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

5 août 2026 mis à jour par: 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.

Aperçu de l'étude

Statut

Pas encore de recrutement

Les conditions

Type d'étude

Observationnel

Inscription (Estimé)

2000

Contacts et emplacements

Cette section fournit les coordonnées de ceux qui mènent l'étude et des informations sur le lieu où cette étude est menée.

Coordonnées de l'étude

Nom: KUNSHAN HE
Numéro de téléphone: +86 18500535530
E-mail: hekunshan@buaa.edu.cn

Lieux d'étude

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

Critères de participation

Les chercheurs recherchent des personnes qui correspondent à une certaine description, appelée critères d'éligibilité. Certains exemples de ces critères sont l'état de santé général d'une personne ou des traitements antérieurs.

Critère d'éligibilité

Âges éligibles pour étudier

Adulte
Adulte plus âgé

Accepte les volontaires sains

Non

Méthode d'échantillonnage

Échantillon non probabiliste

Population étudiée

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.

La 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.

Plan d'étude

Cette section fournit des détails sur le plan d'étude, y compris la façon dont l'étude est conçue et ce que l'étude mesure.

Comment l'étude est-elle conçue ?

Détails de conception

Que mesure l'étude ?

Principaux critères de jugement

Mesure des résultats	Délai
Completion rate of qualified intraoperative surgical imaging data Délai: Immediately after each surgery	Immediately after each surgery

Mesures de résultats secondaires

Mesure des résultats	Description de la mesure	Délai
Completeness rate of long-term postoperative clinical follow-up Délai: 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 Délai: 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 Délai: 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 Délai: 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.

Collaborateurs et enquêteurs

C'est ici que vous trouverez les personnes et les organisations impliquées dans cette étude.

Parrainer

Chinese Academy of Sciences

Collaborateurs

Beihang University

Beijing Digital Precision Medicine Technology Co., Ltd.

Dates d'enregistrement des études

Ces dates suivent la progression des dossiers d'étude et des soumissions de résultats sommaires à ClinicalTrials.gov. Les dossiers d'étude et les résultats rapportés sont examinés par la Bibliothèque nationale de médecine (NLM) pour s'assurer qu'ils répondent à des normes de contrôle de qualité spécifiques avant d'être publiés sur le site Web public.

Dates principales de l'étude

Début de l'étude (Estimé)

1 août 2026

Achèvement primaire (Estimé)

31 juillet 2031

Achèvement de l'étude (Estimé)

31 juillet 2032

Dates d'inscription aux études

Première soumission

31 juillet 2026

Première soumission répondant aux critères de contrôle qualité

5 août 2026

Première publication (Réel)

7 août 2026

Mises à jour des dossiers d'étude

Dernière mise à jour publiée (Réel)