Related Experiment Video
Updated: Sep 9, 2025
04:48
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
2.9K
Applications of Deep Learning Models in Laparoscopy for Gynecology
Fani Gkrozou1, Vasileios Bais1, Charikleia Skentou1
1Department of Obstetrics and Gynecology, Medical School of Ioannina, University General Hospital, 45500 Ioannina, Greece.
Medicina (Kaunas, Lithuania)
|August 28, 2025
Summary
Deep learning (DL) models are advancing gynecologic laparoscopy by improving anatomy recognition and instrument tracking. These artificial intelligence (AI) applications enhance surgical guidance and training.
Area of Science:
- Medical Technology
- Surgical Innovation
- Artificial Intelligence in Medicine
Background:
- Artificial Intelligence (AI) is increasingly utilized in healthcare.
- Deep learning (DL) models show significant potential in surgical applications.
Purpose of the Study:
- To systematically review and synthesize research on DL model development and validation in gynecologic laparoscopic surgeries.
- To identify key applications and trends in AI-driven gynecologic laparoscopy.
Main Methods:
- Comprehensive literature search of MEDLINE, IEEE Xplore, and Google Scholar (2000-2025).
- Inclusion criteria focused on DL models (specifically Convolutional Neural Networks - CNNs) applied to gynecologic laparoscopic datasets.
- Exclusion of non-gynecologic, non-laparoscopic, non-CNN, and non-English studies.
Main Results:
- 16 studies met inclusion criteria from 621 initial records.
- Identified applications include anatomy classification (6 studies), anatomy segmentation (5 studies), surgical instrument classification/segmentation (5 studies), and surgical action recognition (5 studies).
Conclusions:
- AI, particularly DL, is playing a growing role in gynecologic laparoscopy.
- Advancements in AI are expected to enhance intraoperative guidance and standardize surgical training through improved anatomy recognition, instrument tracking, and action analysis.
