Related Experiment Video
Updated: May 10, 2025
07:46
Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
Published on: August 9, 2024
583
Artificial intelligence-assisted phase recognition and skill assessment in laparoscopic surgery: a systematic review
Wenqiang Liao1, Ying Zhu2, Hanwei Zhang3
1Department of General Surgery, RuiJin Hospital LuWan Branch, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Frontiers in Surgery
|April 28, 2025
Summary
This review explores laparoscopic surgery phase recognition and skill evaluation. It details datasets, high-performing methods, and common characteristics to advance surgical training and patient outcomes.
Area of Science:
- Minimally invasive surgery
- Surgical technology
- Medical education
Background:
- Laparoscopic surgery is integral to modern procedures.
- Automated phase recognition and skill evaluation enhance surgical quality.
- These technologies are crucial for improving surgeon proficiency.
Purpose of the Study:
- To review advancements in laparoscopic surgery, phase recognition, and skill evaluation.
- To clarify the relationship between surgical phases, skill assessment, and surgical tasks.
- To provide references for researchers and guide future development.
Main Methods:
- Summarizing research progress in laparoscopic surgery.
- Detailing publicly available surgical datasets for phase recognition.
- Highlighting high-performing research methods and their characteristics.
Main Results:
- Common characteristics of superior methods in laparoscopic phase recognition identified.
- Summary of prevalent phase recognition and skill evaluation models.
- Overview of laparoscopic surgical skill evaluation standards and methods.
Conclusions:
- Research in laparoscopic surgery phase recognition and skill evaluation is advancing.
- Understanding datasets and methods is key to improving surgical training.
- Future directions focus on overcoming current research challenges.
