CholecystectomyNCT07158372
Research on Identifying Critical Surgical Anatomy in Cholecystectomy Videos Based on Deep Learning
An observational study of Cholecystectomy and Surgical Video Identification, run by Chinese Academy of Sciences at 5 sites in China.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Cholecystectomy, Surgical Video Identification
- What is being tested?
- AI-assisted Intraoperative Anatomy Analysis
- How many people
- 200 participants
- Where
- 5 sitesChina
- Study dates
- 15 Aug 2025to 15 Aug 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients aged 18 or above who are diagnosed by a doctor as needing laparoscopic cholecystectomy
Who may be able to join
- Patients aged 18 or above who are diagnosed by a doctor as needing laparoscopic cholecystectomy
Who cannot join
- Patients who did not undergo surgery at the original hospital and those whose videos were blurry were excluded.
Trial locations and contacts
5 sites in 1 country. Contact the study team or a site near you to ask about taking part.
Qian Liang, M.A. contact
5 sites in 1 country
China5 sites, 5 recruiting
- Recruiting
Beijing Anzhen Hospital, Capital Medical University
Beijing
Wei Han
- Recruiting
Beijing Luhe Hospital, Capital Medical University
Beijing
ShaoLong Hao
- Recruiting
Peking university people's hospital
Beijing
Peng Guo
- Recruiting
Shanghai East Hospital of Tongji University
Shanghai
Zhonghua Ji
- Recruiting
The First Affiliated Hospital of Zhengzhou University
Zhengzhou, Henan
Zhaochen Liu
About this study
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.
Read the 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.
- Conditions
- Cholecystectomy, Surgical Video Identification
- Being tested
- AI-assisted Intraoperative Anatomy Analysis
- Keywords
- Surgical Video Identification, Deep Learning, Cholecystectomy, Artificial Intelligence
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-assisted Intraoperative Anatomy AnalysisDiagnostic test
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 group | Role | What they receive |
|---|---|---|
| The First Affiliated Hospital of Zhengzhou University | patients aged 18 years and older 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. Receives: Diagnostic Test: AI-assisted Intraoperative Anatomy Analysis | |
| Beijing Luhe Hospital, Capital Medical University | patients aged 18 years and older 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. Receives: Diagnostic Test: AI-assisted Intraoperative Anatomy Analysis | |
| Shanghai East Hospital of Tongji University | patients aged 18 years and older 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. Receives: Diagnostic Test: AI-assisted Intraoperative Anatomy Analysis | |
| Peking university people's hospital | patients aged 18 years and older 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 Receives: Diagnostic Test: AI-assisted Intraoperative Anatomy Analysis |
What is being measured
The primary outcome is the main question the study is designed to answer.
Dice Similarity Coefficient
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.
Measured: 3 years
Mean Intersection over Union
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.
Measured: 3 years
Global Accuracy
The proportion of correctly classified pixels out of the total number of pixels in the image.
Measured: 3 years
Secondary outcomes (1)
Inference Latency
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
Measured: 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Sep 2025
- Study start15 Aug 2025
- Primary completion15 Aug 2026 (estimated)
- Study completion15 Aug 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for CholecystectomyStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Chinese Academy of Sciences
- Sponsor type
- Other government
- Collaborators
- The First Affiliated Hospital of Zhengzhou University, Capital Medical University, Beijing Anzhen Hospital, Shanghai East Hospital of Tongji University, Peking University People's Hospital
- Responsible party
- Principal investigator, Di Dong, Chinese Academy of Sciences
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07158372
- Sponsor's protocol ID
- CASMI007
- Status last verified
- Aug 2025
- First submitted
- 28 Aug 2025
- Last updated
- 5 Sep 2025
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 5 Sep 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Research on Identifying Critical Surgical Anatomy in Cholecystectomy Videos Based on Deep Learning
- ClinicalTrials.gov ID
- NCT07158372
- Acronym / sponsor ID
- CASMI007
- Conditions
- Cholecystectomy, Surgical Video Identification
- Keywords
- Surgical Video Identification, Deep Learning, Cholecystectomy, Artificial Intelligence
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Chinese Academy of SciencesOther government
- Collaborators
- The First Affiliated Hospital of Zhengzhou University, Capital Medical University, Beijing Anzhen Hospital, Shanghai East Hospital of Tongji University, Peking University People's Hospital
- Study start
- 15 Aug 2025 (actual)
- Primary completion
- 15 Aug 2026 (estimated)
- Study completion
- 15 Aug 2028 (estimated)
- First posted
- 5 Sep 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| The First Affiliated Hospital of Zhengzhou University | – | Diagnostic Test: AI-assisted Intraoperative Anatomy Analysispatients aged 18 years and older 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. |
| Beijing Luhe Hospital, Capital Medical University | – | Diagnostic Test: AI-assisted Intraoperative Anatomy Analysispatients aged 18 years and older 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. |
| Shanghai East Hospital of Tongji University | – | Diagnostic Test: AI-assisted Intraoperative Anatomy Analysispatients aged 18 years and older 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. |
| Peking university people's hospital | – | Diagnostic Test: AI-assisted Intraoperative Anatomy Analysispatients aged 18 years and older 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 |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients aged 18 or above who are diagnosed by a doctor as needing laparoscopic cholecystectomy
Key inclusion criteria (1 listed)
- Patients aged 18 or above who are diagnosed by a doctor as needing laparoscopic cholecystectomy
Key exclusion criteria (1 listed)
- Patients who did not undergo surgery at the original hospital and those whose videos were blurry were excluded.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-assisted Intraoperative Anatomy AnalysisThis 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. | Diagnostic test | Beijing Luhe Hospital, Capital Medical University, Peking university people's hospital, Shanghai East Hospital of Tongji University, The First Affiliated Hospital of Zhengzhou University |
Primary outcome
Dice Similarity Coefficient
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.
Mean Intersection over Union
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.
Global Accuracy
Time frame: 3 years
The proportion of correctly classified pixels out of the total number of pixels in the image.
1 secondary outcome
Inference Latency
Time frame: 3 years
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 Sep 2025.
- Individual participant dataSharing planned: Undecided
Related trials for Cholecystectomy
Not yet recruitingPhase 4
Effect of Prophylactic Dexamethasone vs Placebo on Airway Dimensions Assessed by Ultrasonography in Patients Undergoing Laparoscopic Cholecystectomy
Airway Edema, Postoperative Complications
Not yet recruitingObservational study
Assessment of Optic Sheat Diameter (ONSD) and Eyeball Transverse Diameter (ETD) Ratio Measurement From Ocular Ultrasound
Optic Nerve Sheath Diameter Measurement, Intracranial Pressure Increase
Recruiting
US Guided EOI Block Versus ESPB for Postoperative Analgesia in Laparoscopic Cholecystectomy
Postoperative Pain
Enrolling by invitation
Heat Application and Shoulder Pillow for Post-Laparoscopic Shoulder Pain
Laparoscopic Cholecystectomy, Shoulder Pain
Active, not recruitingObservational study
Cholecystectomy, Biliary Diseases, and Colorectal Adenoma Risk: A Real-World Retrospective Study
Colorectal Adenoma, Cholecystectomy
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Qian Liang, M.A. contact
5 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Cholecystectomy and Surgical Video Identification, run by Chinese Academy of Sciences at 5 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 5 Sep 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 5 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Di Dong, Ph.D (+86 010-82618465, di.dong@ia.ac.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Dice Similarity Coefficient (time frame: 3 years). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 5 Sep 2025.