New Release: Cytoreader V2 is now available! Read our latest paper: Preprint Link
Cytoreader provides a validated, end-to-end digital solution to increase efficiency and standardization in your laboratory.
For research and internal quality monitoring purposes only. Not CE-IVD. Not for clinical diagnostic use.
Seamless Integration
Hamamatsu Scanners
Validated By
Peer-Reviewed Research
In Daily Use At
Kaiser Permanente
Core Technology
Patented AI Algorithms
The Challenge & The Solution
Cervical cancer screening increasingly relies on primary HPV testing, which enables large-scale automation but lacks specificity. p16/Ki-67 dual-stain (DS) cytology is now widely used for triage due to its improved accuracy over Pap cytology—detecting more precancers while reducing unnecessary colposcopies. However, DS brings challenges: manual review is slow, staining varies, and workflows remain analog. Cytoreader tackles these issues using AI and digital technology.:
Enables full-slide review in about 10 seconds. The underlying AI algorithm has been shown to reach a performance level comparable to experienced human readers, freeing valuable expert time.
Provides on-slide AI controls that continuously assess staining performance and identify inconsistencies or artifacts early, ensuring reproducible and high-quality workflows.
Integrates dual-stain slides into a high-throughput digital workflow, including seamless connectivity with Hamamatsu whole slide scanners for ultimate scalability.
Core Platform Features
Efficiently assess the 30 most relevant dual-stain events.
Streamline case review with intuitive shortcut keys.
Utilize heatmaps to guide review of regions of interest.
Enable blinded analysis for training and quality assurance.
Facilitate tiered workflows for optimal QC.
Manage slide workflows, assign tasks, and monitor progress.
Implementation Models
For high-volume laboratories, Cytoreader is installed on-premise and fully integrated into your existing IT and digital pathology infrastructure.
For a rapid start with no capital investment, leverage our full-service model. Simply send us your slides, and we provide the results.
Key publications supporting our work and technology.
1. Clinical Evaluation of Human Papillomavirus Screening With p16/Ki-67 Dual Stain Triage in a Large Organized Cervical Cancer Screening Program.
Wentzensen, N., Clarke, M., Bremer, R., et al. (2019).
JAMA Internal Medicine, 179(7), 881-889. [DOI]
2. Performance of p16/Ki-67 immunostaining to detect cervical cancer precursors in a colposcopy referral population.
Wentzensen, N., Schwartz, L., Zuna, R., et al. (2012).
Clinical Cancer Research, 18(15), 4154-4162. [DOI]
3. Multiple biopsies and detection of cervical cancer precursors at colposcopy.
Wentzensen, N., Walker, J., Gold, M., et al. (2015).
Journal of Clinical Oncology, 33(1), 83-89. [DOI]
4. The Improving Risk Informed HPV Screening (IRIS) Study: Design and Baseline Characteristics.
Gage, J., Raine-Bennett, T., Schiffman, M., et al. (2022).
Cancer Epidemiology, Biomarkers & Prevention, 31(2), 486-492. [DOI]
5. Recommendations for Use of p16/Ki67 Dual Stain for Management of Individuals Testing Positive for Human Papillomavirus.
Clarke, M., Wentzensen, N., Perkins, R., et al. (2024).
Journal of Lower Genital Tract Disease.
6. STRIDES - STudying Risk to Improve DisparitiES in Cervical Cancer in Mississippi - Design and baseline results of a Statewide Cohort Study.
Risley, C., Stewart, M., Geisinger, K., et al. (2021).
Preventive Medicine. [DOI]
7. Human papillomavirus genotyping, human papillomavirus mRNA expression, and p16/Ki-67 cytology to detect anal cancer precursors in HIV-infected MSM.
Wentzensen, N., Follansbee, S., Borgonovo, S., et al. (2012).
AIDS, 26(13), 2185-2192. [PMID]
8. Automated evaluation of p16/Ki-67 dual-stain cytology as a biomarker for detection of anal precancer in men who have sex with men and are living with human immunodeficiency virus.
Cohen, C., Wentzensen, N., Lahrmann, B., et al. (2022).
Clinical Infectious Diseases, 75(3), 1565-1572. [DOI]
9. Accuracy and Efficiency of Deep-Learning–Based Automation of Dual Stain Cytology in Cervical Cancer Screening.
Wentzensen N, Lahrmann B, Clarke MA, et al. (2020).
J Natl Cancer Inst, djaa066. [DOI]
10. Closing the Automation Gap: Robust AI for Dual-Stain Cervical Cancer Screening Triage.
Lahrmann B, Keil A, Miranda-Ruiz F, et al. (2025, March 3).
PREPRINT (Version 1) Research Square. [DOI]