Cervical cytology screening facilitated by an artificial intelligence microscope: A preliminary study. Issue 9 (7th April 2021)

• Record Type: Journal Article
• Authors: Tang, Hong‐Ping; Cai, De; Kong, Yan‐Qing; Ye, Hu; Ma, Zhao‐Xuan; Lv, Huai‐Sheng; Tuo, Lin‐Rong; Pan, Qin‐Jing; Liu, Zhi‐Hua; Han, Xiao
• Is Part Of: Cancer cytopathology. Volume 129:Issue 9(2021)
• Page Start: 693
• Page End: 700
• Publication Date: 2021-04-07
• Subjects: artificial intelligence -- cervical cancer screening -- cervical cytology -- microscope; Cancer -- Cytopathology -- Periodicals; Pathology, Cellular -- Periodicals; Cytology -- Technique -- Periodicals; 611.01815
• DOI: 10.1002/cncy.22425
• Languages: English
• ISSNs: 1934-662X

Abstract

Abstract : 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. Abstract : In this study, an artificial intelligence microscope has been developed that can provide real‐time assistance for cervical cytology screening. This artificial intelligence microscope can improve the efficiency and accuracy of cervical cytology diagnosis.