BACKGROUND Cervical cytology screening is usually laborious with a heavy workload and poor diagnostic consistency. The authors have developed an artificial intelligence (AI) microscope that can provide onsite diagnostic assistance for cervical cytology screening in real time. METHODS A total of 2167 cervical cytology slides were selected from a cohort of 10,601 cases from Shenzhen Maternity and Child Healthcare Hospital, and the training data set consisted of 42,073 abnormal cervical epithelial cells. The recognition results of an AI technique were presented in a microscope eyepiece by an augmented reality technique. Potentially abnormal cells were highlighted with binary classification results in a 10× field of view (FOV) and with multiclassification results according to the Bethesda system in 20× and 40× FOVs. In addition, 486 slides were selected for the reader study to evaluate the performance of the AI microscope. RESULTS In the reader study, which compared manual reading with AI assistance, the sensitivities for the detection of low‐grade squamous intraepithelial lesions and high‐grade squamous intraepithelial lesions were significantly improved from 0.837 to 0.923 (P < .001) and from 0.830 to 0.917 (P < .01), respectively; the κ score for atypical squamous cells of undetermined significance (ASCUS) was improved from 0.581 to 0.637; the averaged pairwise κ of consistency for multiclassification was improved from 0.649 to 0.706; the averaged pairwise κ of consistency for binary classification was improved from 0.720 to 0.798; and the averaged pairwise κ of ASCUS was improved from 0.557 to 0.639. CONCLUSIONS The results of this study show that an AI microscope can provide real‐time assistance for cervical cytology screening and improve the efficiency and accuracy of cervical cytology diagnosis.

Endometrial carcinoma (EC) is the second major female genital malignancy. Genetic signatures may be an improved choice to predict the prognosis of EC patients. The relationship between pyroptosis and tumours has attracted much attention in recent years. Here, we constructed a new pyroptosis-related gene (PRG) signature for predicting the prognosis of EC. In this study, gene data and clinical information of EC patients were obtained from The Cancer Genome Atlas (TCGA). Following the identification of PRGs correlated with EC prognosis, we further investigate the bioinformatics functions of these PRGs by univariate Cox regression analysis and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Then, we used the least absolute contraction and selection operator (LASSO) regression and multiple Cox regression analysis to construct a new PRG signature that contains seven PRGs (NFKB1, EEF2K, CTSV, MDM2, GZMB, PANX1, and PTEN) and performed the Kaplan-Meier (K-M) analysis, receiver operating characteristic curve (ROC) analysis, and principal component analysis (PCA) to evaluate the prognostic value of our novel PRG signature. Finally, we assessed the correlations between pyroptosis and immune cells/checkpoints through the CIBERSORT tool and single-sample gene set enrichment analysis (ssGSEA). The result suggested that our signature was powerful in predicting EC prognosis and may play a part in assessing response to immunotherapy in EC patients. In conclusion, our study established a novel PRG signature for EC, which can be used as an effective prognostic marker in clinical practice in the future.