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Model Study on Cervical Cancer Screening Strategies and Risk Prediction

NCT06204133 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 1112846

Last updated 2024-07-22

No results posted yet for this study

Summary

By collecting non-image medical data of women undergoing cervical screening in multiple centers in China, including age, HPV infection status, HPV infection type, TCT results, and colposcopy biopsy pathology results, a multi-source heterogeneous cervical lesion collaborative research big data platform was established. Based on artificial intelligence (AI) machine learning, cervical lesion screening features are refined, a multi-modal cervical cancer intelligent screening prediction and risk triage model is constructed, and its clinical application value is preliminarily explored.

Conditions

  • Cervical Cancer Screening
  • Risk Assessment
  • Artificial Intelligence
  • Machine Learning

Interventions

OTHER

Artificial intelligence model building

Using non-image medical data of cervical lesions and clinical pathology results in different medical institutions, machine learning is adopted to establish multiple multi-modal cervical cancer intelligent screening prediction models. This method was used to analyze the prediction performance of the multi-modal cervical cancer intelligent screening prediction and risk triage model, and to evaluate and optimize the self-learning ability of the established multi-modal cervical cancer intelligent screening prediction model.

Sponsors & Collaborators

  • Fujian Maternity and Child Health Hospital

    lead OTHER

Principal Investigators

  • Pengming Sun · Fujian Maternal and Child Health Hospital

Eligibility

Min Age
25 Years
Max Age
64 Years
Sex
FEMALE
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2023-11-01
Primary Completion
2024-04-30
Completion
2024-06-30

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06204133 on ClinicalTrials.gov