Leveraging Artificial Intelligence for Self-Directed Learning: A Comparative Analysis of Surgical Suture Techniques Through Image-Based Assessment
Author: Khawlah Alzaben
Base Hospital / Institution: King Khaled Eye Specialist Hospital
ePoster presentation
Abstract ID: 24-428
Purpose
Explore the ability and limitation of artificial intelligence in serving as a tool for self-direct learning in the field of surgical suturing.
Methods
Images of sutured wound models were captured and processed using edge detection algorithms to highlight the sutures and knots. Stitch lengths were measured in pixels, and the mean and standard deviation of the stitch spacing were calculated for each suture.
Results
Algorithms for detecting edges can identify knots, produce a histogram visualization of the suture method, and provide constructive criticism.
Conclusion
The research highlights the potential use of artificial intelligence and image-based analysis in surgical education.
Additional Authors
| First name | Last name | Base Hospital / Institution |
|---|---|---|
| Mohammad | Alzaben | King Khaled Eye Specialist Hospital |
| Alanoud | Albazi | King Khaled Eye Specialist Hospital |
