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

N/ANot Yet RecruitingNCT07752862

Chinese Academy of Sciences2,000 enrolled

Overview

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 Type

OBSERVATIONAL

Enrollment

2,000

Conditions

Surgical Neoplasms Abdominal Surgical Diseases Pelvic Surgical Diseases Urologic Surgical Diseases Musculoskeletal Surgical Diseases

Eligibility

Sex: ALLMin age: 18 YearsMax age: 65 Years

Medical Language ↔ Plain English

Inclusion Criteria: 1. Patients aged ≥ 18 years old hospitalized to receive minimally invasive endoscopic, laparoscopic or robotic surgical treatment for diseases of various body systems; 2. Complete full-length intraoperative surgical videos can be recorded during operation, with complete medical records and preoperative imaging data; 3. 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: 1. Minors under 18 years of age; 2. Patients with incomplete intraoperative videos or missing clinical imaging documents; 3. Patients with consciousness disturbance or mental disorders who cannot sign informed consent independently; 4. Subjects who refuse to participate in the study and disapprove the use of their medical data for research; 5. Patients who are predicted to be unavailable for long-term postoperative follow-up.

Outcomes

Primary Outcomes

Completion rate of qualified intraoperative surgical imaging data

Time frame: Immediately after each surgery

Secondary Outcomes

Completeness rate of long-term postoperative clinical follow-up

Time frame: 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.

Feasibility rate (%) of surgical video dataset applied in different clinical AI research scenarios

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%.

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.

Accuracy percentage (%) of AI-based surgical procedure identification on annotated surgical videos

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%.

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.