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

Xue, Peng and Xu, Hai‐Miao and Tang, Hong‐Ping and Wu, Wen‐Qing and Seery, Samuel and Han, Xiao and Ye, Hu and Jiang, Yu and Qiao, You‐Lin (2023) Assessing artificial intelligence enabled liquid‐based cytology for triaging HPV ‐positive women: a population‐based cross‐sectional study. Acta Obstetricia et Gynecologica Scandinavica, 102 (8). pp. 1026-1033. ISSN 0001-6349

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Abstract

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 < 0.001), it was significantly higher than cytologists at detecting CIN2+ (51.33% vs 40.93%, P < 0.001). AI‐LBC reduced referrals to colposcopy by approximately 10%, compared with cytologists (51.53% vs 60.94%, P = 0.003). Similar patterns were also observed for CIN3+. Conclusions: AI‐LBC has equivalent sensitivity and higher specificity compared with cytologists, with more efficient colposcopy referrals for HPV‐positive women. AI‐LBC could be particularly useful in regions where experienced cytologists are few in number. Further investigations are needed to determine triaging performance through prospective designs.

Item Type:
Journal Article
Journal or Publication Title:
Acta Obstetricia et Gynecologica Scandinavica
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2700/2729
Subjects:
?? cytologycervical cancer screeningartificial intelligencehpv triageobstetrics and gynaecology ??
ID Code:
196236
Deposited By:
Deposited On:
06 Jul 2023 09:45
Refereed?:
Yes
Published?:
Published
Last Modified:
17 Sep 2024 13:10