ArticleNature communications2025
Deep learning enabled liquid-based cytology model for cervical precancer and cancer detection.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- A five-phase evaluation framework for diagnostic and predictive medical artificial intelligence.NPJ digital medicine · 2026Article
- Accurate Segmentation of Overlapping Cervical Cells Using an Optimized Deep Learning Framework for Cytology Screening.Diagnostics (Basel, Switzerland) · 2026Article
- Deep learning-based cervical cancer T-staging using MRI: multi-structure segmentation and classification.BMC medical imaging · 2026Article
- Artificial Intelligence in Cervical Cytology: Opportunities and Limitations in Screening, Triage, and Diagnostic Support.Diagnostics (Basel, Switzerland) · 2026Review
- A cell comparative multiple instance learning network guided by image quality assessment for cervical whole slide image classification.iScience · 2026Article
- Artificial Intelligence in Oncology: A Comprehensive Cross-Cancer Translational Readiness Analysis Across 18 Malignancies.Cancers · 2026Review
- Explainable artificial intelligence with pyramid vision transformer model for multi-class malignant cell classification on cytology slides.Scientific reports · 2026Article
- Article
- Development and interpretability of an XGBoost model to predict high grade cervical intraepithelial neoplasia.BMC medical informatics and decision making · 2026Article
- Artificial intelligence for colposcopic and cytological image analysis in early cervical cancer detection.iScience · 2026Review
- Recent advancements in the application of artificial intelligence-based approaches for screening, diagnosis, prognosis and treatment of cervical cancer.Oncology reviews · 2026Review
- Artificial intelligence for rapid on-site evaluation of lymph node fine-needle aspiration: improving diagnostic efficiency and accuracy.Frontiers in medicine · 2026Article
- Artificial intelligence-assisted rapid on-site evaluation in liver biopsy: a diagnostic accuracy study.Frontiers in oncology · 2026Article
- A quantitative study of pathologists' perceptions towards artificial intelligence-assisted diagnostic system.PLOS digital health · 2025Article
- PAX1/SOX1 gene methylation as a detection and triage method for triage of high risk HPV-Positive women in cervical cancer screening.Gynecologic oncology reports · 2025Article
- AI in Cervical Cancer Cytology Diagnostics: A Narrative Review of Cutting-Edge Studies.Bioengineering (Basel, Switzerland) · 2025Review
- The diagnostic of PAX1 gene methylation in cervical lesions and its role in the triage of non-16/18 HR-HPV positive.Frontiers in immunology · 2025Article
- A Systematic Review of the Application of Artificial Intelligence in Colposcopy: Diagnostic Accuracy for Cervical Intraepithelial Neoplasia and Cervical Cancer.Clinical Medicine Insights. Oncology · 2025Review
Corrections and comments
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Authors and funding
23 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.