ABSTRACT
Objective: To explore the application value of artificial intelligence-assisted diagnosis system for TBS report in cervical cancer screening. Methods: A total of 16 317 clinical samples and related data of cervical liquid-based thin-layer cell smears, which were obtained from July 2020 to September 2020, were collected from Southern Hospital, Guangzhou Huayin Medical Inspection Center, Shenzhen Bao'an People's Hospital(Group) and Changsha Yuan'an Biotechnology Co., Ltd. The TBS report artificial intelligence-assisted diagnosis system of cervical liquid-based thin-layer cytology jointly developed by Southern Medical University and Guangzhou F. Q. PATHOTECH Co., Ltd. based on deep learning convolution neural network was used to diagnose all clinical samples. The sensitivity,specificity and accuracy of both artificial intelligence-assisted diagnosis system and cytologists using artificial intelligence-assisted diagnosis system were analyzed based on the evaluation standard(2014 TBS). The time spent by the two methods was also compared. Results: The sensitivity of artificial intelligence-assisted diagnosis system in predicting cervical intraepithelial lesions and other lesions (including endometrial cells detected in women over 45 years old and infectious lesions) under different production methods, different cytoplasmic staining and different scanning instruments was 92.90% and 83.55% respectively, and the specificity of negative samples was 87.02%, while that of cytologists using artificial intelligence-assisted diagnosis system was 99.34%, 97.79% and 99.10%, respectively. Moreover, cytologists using artificial intelligence-assisted diagnosis system could save about 6 times of reading time than manual. Conclusions: Artificial intelligence-assisted diagnosis system for TBS report of cervical liquid-based thin-layer cytology has the advantages of high sensitivity, high specificity and strong generalization. Cytologists can significantly improve the accuracy and work efficiency of reading smears by using artificial intelligence-assisted diagnosis system.
Subject(s)
Uterine Cervical Neoplasms , Artificial Intelligence , Early Detection of Cancer , Female , Humans , Middle Aged , Sensitivity and Specificity , Uterine Cervical Neoplasms/diagnosis , Vaginal SmearsABSTRACT
The ultrastructure of the tegument of the early excysted P. westermani metacercariae was observed with transmission electron microscope. The tegument was fine and dense in texture, containing a lot of disk-like bodies and a few mitochondria. The outer-plasma membrane was clear and intact. After the metacercariae were incubated in rabbit anti-serum for 3 hours, metacercarial membrane reaction (MMR) appeared closely to the outer plasma membrane and the tegument was damaged. Indirect peroxidase-labelled immunohistochemical technique was used to identify the location of the MMR at ultrastructural level. The result showed that the antibody-antigen complex was localized on the tegumental surface of the worm. The data constituted the evidence that the MMR resulted from the antibody-antigen reaction and the tegumental surface was the reaction site for the MMR.
