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Related Experiment Video

Updated: Jun 20, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

A microdiscectomy surgical video annotation framework for supervised machine learning applications.

Kochai Jan Jawed¹, Ian Buchanan², Kevin Cleary³

¹Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC, USA. kjawed@childrensnational.org.

International Journal of Computer Assisted Radiology and Surgery

|July 19, 2024

Summary

This summary is machine-generated.

This study introduces a standardized workflow for annotating microdiscectomy surgical videos. This method aids in developing machine learning models to potentially predict adverse surgical events and improve patient outcomes.

Keywords:

Lumbar discectomy Machine learning Microdiscectomy

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Area of Science:

Spine Surgery
Medical Imaging
Machine Learning

Background:

Lumbar discectomy is a common spinal procedure with potential complications.
Standardized video annotation is crucial for developing machine learning applications in surgery.

Purpose of the Study:

To present a video annotation methodology for microdiscectomy.
To develop a surgical workflow for standardized video annotation.
To lay the groundwork for using computer vision and machine learning to predict adverse surgical events.

Main Methods:

Collected and anonymized microdiscectomy surgical videos from a multi-center collaboration.
Utilized an online annotation platform for frame-by-frame labeling of instruments, anatomy, and surgical phases.
Developed an annotation framework based on literature review and surgical expertise.

Main Results:

Produced a single-surgeon annotated microdiscectomy video with labeled elements.
Created a standardized workflow for training novice annotators on the annotation software.
Established a repeatable process for consistent surgical video annotation.

Conclusions:

A standardized workflow is essential for surgical video annotation and machine learning.
The developed workflow facilitates quantitative analysis of microdiscectomy videos.
Future work will focus on process modeling and outcome prediction to demonstrate clinical relevance.