Prospective Collection and Registry Study of Multicenter, Multidisciplinary Surgical Minimally Invasive Videos (VISION)
5. august 2026 opdateret af: 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.
Studieoversigt
Status
Ikke rekrutterer endnu
Undersøgelsestype
Observationel
Tilmelding (Anslået)
2000
Kontakter og lokationer
Dette afsnit indeholder kontaktoplysninger for dem, der udfører undersøgelsen, og oplysninger om, hvor denne undersøgelse udføres.
Studiekontakt
- Navn: KUNSHAN HE
- Telefonnummer: +86 18500535530
- E-mail: hekunshan@buaa.edu.cn
Studiesteder
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Beijing, Kina
- Institute of Automation, Chinese Academy of Sciences
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Deltagelseskriterier
Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.
Berettigelseskriterier
Aldre berettiget til at studere
- Voksen
- Ældre voksen
Tager imod sunde frivillige
Ingen
Prøveudtagningsmetode
Ikke-sandsynlighedsprøve
Studiebefolkning
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.
Beskrivelse
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.
Studieplan
Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.
Hvordan er undersøgelsen tilrettelagt?
Design detaljer
Hvad måler undersøgelsen?
Primære resultatmål
Resultatmål |
Tidsramme |
|---|---|
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Completion rate of qualified intraoperative surgical imaging data
Tidsramme: Immediately after each surgery
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Immediately after each surgery
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Sekundære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
|---|---|---|
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Completeness rate of long-term postoperative clinical follow-up
Tidsramme: 3 months, 1 year, 3 years and 5 years after surgery
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3 months, 1 year, 3 years and 5 years after surgery
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Reusability rate of annotated key anatomical structures in videos
Tidsramme: From completion of data warehousing and annotation, the reusability rate will be analyzed within 3 months, assessed up to 3 months after annotation completion.
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From completion of data warehousing and annotation, the reusability rate will be analyzed within 3 months, assessed up to 3 months after annotation completion.
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Feasibility rate (%) of surgical video dataset applied in different clinical AI research scenarios
Tidsramme: 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.
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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.
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Accuracy percentage (%) of AI-based surgical procedure identification on annotated surgical videos
Tidsramme: 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.
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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.
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Samarbejdspartnere og efterforskere
Det er her, du vil finde personer og organisationer, der er involveret i denne undersøgelse.
Sponsor
Datoer for undersøgelser
Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.
Studer store datoer
Studiestart (Anslået)
1. august 2026
Primær færdiggørelse (Anslået)
31. juli 2031
Studieafslutning (Anslået)
31. juli 2032
Datoer for studieregistrering
Først indsendt
31. juli 2026
Først indsendt, der opfyldte QC-kriterier
5. august 2026
Først opslået (Faktiske)
7. august 2026
Opdateringer af undersøgelsesjournaler
Sidste opdatering sendt (Faktiske)
7. august 2026
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
5. august 2026
Sidst verificeret
1. juli 2026
Mere information
Begreber relateret til denne undersøgelse
Nøgleord
Andre undersøgelses-id-numre
- VISION
Lægemiddel- og udstyrsoplysninger, undersøgelsesdokumenter
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