Application of an artificial intelligence-assisted diagnosis system for cervical cell pathology images：a prospective diagnostic test study

XIE Ling-ling; YE Dong-dong; HE Gui; LIN Zhong-qiu; ZHOU Hui

doi:10.19538/j.fk2026020116

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Chinese Journal of Practical Gynecology and Obstetrics ›› 2026, Vol. 42 ›› Issue (2) : 212-217. DOI: 10.19538/j.fk2026020116

Application of an artificial intelligence-assisted diagnosis system for cervical cell pathology images：a prospective diagnostic test study

XIE Ling-ling ^1a ,
YE Dong-dong ^1a ,
HE Gui ^1b ,
LIN Zhong-qiu ^1a ,
ZHOU Hui ^1a^,²

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Abstract

Objective To assess the feasibility of an artificial intelligence （AI）-assisted diagnosis system for cervical cell pathtology images in diagnosing cervical lesions through a prospective diagnostic text study. Methods A total of 347 liquid-based cytology samples were collected from patients who visited the Department of Gynecology at Sun Yat-sen Memorial Hospital，Sun Yat-sen University between April 27，2022 and March 21，2023. The samples were independently analyzed by the AI system or with AI assistance in ThinPrep Bethesda System （TBS） classification. The results were compared with those obtained from the expert group and the pure researcher-independent reading group. The accuracy of the AI-assisted diagnosis model was evaluated by taking histopathological findings into account. Results Using the TBS diagnosis of the expert group as the reference standard，the highest consistency was observed between the AI-assisted reading group and the expert group，with a weighted Kappa value of 0.924. When biopsy or conization pathological results were used as the reference standard，the sensitivity of the AI-independent reading group and the AI-assisted reading group in diagnosing ≥LSIL and ≥HSIL diseases was higher than that of the pure researcher-independent reading group and the expert group （0.614 vs. 0.614 vs. 0.561 vs. 0.579；0.769 vs. 0.590 vs. 0.564 vs. 0.513）. The accuracy of the AI diagnosis system combined with HPV screening in detecting ≥HSIL lesions was 75.3%，which was higher than that of the pure researcher-independent reading group （67.1%） and the expert group （74.0%）. Conclusion The AI diagnostic system demonstrates high accuracy and stability in cervical TBS classification, and may possess superior predictive value for cervical lesions compared with manual diagnosis.

Key words

artificial intelligence / cervical cytology diagnosis system / cervical cancer screening

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XIE Ling-ling , YE Dong-dong , HE Gui , et al . Application of an artificial intelligence-assisted diagnosis system for cervical cell pathology images：a prospective diagnostic test study[J]. Chinese Journal of Practical Gynecology and Obstetrics. 2026, 42(2): 212-217 https://doi.org/10.19538/j.fk2026020116

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