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Deep learning enabled liquid-based cytology model for cervical precancer and cancer detection
Deep learning enabled liquid-based cytology model for cervical precancer and cancer detection
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Deep learning enabled liquid-based cytology model for cervical precancer and cancer detection
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Deep learning enabled liquid-based cytology model for cervical precancer and cancer detection
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Deep learning enabled liquid-based cytology model for cervical precancer and cancer detection
Deep learning enabled liquid-based cytology model for cervical precancer and cancer detection
Journal Article

Deep learning enabled liquid-based cytology model for cervical precancer and cancer detection

2025
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Overview
Deep learning (DL) enabled liquid-based cytology has potential for cervical cancer screening or triage. Here, we develop a DL model using whole cytology slides from 17,397 women and test it on 10,826 additional cases through a three-stage process. The DL model achieves robust performance across nine hospitals. In a multi-reader, multi-case study, it outperforms cytopathologists’ sensitivity by 9%. Reading time significantly decreases with DL assistance (218s vs 30s; p  < 0.0001). In community-based organized screening, the DL model’s sensitivity matches that of senior cytopathologists (0.878 vs 0.854; p  > 0.999), yet it has reduced specificity (0.831 vs 0.901; p  < 0.0001). Notably, hospital-based opportunistic screening shows that junior cytopathologists with DL assistance significantly improve both their sensitivity and specificity (0.857 vs 0.657, 0.840 vs 0.737; both p  < 0.0001). When triaging human papillomavirus-positive cases, DL assistance exhibits better performance than junior cytopathologists alone. These findings support using the DL model as an assistance tool in cervical screening and case triage. Cytology is commonly used for cervical cancer screening or triage. Here, the authors used deep learning to develop a liquid-based cytology screening approach to detect cervical cancer in both community and hospital-based settings.