Assessing artificial intelligence enabled liquid‐based cytology for triaging HPV‐positive women: a population‐based cross‐sectional study

Introduction Cytology‐based triaging is commonly used to manage the care of women with positive human papillomavirus (HPV) results, but it suffers from subjectivity and a lack of sensitivity and reproducibility. The diagnostic performance of an artificial intelligence‐enabled liquid‐based cytology (AI‐LBC) triage approach remains unclear. Here, we compared the clinical performance of AI‐LBC, human cytologists and HPV16/18 genotyping at triaging HPV‐positive women. Material and methods HPV‐positive women were triaged using AI‐LBC, human cytologists and HPV16/18 genotyping. Histologically confirmed cervical intraepithelial neoplasia grade 2/3 or higher (CIN2+/CIN3+) were accepted as thresholds for clinical performance assessments. Results Of the 3514 women included, 13.9% (n = 489) were HPV‐positive. The sensitivity of AI‐LBC was comparable to that of cytologists (86.49% vs 83.78%, P = 0.744) but substantially higher than HPV16/18 typing at detecting CIN2+ (86.49% vs 54.05%, P = 0.002). While the specificity of AI‐LBC was significantly lower than HPV16/18 typing (51.33% vs 87.17%, P