This article discusses the possibilities of application of artificial neural networks to solve problems of increasing the diagnostic outcomes in clinical laboratory examination. High diagnostic sensitivity (96 %) and diagnostic accuracy (89.5 %) of the results were shown on a large amount of cellular material digitized by artificial intelligence microscopy automation system like the Vision Cyto Pap.
The high resolution and sharpness of digital slides, the mode of viewing objects (cells) in the gallery, quick access to the results of preclassification, all of these factors together allow to reduce turnearound time in more than 2.5 times reducing disadvantages of the microscopy.
Application of artificial neural networks does not substitute a doctor’s skills. The role in validation of reports eligible only for cytopathologist. This concept indicates a carefully approach for staff working with a microscope, respectful attitude to them professional skills, and highlights a personalized approach to patients..
V. 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.
O. N. Budadin – Doctor of Technical Sciences, Professor, Laureate of the State Prize of the Russian Federation and the Prize of the Government of the Russian Federation, Researcher in Chief of the Central Research Institute of Special Engineering, Khot’kovo, Moscow Region, Russia. E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
A. 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.
B. F. Falkov – Director of Software Development, LLC ‘Medica Product’, Perm, Russia. E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Zh. Yu. Sapozhkova – MD, PhD, International Cytology School, Head, Senior Lecturer, Moscow Russia; Privat Medical Centre of Podolsk, Moscow Region, Head of Clinical Lab, doctor/cytologist, Podolsk, Moscow region, Russia. E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
1. Abramova EV, Budadin ON, Lebedev OV. Primenenie nejronnyh setej v teplovom nerazrushayushchem kontrole. Military Enginery. Issue 15. Composite non-metallic. materials in mechanical engineering. 2010;1:60-67. (In Russ).
2. Sozykin AV. Obzor metodov obucheniya glubokih nejronnyh setej. Vestnik YUUrGU. Seriya Vychislitel'naya matematika i informatika. 2017;6(3):28-59.
3. Abramova EV. Optimizaciya diagnosticheskih sistem teplovogo kontrolya [dissertation]. Moscow; 2011. Available at: https://new-disser.ru/_avtoreferats/01005084775.pdf. Accessed: 15 June 2021. (In Russ).
4. Semenov UA. Elektronnaya pressa i nejronnye seti. Moscow: ITEF; 1994. (In Russ).
5. Anapolskij LU, et al. Reshenie linejnogo algebraicheskogo uravneniya s pomoshchyu nejronnoj seti Hopfilda. Journal of Instrument Engineering. 1994;37(3-4):51-56. (In Russ).
6. Barshdorf D. Nejronnye seti i nechetkaya logika. Novye koncepcii dlya tekhnicheskoj diagnostiki neispravnostej. Instruments and Systems: Monitoring, Control, and Diagnostics. 1996;2:48-53. (In Russ)
7. Yudashkin AA. Bifurkacii stacionarnyh reshenij v sinergeticheskoj nejronnoj seti i upravlenie raspoznavaniem obrazov. Automation and Remote Control. 1996;11:139-147. (In Russ).
8. Aversa F, et al. Identification of Cosmic Ray Electrons and Positrons by Neural Networks. Astroparticle Phys. 1996; 5(2):111-117.
9. Odorico R. Neural 2.00 - A Program for Neural Net and Statistical Pattern Recognition. Comput. Phys. Commun. 1996;96(2-3):314-330.
10. Nikonov VV. Ispolzovanie nejrosetevyh tekhnologij dlya opredeleniya kolichestvennogo sostava vrednyh veshchestv v vozdushnom bassejne atmosfery po dannym ik-spektrometrii. Ecological Systems and Devices. 2010;(5):16-19.
11. PCR News – glavnyj portal o molekulyarnoj diagnostike [Internet]. EASYPREP: zhidkostnaya citologiya protiv raka shejki matki. Available at: https://pcr.news/stati/easyprep-zhidkostnaya-tsitologiya-protiv-raka-sheyki-matki/. Accessed: 15 June 2021. (In Russ).
12. Cherushov IV. Povyshenie effektivnosti informacionno-izmeritel'noj sistemy kompleksnoj bezopasnosti zdanij i sooruzhenij [dissertation]. Moscow; 2012. Available at: https://dlib.rsl.ru/viewer/01005045581#?page=1. Accessed: 15 June 2021. (In Russ).
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DOI: 10.14489/lcmp.2021.01.pp.040-051
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