NCT Number: NCT07158372
Laparoscopic cholecystectomy is a common surgical procedure, but it carries the potential for bile duct injury and other surgical risks. To provide visual assistance to surgeons during surgery and mitigate these risks, this research project aims to develop a real-time object recognition algorithm based on deep learning technology. This algorithm will label key anatomical structures in laparoscopic cholecystectomy videos, providing surgeons with immediate information on dangerous and safe areas.
Interested in participating?
Request Info18 year and older
All sexes
Observational
The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
Laparoscopic cholecystectomy is a common surgical procedure, but it carries the potential for bile duct injury and other surgical risks. To provide visual assistance to surgeons during surgery and mitigate these risks, this research project aims to develop a real-time object recognition algorithm based on deep learning technology. This algorithm will label key anatomical structures in laparoscopic cholecystectomy videos, providing surgeons with immediate information on dangerous and safe areas.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
This is a prospective study on patients aged 18 years or more diagnosed with laparoscopic cholecystectomy. We will collect information such as laparoscopic cholecystectomy videos and procedure type, excluding patients who did not undergo surgery at the original hospital or whose videos were blurry.
Time frame: 3 years
Dice Similarity Coefficient is a statistical measure of the similarity between two sets of data. In the context of image segmentation, it is used to quantify the spatial overlap between a predicted segmentation mask and its corresponding ground truth mask.
Time frame: 3 years
Mean Intersection over Union provides a measure of the overlap between the predicted segmentation and the ground truth, averaged across all classes present in the dataset.
Time frame: 3 years
The proportion of correctly classified pixels out of the total number of pixels in the image.
Time frame: 3 years
time taken by the algorithm to process a single video frame and generate the segmentation masks (inference latency), or equivalently, the number of frames processed per second
Time frame: 3 years
Precision: The proportion of predicted pixels for a structure that are actually part of that structure Recall: The proportion of actual ground truth pixels for a structure that were correctly identified by the algorithm
Contact information is provided by the study sponsor or research team.
Chinese Academy of Sciences
Other Gov
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