In order to automate microscopic examinations of female urogenital samples, the “learning” of the “Vision Cyto STD” system was performed using the technology of image recognition based on convolutional neural networks and deep learning.
More than a thousand digital slides with urogenital material with different microscopic composition were evaluated during the learning process. As the dataset was replenished with new clinical examples, the accuracy of object recognition increased.
The use of artificial neural networks (ANN) for imaging and analysis of urogenital samples ensures high quality of examination performance due to the clarity of identification, accuracy of analysis and correctness of entering the examination results. Using of ANN speeds up the process of giving the results, allows to minimize the influence of the subjective factor, and also provides an opportunity to redistribute the workload of laboratory workers, without expanding the laboratory staff when the number of examinations to be performed increases.
A brief overview of the automated Vision Cyto STDs system is presented.
Nataliya A. Lipatova – Candidate of Medical Sciences, Doctor of Clinical Laboratory Diagnostics, Medical Director of LLC “Mobil Medical Lab”, Moscow, Russia. E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Snezhana V. Stolyarova – Doctor of Clinical Laboratory Diagnostics, CEO of LLC “Mobil Medical Lab”, Moscow, Russia. E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Vladislav A. Sapozhkov – Engineer of the analytical systems group of the service center LLC “Medica product”; Head of R&D Department, International Cytology School&Innovatory Medical School, Moscow, Russia. E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Anna S. Churilova – Head of the Artificial Intelligence Development Group of the Software Development Department LLC “Medica Product”, Perm, Russia. E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
1. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44. DOI:10.1038/nature14539
2. “Approval of the lists of harmful and (or) dangerous production factors and works, in the performance of which mandatory preliminary and periodic medical examinations (examinations) and the Procedure for conducting mandatory preliminary and periodic medical examinations (examinations) of workers engaged in heavy work and work in harmful and (or) dangerous working conditions” // Order of the Ministry of Health and Social Development of Russia from 12.04.2011 №302n (ed. from 18.05.2020) registered in the Ministry of Justice of Russia 21.10.2011 №22111 (In Russ).
3. “Approval of the Procedure of Medical Care in the Profile of Obstetrics and Gynecology (Except for the Use of Assisted Reproductive Technologies)” // Order №572n of November 1, 2012. (In Russ).
4. Federal Clinical Guidelines. Dermatovenerology 2015: Skin Diseases. Sexually transmitted infections. 5th ed, revised and supplemented. Moscow: Delovoy Express, 2016.
5. Abels E, Pantanowitz L, Aeffner F, et al. Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association. J Pathol. 2019;249(3):286–94. DOI: 10.1002/path.5331
6. Krizhevsky A, Sutskever I, Hinton G. ImageNet classification with deep convolutional neural networks. Proc. Advances in Neural Information Processing Systems. 2012(25):1090–8.
7. Landau MS, Pantanowitz L. Artificial intelligence in cytopathology: a review of the literature and overview of commercial landscape. J Am Soc Cytopathol. 2019;8(4):230–41. DOI: 10.1016/j.jasc.2019.03.003
8. Szegedy C, Toshev A, Erhan D. Deep neural networks for object detection. Advances in Neural Information Processing Systems. 2013.
9. Savicheva AM, Sokolovsky EV, Domeika M. Order of microscopic examination of smears from the urogenital tract: Methodological recommendations for treating physicians. Saint Petersburg: Izdatelstvo N-L; 2007. (In Russ).
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DOI: 10.14489/lcmp.2023.04.pp.004-010
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