Background:

  • Endonasal endoscopic approach (EEA) is effective for pituitary adenoma resection.
  • Manual review of operative videos is time-consuming and hinders surgeon training.
  • Computer vision (CV) algorithms offer a potential solution to expedite video review and facilitate learning curves.

Purpose of the Study:

  • To evaluate a CV-based video analysis system for detecting surgical interruptions and assessing surgical fluency in EEA.
  • To compare the accuracy and time efficiency of CV-based analysis versus manual review.
  • To investigate the impact of surgical experience on interruptions during EEA.

Main Methods:

  • OpenCV algorithm used to determine dominant color per video frame.
  • Algorithm developed to identify surgical interruptions based on dominant color pixel alterations exceeding defined thresholds.
  • CV analysis accuracy validated against manual review; time efficiency was recorded.

Main Results:

  • CV analysis achieved 93-95% accuracy across training and test datasets.
  • CV-based analysis reduced video review time by 86% (22.6 vs. 166.8 minutes).
  • Human-CV collaboration improved accuracy to 98.5% with a 74% time reduction.
  • Sellar phase showed fewer interruptions (4.9/10 min) and shorter durations (31.1s/10 min).
  • Increased surgical experience correlated with fewer (2.9 vs. 4.9/10 min) and shorter (19.8 vs. 41.1s/10 min) interruptions in the sellar phase.

Conclusions:

  • CV-based analysis accurately detects surgical interruptions during EEA with 93-98% accuracy.
  • CV significantly reduces operative video review time compared to manual methods.
  • CV application facilitates surgeon training and overcomes the learning curve in endoscopic skull base surgery.