Comprehensive Assessment of Artificial Intelligence as a Stand-Alone Tool for Cervical Cancer Screening Using a 50%...

An emerging application of artificial intelligence (AI) in cervical cytology is its use as an independent screener for triaging cases that do not require human review. This study aimed to assess the possibility of clinical practice.

Artificial Intelligence-Assistive Cytology Diagnostic System (AICyte) alone with a 50% negative cutoff is evaluated with detailed analysis of any AICyte-negative cases originally diagnosed as atypical squamous cell of undetermined significance or worse (ASC-US+/AI-), including re-examination by 3 pathologists (X.Z., X.B., Y.L.) with and without AICyte assistance and follow-up histological correlation.

Of 80,899 Pap tests, only 390 (0.48%) were ASC-US+/AI-. Of these, 379 (97.18%) were originally diagnosed as ASC-US with no cases of high-grade intraepithelial lesion or squamous cell carcinoma. AICyte alone demonstrated 92.11% sensitivity and 98.98% negative predictive value for an original ASC-US+ diagnosis, which increased to 99.44% sensitivity and 99.97% negative predictive value for detecting positive low-grade squamous intraepithelial lesions. Upon review of ASC-US+/AI- cases using microscopy by 3 pathologists, negative for intraepithelial lesion or malignancy (NILM) was interpreted in 46.41%, 40.77%, and 47.95% of cases, and ASC-US in 49.74%, 52.31%, and 46.67% of cases. Review of the cases using AICyte assistance showed NILM reporting rates of 79.74%, 68.46%, and 58.97%. None of the AICyte-negative cases reached consensus among the 3 pathologists for classification as high-grade intraepithelial lesion, atypical squamous cells-cannot exclude high-grade squamous intraepithelial lesion (ASC-H) using either method, indicating no obvious abnormal Pap cases in the AICyte-negative group, aside from the ASC-US, rare low-grade squamous intraepithelial lesion categories. A total of 151 cases (38.72%) were interpreted as NILM by both methods. Of 98 ASC-US+/AI- cases with histological follow-up, 2 cases showed cervical intraepithelial neoplasia 2 with mixed Pap cytological diagnoses of ASC-US by original diagnosis, NILM by microscopy, and 1 ASC-US/1 NILM using AICyte assistance. One case of endometrial carcinoma was noted in the follow-up, which was classified as atypical glandular cells in the original, consensus interpretation in both microscopy and AICyte assistance. For all 3 pathologists, reading times were significantly lower using AICyte assistance compared with microscopy.

AICyte can safely function as an independent screener to triage cervical cytology cases not needing further review, reducing the workload by half. It can also function as an assistive tool to improve the efficiency and accuracy of cervical cytology interpretation.