ArticleResearch square2025
Closing the Automation Gap: Robust AI for Dual-Stain Cervical Cancer Screening Triage.
Article in Research square, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- AI in Cervical Cancer Cytology Diagnostics: A Narrative Review of Cutting-Edge Studies.Bioengineering (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors.
Funding
Abstract
Dual-stain cytology, using p16 and Ki67, is superior to conventional PAP cytology for triage of HPV-positive test results in cervical cancer screening. Its AI-based evaluation can remove subjectivity, improve performance and facilitate implementation. Using 5,722 dual-stain slides from population-based screening cohorts, we developed and validated Cytoreader-V2. In the SurePath Kaiser Implementation Study, Cytoreader-V2 achieved 87.2%/57.8% (sensitivity/specificity) compared to 89.9/52.6 (manual DS) and 85.8/41.9 (Pap cytology). In the Thin-Prep Biopsy Study, it reached 95.7/44.4 versus 89.4/35.0 (manual DS), and in anal DS cytology slides, 87.0/41.3 compared to 87.0/27.7 (manual). Robustness testing demonstrated significant stability across image transformations. Cytoreader-V2 improves specificity and reproducibility compared to manual dual-stain reading while maintaining high sensitivity. Its adaptability across populations with consistent performance makes it scalable for diverse clinical settings. Cytoreader-V2 can be a transformative tool in global cervical cancer screening as a critical AI applications in digital pathology.
Identifiers
What OpenQuestion holds
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.