LC-Smart: A Deep Learning-Based Quality Control Model for Laparoscopic Cholecystectomy

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

308 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-10-24

Study Completion Date

2024-11-30

Brief Summary

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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.

Detailed Description

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Conditions

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Laparoscopic Cholecystectomy

Study Design

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Observational Model Type

OTHER

Study Time Perspective

RETROSPECTIVE

Eligibility Criteria

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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.

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Responsible Party

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Responsibility Role SPONSOR

Locations

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Sun Yat-sen Memorial Hospital,SUn Yat-sen UNiversity

Guangzhou, Guangdong, China

Site Status

Countries

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China

Other Identifiers

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SYSKY-2024-1015-01

Identifier Type: -

Identifier Source: org_study_id