Trials / Completed
CompletedNCT06204133
Model Study on Cervical Cancer Screening Strategies and Risk Prediction
- Status
- Completed
- Phase
- —
- Study type
- Observational
- Enrollment
- 1,112,846 (actual)
- Sponsor
- Fujian Maternity and Child Health Hospital · Academic / Other
- Sex
- Female
- Age
- 25 Years – 64 Years
- Healthy volunteers
- Accepted
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.
Detailed description
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. The effect of clinical application of the model was evaluated by internal data from Fujian Province and external data from several other regions in China.
Conditions
Interventions
| Type | Name | Description |
|---|---|---|
| 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. |
Timeline
- Start date
- 2023-11-01
- Primary completion
- 2024-04-30
- Completion
- 2024-06-30
- First posted
- 2024-01-12
- Last updated
- 2024-07-22
Locations
7 sites across 1 country: China
Source: ClinicalTrials.gov record NCT06204133. Inclusion in this directory is not an endorsement.