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.

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.

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;

For women aged 25-64 years who undergo cervical cancer screening, all women use HR-HPV testing as a primary screening strategy.