--- title: Research on Identifying Critical Surgical Anatomy in Cholecystectomy Videos Based on Deep Learning nct_id: NCT07158372 overall_status: RECRUITING sponsor: Chinese Academy of Sciences study_type: OBSERVATIONAL primary_condition: Cholecystectomy countries: China canonical_url: "https://parkinsonspathways.com/agent/trials/NCT07158372.md" clinicaltrials_gov: "https://clinicaltrials.gov/study/NCT07158372" ct_last_update_post_date: 2025-09-05 last_seen_at: "2026-05-12T07:12:49.585Z" source: ClinicalTrials.gov (mirrored, no enrichment) --- # Research on Identifying Critical Surgical Anatomy in Cholecystectomy Videos Based on Deep Learning **NCT ID:** [NCT07158372](https://clinicaltrials.gov/study/NCT07158372) ## Key Facts - **Status:** RECRUITING - **Study Type:** OBSERVATIONAL - **Target Enrollment:** 200 - **Lead Sponsor:** Chinese Academy of Sciences - **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 - **Conditions:** Cholecystectomy, Surgical Video Identification - **Start Date:** 2025-08-15 - **Completion Date:** 2028-08-15 - **CT.gov Last Update:** 2025-09-05 ## 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. ## Eligibility - **Minimum age:** 18 Years - **Sex:** ALL - **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. ``` ## Arms - **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. - **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. - **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. - **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 ## Interventions - **AI-assisted Intraoperative Anatomy Analysis** (DIAGNOSTIC_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. ## Primary Outcomes - **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. ## Secondary Outcomes - **Inference Latency** _(time frame: 3 years)_ ## Locations (5) - The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China — _RECRUITING_ - Beijing Anzhen Hospital, Capital Medical University, Beijing, China — _RECRUITING_ - Beijing Luhe Hospital, Capital Medical University, Beijing, China — _RECRUITING_ - Peking university people's hospital, Beijing, China — _RECRUITING_ - Shanghai East Hospital of Tongji University, Shanghai, China — _RECRUITING_ ## Recent Field Changes (last 30 days) - `eligibility.minAge` — added _(2026-05-12)_ - `eligibility.sex` — added _(2026-05-12)_ - `status.overallStatus` — added _(2026-05-12)_ - `status.primaryCompletionDate` — added _(2026-05-12)_ - `status.completionDate` — added _(2026-05-12)_ - `status.lastUpdatePostDate` — added _(2026-05-12)_ - `design.enrollmentCount` — added _(2026-05-12)_ - `eligibility.criteria` — added _(2026-05-12)_ - `outcomes.primary` — added _(2026-05-12)_ - `outcomes.secondary` — added _(2026-05-12)_ - `armsInterventions.arms` — added _(2026-05-12)_ - `armsInterventions.interventions` — added _(2026-05-12)_ - `sponsor.lead` — added _(2026-05-12)_ - `sponsor.collaborators` — added _(2026-05-12)_ - `results.hasResults` — added _(2026-05-12)_ - `locations.the first affiliated hospital of zhengzhou university|zhengzhou|henan|china` — added _(2026-05-12)_ - `locations.beijing anzhen hospital, capital medical university|beijing||china` — added _(2026-05-12)_ - `locations.beijing luhe hospital, capital medical university|beijing||china` — added _(2026-05-12)_ - `locations.peking university people's hospital|beijing||china` — added _(2026-05-12)_ - `locations.shanghai east hospital of tongji university|shanghai||china` — added _(2026-05-12)_ --- *Canonical: https://parkinsonspathways.com/agent/trials/NCT07158372.md* *Source data (authoritative): https://clinicaltrials.gov/study/NCT07158372* *This page is a raw mirror with no AI summary, no editorial enrichment, and no Parkinson's-specific filtering.*