Automatic performance evaluation of the intracorporeal suture exercise

Purpose This work uses deep learning algorithms to provide automated feedback on the suture with intracorporeal knot exercise in the fundamentals of laparoscopic surgery simulator. Different metrics were designed to provide informative feedback to the user on how to complete the task more efficientl...

Full description

Saved in:
Bibliographic Details
Published in:International journal for computer assisted radiology and surgery 2024-01, Vol.19 (1), p.83-86
Main Authors: Halperin, Liran, Sroka, Gideon, Zuckerman, Ido, Laufer, Shlomi
Format: Article
Language:English
Subjects:
Algorithms
Automation
Computer Imaging
Computer Science
Deep learning
Feedback
Health Informatics
Image classification
Image segmentation
Imaging
Insertion
Machine learning
Medicine
Medicine & Public Health
Object recognition
Pattern Recognition and Graphics
Performance evaluation
Performance measurement
Radiology
Semantic segmentation
Short Communication
Surgeons
Surgery
Sutures
Vision
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Purpose This work uses deep learning algorithms to provide automated feedback on the suture with intracorporeal knot exercise in the fundamentals of laparoscopic surgery simulator. Different metrics were designed to provide informative feedback to the user on how to complete the task more efficiently. The automation of the feedback will allow students to practice at any time without the supervision of experts. Methods Five residents and five senior surgeons participated in the study. Object detection, image classification, and semantic segmentation deep learning algorithms were used to collect statistics on the practitioner’s performance. Three task-specific metrics were defined. The metrics refer to the way the practitioner holds the needle before the insertion to the Penrose drain, and the amount of movement of the Penrose drain during the needle’s insertion. Results Good agreement between the human labeling and the different algorithms’ performance and metric values was achieved. The difference between the scores of the senior surgeons and the surgical residents was statistically significant for one of the metrics. Conclusion We developed a system that provides performance metrics of the intracorporeal suture exercise. These metrics can help surgical residents practice independently and receive informative feedback on how they entered the needle into the Penrose.
ISSN:1861-6429
1861-6410
1861-6429
DOI:10.1007/s11548-023-02963-6