期刊
GYNECOLOGIC ONCOLOGY
卷 159, 期 1, 页码 171-178出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ygyno.2020.07.099
关键词
Cervical cancer screening; Artificial intelligence; Cytology; Cervical intraepithelial neoplasia; Early detection
资金
- Association of Maternal and Child Health Studies [2017AMCHS006]
- National Natural Science Foundation of China [81903328]
Objective. Artificial intelligence (AI) could automatedly detect abnormalities in digital cytological images, however, the effect in cervical cancer screening is inconclusive. We aim to evaluate the performance of AI-assisted cytology for the detection of histologically cervical intraepithelial lesions (CIN) or cancer. Methods. We trained a supervised deep learning algorithm based on 188,542 digital cytological images. Between Mar 13, 2017, and Oct 20, 2018, 2145 referral women from organized screening were enrolled in a multicenter, clinical-based, observational study. Cervical specimen was sampled to generate two liquid-based slides: one random slide was allocated to AI-assisted reading, and the other to manual reading conducted by skilled cytologists from senior hospital and cytology doctors from primary hospitals. HPV testing and colposcopy-directed biopsy was performed, and histological result was regarded as reference. We calculated the relative sensitivity and relative specificity of Al-assisted reading compared to manual reading for CIN2+. This trial was registered, number ChiCTR2000034131. Results. In the referral population, Al-assisted reading detected 92.6% of CIN 2 and 96.1% of CIN 3+, significantly higher than or similar to manual reading. AI-assisted reading had equivalent sensitivity (relative sensitivity 1.01, 95%CI, 0.97-1.05) and higher specificity (relative specificity 1.26, 1.20-1.32) compared to skilled cytologists; whereas higher sensitivity (1.12, 1.05-1.20) and specificity (1.36, 1.25-1.48) compared to cytology doctors. In HPV-positive women, AI-assisted reading improved specificity for CIN1 or less at no expense of reduction of sensitivity compared to manual reading. Conclusions. AI-assisted cytology may contribute to the primary cytology screening or triage. Further studies are needed in general population. (C) 2020 Published by Elsevier Inc.
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