期刊
ANNALS OF SURGERY
卷 275, 期 5, 页码 955-961出版社
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/SLA.0000000000004351
关键词
anatomical segmentation; artificial intelligence; computer vision; critical view of safety; deep learning; laparoscopic cholecystectomy; surgical data science; surgical safety
类别
资金
- EAES Research Grant
- French State Funds [ANR-11-LABX-0004, ANR-10-IDEX-0002-02, ANR-10-IAHU-02]
- BPI France through Project CONDOR
This study developed a deep learning model to automatically segment hepatocystic anatomy and assess the criteria defining the critical view of safety in laparoscopic cholecystectomy. The results demonstrate that the model achieved reliable performance in segmentation and evaluation tasks.
Objective: To develop a deep learning model to automatically segment hepatocystic anatomy and assess the criteria defining the critical view of safety (CVS) in laparoscopic cholecystectomy (LC). Background: Poor implementation and subjective interpretation of CVS contributes to the stable rates of bile duct injuries in LC. As CVS is assessed visually, this task can be automated by using computer vision, an area of artificial intelligence aimed at interpreting images. Methods: Still images from LC videos were annotated with CVS criteria and hepatocystic anatomy segmentation. A deep neural network comprising a segmentation model to highlight hepatocystic anatomy and a classification model to predict CVS criteria achievement was trained and tested using 5-fold cross validation. Intersection over union, average precision, and balanced accuracy were computed to evaluate the model performance versus the annotated ground truth. Results: A total of 2854 images from 201 LC videos were annotated and 402 images were further segmented. Mean intersection over union for segmentation was 66.6%. The model assessed the achievement of CVS criteria with a mean average precision and balanced accuracy of 71.9% and 71.4%, respectively. Conclusions: Deep learning algorithms can be trained to reliably segment hepatocystic anatomy and assess CVS criteria in still laparoscopic images. Surgical-technical partnerships should be encouraged to develop and evaluate deep learning models to improve surgical safety.
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