Cholecystectomy, Surgical Video Identification
Conditions
Keywords
Surgical Video Identification, Deep Learning, Cholecystectomy, Artificial Intelligence
Brief 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.
Detailed description
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.
Interventions
DIAGNOSTIC_TESTAI-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.
Sponsors
The First Affiliated Hospital of Zhengzhou University
Capital Medical University
Beijing Anzhen Hospital
Shanghai East Hospital of Tongji University
Peking University People's Hospital
Chinese Academy of Sciences
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Patients aged 18 or above who are diagnosed by a doctor as needing laparoscopic cholecystectomy
Exclusion criteria
* Patients who did not undergo surgery at the original hospital and those whose videos were blurry were excluded.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Inference Latency | 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 |
Other
| Measure | Time frame | Description |
|---|---|---|
| Class-Specific Precision and Recall for Critical Structures | 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 |
Countries
China
Contacts
Primary ContactDi Dong, Ph.D
Backup ContactQian Liang, M.A.
Outcome results
None listed