Research on Identifying Critical Surgical Anatomy in Cholecystectomy Videos Based on Deep Learning

Condition(s) studied

Investigational drug(s) / intervention(s)

AI-assisted Intraoperative Anatomy Analysis: 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.

Study summary

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.

Eligibility

Primary outcome measure(s)

• Dice Similarity Coefficient — 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.
• Mean Intersection over Union — 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.
• Global Accuracy — 3 years
  The proportion of correctly classified pixels out of the total number of pixels in the image.

Trial sites (5)

More Chinese Academy of Sciences trials in China

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