Related Experiment Video
Updated: Sep 8, 2025
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
Published on: July 11, 2025
Deep Learning-Assisted System Improves Practical Effects in Cervical Cytopathology Diagnosis: A Comparative Study of
Zichen Ye1, Peiyu Zhang2, Ronggan Wei3
1School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
None:
Deep learning (DL) has significantly improved the diagnostic accuracy and efficiency of cytopathologists. However, current DL-assisted reading modes have yet to be fully evaluated, and there is limited evidence regarding cytopathologists' preferences and experiences. This study employs a randomized, controlled, 4-way crossover design to assess the effectiveness of 4 distinct cytopatholog reading modes. This study included retrospectively collected 1620 cervical slides between 2021 and 2022. These slides were read by 108 certified cytopathologists with varying expertise using the 4 reading modes: unassisted, concurrent, second, and triage mode. A questionnaire survey was conducted to gather the cytopathologists' adoption of each mode, including mode score and their confidence and preferences. Compared with unassisted, all DL-assisted modes improved the cytopathologists' diagnostic performance. The unassisted mode had a sensitivity of 66.2% and a specificity of 72.5%. The second, concurrent, and triage modes all improved on these metrics: sensitivity increased by 20.2%, 17.3%, and 16.7%, respectively, whereas specificity increased by 13.2%, 10.8%, and 22.3%, respectively. The median reading time per slide was prolonged in second mode from 200 to 235 seconds but substantially reduced to 130 seconds in triage mode, and 53 seconds in concurrent mode (P < .0001 for all). Moreover, the questionnaire survey showed that >90% of cytopathologists agreed that the DL-assisted mode was useful in improving diagnostic confidence, and they preferred the concurrent mode, whereas the triage mode achieved the highest performance in the mode score. The second mode stands out for its heightened sensitivity, the triage mode boasts superior specificity, and the concurrent mode leads in efficiency when assisting cytopathologists. These findings provide useful information for choosing appropriate DL-assisted modes in cytopathological practice.
