LC-Smart: A Deep Learning-Based Quality Control Model for Laparoscopic Cholecystectomy
Status
Conditions
Details and patient eligibility
About
Objective: Critical view of safety (CVS) is a successful technique to reduce bile duct injury during laparoscopic cholecystectomy (LC). We aimed to create a deep learning-based quality control model for LC and reduce the learning curve for junior surgeons, which would automatically assess whether surgeons are CVS conscious during procedures.Methods: We retrospectively collected 308 LC videos from public datasets (Cholec80, Endoscapes) and Sun Yat-sen Memorial Hospital. Video frames were labeled using binary classification and feature optimization methods, such as black border clipping and sliding windows. Two neural networks, ResNet-50 and EfficientNetV2-S, were trained and evaluated based on F1 scores and accuracy. Additionally, We created an online CVS recognition system (LC-Smart), tested it using 171 films from two hospitals, and compared the results to two local senior doctors.
Enrollment
Sex
Volunteers
Inclusion criteria
- complete video data with no missing footage;
- surgical procedure identified as laparoscopic cholecystectomy;
- full visibility of the surgical area in the video;
- successful completion of the procedure;
- absence of significant anatomical variations
- video resolution no less than 720×560.
Exclusion criteria
- substantial intraoperative adhesions
- a history of previous abdominal or pelvic procedures
- a conversion to open surgery during the procedure
- significant bleeding that obscured structural identification.
Trial contacts and locations
Loading...
Data sourced from clinicaltrials.gov
Clinical trials
Research sites
Resources
Legal