NEJM AI · Sep 24, 2026
Closing the Automation Gap in HPV-Based Cervical Cancer Screening: Independent External Validation of an AI Model for Dual-Stain Triage
By Lahrmann B, Keil A, Miranda Ruiz F et al.
BACKGROUND: Primary human papillomavirus (HPV) screening has enabled scalable molecular detection in cervical cancer prevention, but downstream triage of HPV-positive individuals remains dependent on manual p16/Ki-67 dual-stain (DS) cytology interpretation. Although DS is guideline-endorsed and biologically well-defined, scalable implementation is limited by reader-dependent variability and lack of standardized automation. METHODS: We developed Cytoreader-Fusion, a transparent, ensemble-based AI system for automated slide-level DS classification within HPV-based screening workflows that produces well-calibrated probabilistic outputs. The model was trained on 1919 cases and evaluated under blinded honest-broker oversight on 9006 cases across four independent cohorts, including a fully independent post-lock external validation cohort (EXVC; n=4996). All preprocessing steps, aggregation rules, and decision parameters were prespecified and irreversibly locked prior to validation. Performance was evaluated against independently ascertained histologically confirmed cervical intraepithelial neoplasia grade 3 or worse (CIN 3+) (or anal intraepithelial neoplasia grade 3 or worse [AIN 3+]) end points rather than cytologic concordance. A single unified classifier was applied across both ThinPrep and SurePath preparations without platform-specific retraining. RESULTS: Cytoreader-Fusion demonstrated stable and reproducible slide-level classification across imaging variability, preparation platforms, and clinical settings. Across blinded screening and referral cohorts, sensitivity and specificity were higher than atypical squamous cells of undetermined significance or worse (ASC-US+) Papanicolaou (Pap) cytology in HPV-based screening populations and comparable to or exceeded manual DS interpretation. In the fully independent post-lock cohort (n=4996), the locked system achieved a sensitivity of 88.3% (90% UI, 85.3%-91.3%) and a specificity of 54.4% (90% UI, 52.9%-55.9%) for detection of CIN 3+, without any model retraining, threshold tuning, or configuration adjustment after lock. Operating characteristics remained consistent across cohorts and preparation platforms. CONCLUSIONS: This study establishes a high-stringency validation framework for clinically deployable artificial intelligence in population-based cancer screening. By combining prespecified algorithm locking, independent blinded evaluation under honest-broker supervision, end point-based validation against histologically confirmed disease, and cross-platform generalization, Cytoreader-Fusion demonstrates robust and scalable automation of DS triage within HPV-based screening programs. (Funded by the U.S. National Cancer Institute and others.).