Prospective Collection and Registry Study of Multicenter, Multidisciplinary Surgical Minimally Invasive Videos (VISION)

August 5, 2026 updated by: Zeyu Zhang, PHD, Chinese Academy of Sciences

Prospective Observational Cohort Study on the Construction of Standardized Video Datasets for Multicenter, Multidisciplinary Minimally Invasive Laparoscopic and Robotic Surgery and Their Application in the Development of Surgical AI Large Models

This is a prospective multicenter patient registry study. We continuously collect full-length intraoperative surgical videos from thoracoscope, laparoscope, hysteroscope, transcervical resectoscope, cystoscope, prostate resectoscope, arthroscope, intervertebral foramen endoscope, otorhinolaryngology endoscope and endoscopic surgical robots, accompanied by inpatient medical records, preoperative imaging data and 5-year postoperative follow-up data. All imaging data will be standardized and de-identified to construct a large-scale standardized surgical video dataset. The dataset will be applied for training, verification and optimization of surgical video foundation large model, serving for surgical teaching, intraoperative operation quality control and basic medical AI research. We will also explore the correlation between intraoperative surgical details and postoperative prognosis to improve the standard specifications of minimally invasive surgery. No clinical intervention will be imposed on participants throughout the whole research.

Study Overview

Status

Not yet recruiting

Conditions

Study Type

Observational

Enrollment (Estimated)

2000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Name: KUNSHAN HE
Phone Number: +86 18500535530
Email: hekunshan@buaa.edu.cn

Study Locations

China
- - Beijing, China
    - Institute of Automation, Chinese Academy of Sciences

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

Adult
Older Adult

Accepts Healthy Volunteers

Sampling Method

Non-Probability Sample

Study Population

This prospective multicenter observational cohort study will enroll a total of 2,000 inpatients undergoing minimally invasive endoscopic, laparoscopic, or robotic surgery across multiple departments and participating medical centers.

Description

Inclusion Criteria:

Patients aged ≥ 18 years old hospitalized to receive minimally invasive endoscopic, laparoscopic or robotic surgical treatment for diseases of various body systems;
Complete full-length intraoperative surgical videos can be recorded during operation, with complete medical records and preoperative imaging data;
Participants fully understand the study, voluntarily sign written informed consent, and agree that their de-identified intraoperative images and clinical data can be used for scientific research.

Exclusion Criteria:

Minors under 18 years of age;
Patients with incomplete intraoperative videos or missing clinical imaging documents;
Patients with consciousness disturbance or mental disorders who cannot sign informed consent independently;
Subjects who refuse to participate in the study and disapprove the use of their medical data for research;
Patients who are predicted to be unavailable for long-term postoperative follow-up.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Time Frame
Completion rate of qualified intraoperative surgical imaging data Time Frame: Immediately after each surgery	Immediately after each surgery

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Completeness rate of long-term postoperative clinical follow-up Time Frame: 3 months, 1 year, 3 years and 5 years after surgery		3 months, 1 year, 3 years and 5 years after surgery
Reusability rate of annotated key anatomical structures in videos Time Frame: From completion of data warehousing and annotation, the reusability rate will be analyzed within 3 months, assessed up to 3 months after annotation completion.		From completion of data warehousing and annotation, the reusability rate will be analyzed within 3 months, assessed up to 3 months after annotation completion.
Feasibility rate (%) of surgical video dataset applied in different clinical AI research scenarios Time Frame: After full construction of the surgical video dataset, scenario feasibility assessment will be finished within 6 months, assessed up to 6 months after dataset construction.	Three core application scenarios are predefined: 1) training of surgical computer vision AI models; 2) validation of intraoperative surgical recognition algorithms; 3) surgical skill assessment and teaching research. An expert review panel consisting of at least 3 attending surgeons and 2 medical AI researchers independently evaluates whether the dataset has sufficient sample size, annotation completeness and video quality to support each scenario. Feasibility proportion is calculated as: (Number of scenarios the dataset is suitable for / Total predefined scenarios) × 100%.	After full construction of the surgical video dataset, scenario feasibility assessment will be finished within 6 months, assessed up to 6 months after dataset construction.
Accuracy percentage (%) of AI-based surgical procedure identification on annotated surgical videos Time Frame: After completion of data warehousing and annotation, AI surgical procedure identification accuracy testing will be conducted within 3 months, assessed up to 3 months post annotation completion.	After all surgical videos are imported into the data warehouse and manually annotated by experienced surgeons to generate gold-standard procedure labels, the surgical video analysis AI model automatically outputs predicted surgical procedure categories for each video clip. Each AI-predicted label is compared against the manual gold-standard annotation label. Identification accuracy is calculated by the formula: (Number of video clips with correctly predicted surgical procedures / Total number of tested video clips) × 100%.	After completion of data warehousing and annotation, AI surgical procedure identification accuracy testing will be conducted within 3 months, assessed up to 3 months post annotation completion.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Chinese Academy of Sciences

Collaborators

Beihang University

Beijing Digital Precision Medicine Technology Co., Ltd.

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

August 1, 2026

Primary Completion (Estimated)

July 31, 2031

Study Completion (Estimated)

July 31, 2032

Study Registration Dates

First Submitted

July 31, 2026

First Submitted That Met QC Criteria

August 5, 2026

First Posted (Actual)

August 7, 2026

Study Record Updates

Last Update Posted (Actual)