Model Study on Cervical Cancer Screenin… | Clinical Trial

Key information

Conditions

Cervical Cancer Screening Artificial Intelligence Machine Learning Risk Assessment

Age range

25 year–64 year

Sex eligibility

Female

Study type

Observational

Primary location

Fujian Maternity and Child Health Hospital, Fuzhou, Fujian, China

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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;

Treatment and study plan

Artificial intelligence model building

Other

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.

Primary outcomes

Cervical histopathology

Time frame: within 8 weeks，

Cervical histopathological diagnosis within 8 weeks
colposcopy

Time frame: Percentage of patients diagnosed with cervical intraepithelial neoplasia of grade 3 (CIN3) or worse by cervical histopathological measurements within 8 weeks

Colposcopists use colposcopic equipment to investigate the occurrence of cervical and vaginal lesions within 8 weeks

Sponsors and collaborators

Lead sponsor

Fujian Maternity and Child Health Hospital

Other

Registry information

Important dates

Study start: 2023
Primary completion: 2024
Study completion: 2024
First posted: Jan 12, 2024
Registry last updated: Jul 22, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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Completed