Assessment of Efficacy and Accuracy of Cervical Cytology Screening with Artificial Intelligence Assistive System

The role of Artificial intelligence (AI) in pathology is one that offers many exciting new possibilities for improving patient care. This study contributes to this development by identifying the viability of AICyte Assistive System for cervical screening, and to investigate the utility of the system in assisting with workflow and diagnostic capability. In this study, a novel scanner was developed using a Ruiqian WSI-2400, trademarked AICyte Assistive system, to create AI-generated gallery of the most diagnostically relevant images, objects of interest (OOI), and provide categorical assessment, according to Bethesda category, for cervical ThinPrep Pap slides. For validation purposes, two pathologists reviewed OOIs from 32,451 cases of ThinPrep Paps independently, and their interpretations were correlated with the original ThinPrep interpretations (OTPI). The analysis was focused on the comparison of reporting rates, correlation between cytological results and histological follow-up findings, and the assessment of independent AICyte screening utility. Pathologists using the AICyte system had a mean reading time of 55.14 seconds for the first 3,000 cases trending down to 12.90 seconds in the last 6,000 cases. Overall average reading time was 22.23 seconds per case as compared to a manual reading time approximation of 180 seconds. Usage of AICyte compared to OTPI had similar sensitivity (97.89% vs 97.89%) and a statistically significant increase in specificity (16.19% vs 6.77%). When AICyte was run alone at a 50% negative cut-off value, it was able to read slides with a sensitivity of 99.30% and specificity of 9.87%. When AICyte was run independently at this cut-off value, no sole case of HSIL/SCC squamous lesion was missed. AICyte can provide a potential tool to help pathologists in both diagnostic capability and efficiency, which remained reliable as compared to baseline standard. Also unique for AICyte is the development of a negative cutoff value for which AICyte can categorize cases as “not needed for review” to triage cases and lower pathologist workload. This is the largest case number study that pathologists reviewed OOI with AI assistive system. The study demonstrates that AI assistive system can be broadly applied for cervical cancer screening.