Introduction: Recent studies have shown that digital cytology (DC) coupled with artificial intelligence (AI) algorithms is a valid approach to the diagnosis of cervico-vaginal lesions using liquid-based cytology (LBC). We evaluated the use of these methods for anal LBC specimens.
Methods: A series of 124 anal LBC slides previously diagnosed by conventional microscopy (CC) were reviewed with a DC/AI system that generated a gallery of images. Diagnoses based on the selected images, according to the 2014 Bethesda System for Reporting Cervical Cytology, were compared to CC.
Results: Overall, CC and DC/AI approaches detected a similar number of abnormal (ASC-US+) cases (63 and 62 cases, respectively). We observed an exact concordance between CC and DC in 70 (57.9%) cases, corresponding to a moderate agreement between the two approaches (κ = 0.41, p < 0.001). A moderate agreement (κ = 0.48, p < 0.001) was also found when positive cases were stratified into 'low-grade' (ASC-US, LSIL) and 'high-grade' lesions (ASC-H, HSIL). The DC/AI system detected more cases of higher severity (ASC-H, HSIL: 9 and 2 cases, respectively) than CC (3 cases classified as HSIL).
Conclusions: The number of ASC-US+ cases detected by both systems was similar. The DC/AI system detected more cases of higher severity compared to the CC.
Keywords: anal cytology; artificial intelligence; digital cytology.
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