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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT06204133
Other study ID # CSRM2304
Secondary ID
Status Recruiting
Phase
First received
Last updated
Start date November 1, 2023
Est. completion date June 30, 2024

Study information

Verified date April 2024
Source Fujian Maternity and Child Health Hospital
Contact Binhua Dong
Phone +8659187558732
Email dongbinhua86@fjmu.edu.cn
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial 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.


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.


Recruitment information / eligibility

Status Recruiting
Enrollment 1500000
Est. completion date June 30, 2024
Est. primary completion date May 31, 2024
Accepts healthy volunteers Accepts Healthy Volunteers
Gender Female
Age group 25 Years to 64 Years
Eligibility Inclusion Criteria: - Age 25-64 years old; - There was no history of precancerous lesions or cervical cancer; - No previous cervical surgery or cervical removal; Exclusion Criteria: - HPV test results are not available; - Pregnant or lactating women; - There is a serious immune system disease, and the disease is active;

Study Design


Intervention

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.

Locations

Country Name City State
China Shunde Women's and Children's Hospital of Guangdong Medical University Foshan Guangdong
China Fujian Maternity and Child Health Hospital Fuzhou Fujian
China Ningde maternal and child health hospital Ningde Fujian
China Shenzhen Maternal and Child Health Hospital Shenzhen Guangdong
China Hubei Maternal and Child Health Hospital Wuhan Hubei

Sponsors (1)

Lead Sponsor Collaborator
Fujian Maternity and Child Health Hospital

Country where clinical trial is conducted

China, 

Outcome

Type Measure Description Time frame Safety issue
Primary Cervical histopathology Cervical histopathological diagnosis within 8 weeks within 8 weeks,
Primary colposcopy Colposcopists use colposcopic equipment to investigate the occurrence of cervical and vaginal lesions within 8 weeks Percentage of patients diagnosed with cervical intraepithelial neoplasia of grade 3 (CIN3) or worse by cervical histopathological measurements within 8 weeks
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