AI-assisted system improves the work efficiency of cytologists via excluding cytology-negative slides and accelerating the slide interpretation

Given the shortage of cytologists, women in low-resource regions had inequitable access to cervical cytology which plays an pivotal role in cervical cancer screening. Emerging studies indicated the potential of AI-assisted system in promoting the implementation of cytology in resource-limited settings. However, there is a deficiency in evaluating the aid of AI in the improvement of cytologists' work efficiency. This study aimed to evaluate the feasibility of AI in excluding cytology-negative slides and improve the efficiency of slide interpretation. Well-annotated slides were included to develop the classification model that was applied to classify slides in the validation group. Nearly 70% of validation slides were reported as negative by the AI system, and none of these slides were diagnosed as high-grade lesions by expert cytologists. With the aid of AI system, the average of interpretation time for each slide decreased from 3 minutes to 30 seconds. These findings suggested the potential of AI-assisted system in accelerating slide interpretation in the large-scale cervical cancer screening.