ArticleJournal of the National Cancer Institute2021
Accuracy and Efficiency of Deep-Learning-Based Automation of Dual Stain Cytology in Cervical Cancer Screening.
Article in Journal of the National Cancer Institute, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers.
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79 citing papers in PubMed, 160 citations in OpenAlex.
- Development and Deployment of DeepSeek-Based Applications in Healthcare: A Chinese Perspective.Health care science · 2026Article
- Implementation and impact of primary HPV testing in cervical cancer screening: Historical perspectives and institutional experience at a large academic medical center.Cancer cytopathology · 2026Review
- How to evaluate and select cervical screening and triage tests in vaccinated populations.Journal of the National Cancer Institute. Monographs · 2026Article
- Urban-rural disparities in HPV prevalence and genotype distribution: a large-scale cervical cancer screening study in Xi'an, China.BMC infectious diseases · 2026Article
- Artificial Intelligence in Cervical Cytology: Opportunities and Limitations in Screening, Triage, and Diagnostic Support.Diagnostics (Basel, Switzerland) · 2026Review
- Article
- Long-Term Prospective Cohort Study of Cervical Cancer Screening Using Triage of Women Who Are Human Papillomavirus-Positive With Dual Stain and Human Papillomavirus Genotyping.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2026Observational
- Review
- Artificial Intelligence in Obstetrics and Gynecology Nursing: Clinical, Educational, and Ethical Perspectives.Cureus · 2026Review
- Development and interpretability of an XGBoost model to predict high grade cervical intraepithelial neoplasia.BMC medical informatics and decision making · 2026Article
- A comprehensive study on cervical cancer diagnosis using deep learning and artificial intelligence techniques.Frontiers in artificial intelligence · 2026Review
- AI-driven radiogenomics in gynecologic oncology: from radiological digital biopsy to a new paradigm in precision therapy.Frontiers in oncology · 2026Review
- Progress in the application research of cervical cancer screening developed by artificial intelligence in large populations.Discover oncology · 2025Review
- Machine and Deep Learning for the Diagnosis, Prognosis, and Treatment of Cervical Cancer: A Scoping Review.Diagnostics (Basel, Switzerland) · 2025Review
- p16/ki-67 dual stain triage of individuals positive for HPV to detect cervical precancerous lesions.International journal of cancer · 2025Review
- Bringing AI to Clinicians: Simplifying Pleural Effusion Cytology Diagnosis with User-Friendly Models.Diagnostics (Basel, Switzerland) · 2025Article
- Deep learning enabled liquid-based cytology model for cervical precancer and cancer detection.Nature communications · 2025Article
- Closing the Automation Gap: Robust AI for Dual-Stain Cervical Cancer Screening Triage.Research square · 2025Article
- Generalizable deep neural networks for image quality classification of cervical images.Scientific reports · 2025Article
- A histopathology-based artificial intelligence system assisting the screening of genetic alteration in intrahepatic cholangiocarcinoma.British journal of cancer · 2025Article
19 more citing papers are in PubMed but not listed here.
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Authors and funding
18 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the advent of primary human papillomavirus testing followed by cytology for cervical cancer screening, visual interpretation of cytology slides remains the last subjective analysis step and suffers from low sensitivity and reproducibility.
methodsWe developed a cloud-based whole-slide imaging platform with a deep-learning classifier for p16/Ki-67 dual-stained (DS) slides trained on biopsy-based gold standards. We compared it with conventional Pap and manual DS in 3 epidemiological studies of cervical and anal precancers from Kaiser Permanente Northern California and the University of Oklahoma comprising 4253 patients. All statistical tests were 2-sided.
resultsIn independent validation at Kaiser Permanente Northern California, artificial intelligence (AI)-based DS had lower positivity than cytology (P < .001) and manual DS (P < .001) with equal sensitivity and substantially higher specificity compared with both Pap (P < .001) and manual DS (P < .001), respectively. Compared with Pap, AI-based DS reduced referral to colposcopy by one-third (41.9% vs 60.1%, P < .001). At a higher cutoff, AI-based DS had similar performance to high-grade squamous intraepithelial lesions cytology, indicating a risk high enough to allow for immediate treatment. The classifier was robust, showing comparable performance in 2 cytology systems and in anal cytology.
conclusionsAutomated DS evaluation removes the remaining subjective component from cervical cancer screening and delivers consistent quality for providers and patients. Moving from Pap to automated DS substantially reduces the number of colposcopies and also achieves excellent performance in a simulated fully vaccinated population. Through cloud-based implementation, this approach is globally accessible. Our results demonstrate that AI not only provides automation and objectivity but also delivers a substantial benefit for women by reduction of unnecessary colposcopies.
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