Background: Current automated cervical cytology screening systems still heavily depend on manipulation of glass slides. We developed a new system called CytoProcessorTM (DATEXIM, Caen, France), which increases sensitivity and takes advantage of virtual slide technology to simplify the workflow and save worker time. We used an approach based on artificial intelligence to identify abnormal cells among the tens of thousands in a cervical preparation. Objectives: We set out to compare the diagnostic sensitivity and specificity of CytoProcessorTM and the ThinPrep Imaging System (HOLOGIC, Marlborough, MA, USA). Methods: A representative population of 1,352 cases was selected from the routine workflow in a private laboratory. Diagnoses were established using the ThinPrep Imaging System and CytoProcessorTM. All discordances were resolved by a consensus committee. Results: Compared to the ThinPrep Imaging System, CytoProcessorTM significantly improves diagnostic sensitivity without compromising specificity. The sensitivity of detection of “atypical squamous cells of undetermined significance (ASC-US) and more severe” and “low-grade squamous intraepithelial lesion and more severe” was significantly higher using CytoProcessorTM. Considering that cases with a truth diagnosis of ASC-US or more severe required clinical follow-up, 1.5% of the cases (21/1,360) would have been missed if the CytoProcessorTM diagnosis had been used for clinical decision-making. In contrast, 4% of the cases (54/1,360) were missed when the ThinPrep Imaging System diagnosis was used for clinical decision-making. There were 2.6 times fewer false negatives using CytoProcessorTM. The CytoProcessorTM workflow was 1.5 times faster in terms of worker time. Conclusions: CytoProcessorTM is the first of a new generation of automated screening systems, demonstrating improved sensitivity and yielding significant gains in processing time. In addition, the fully digital nature of slide presentation in CytoProcessorTM allows the remote diagnosis of Papanicolaou tests for the first time.

Keywords:
Digital pathology, Artificial intelligence, Cervical screening, Papanicolaou test, Gynecologic cytology
© 2019 S. Karger AG, Basel
2019
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