Introduction To explore the effectiveness of an image recognition system based on artificial intelligence (AI) in diagnosing benign and malignant endometrial cell clumps.

Methods Endometrial cytological specimens from the First Affiliated Hospital of Xi’an Jiaotong University and Xi’an Daxing Hospital from August 2021 to February 2023 were selected, and histopathology was used as the gold standard. The sensitivity, specificity, positive predictive value, negative predictive value and accuracy of AI image recognition system (AI diagnosis) and professional pathologists’ manual diagnosis (manual diagnosis) of benign and malignant endometrial cell clumps were compared and analyzed.

Results Among the 126 patients included in the analysis, the overall coincidence rate of AI diagnosis and histological diagnosis was 92.1% (116/126), which was highly consistent with histopathological results (Kappa=0.841); the overall coincidence rate of manual diagnosis and histological diagnosis was 94.4% (119/126), which was highly consistent with histopathological results (Kappa=0.889).There was no statistically significant difference between AI diagnosis and manual diagnosis methods (χ²= 0.568, P=0.451). The sensitivity, specificity, positive predictive value, and negative predictive value of AI diagnosis were 91.8%, 92.3%, 91.8%, and 92.3%, respectively. There were 126 cytology sections, each of which required 6.67 minutes for manual diagnosis and 5.00 minutes for AI diagnosis.

Conclusion/Implications The AI image recognition system has high diagnostic accuracy, sensitivity and specificity, which is equivalent to the manual diagnosis level of professional pathologists, and this system has application value in the diagnosis of benign and malignant endometrial cell clumps.