Application of machine learning algorithms in morphopathology and in assisted reproductive technologies

Machine learning models are used everywhere to analyze images, signals, and videos. At first glance, this is a well-designed process that involves the stages of data collection, mark-up, and training a model, and, as a result, its application in a particular field (recognition of vehicle plate numbers, smartphone faces, etc.). However, everything is much more complicated in the field of medicine: the use of artificial intelligence models is a serious challenge. Machine learning methods are becoming more and more used in morphological sciences and biomedical studies. The introduction of artificial intelligence for image analysis can lower the burden on an operator (a pathologist, a histologist), eliminate the factor of subjective assessment, and reduce the likelihood of an error. This review provides a brief excursion into the history of machine learning methods, considers the examples of their use in two areas where they are most widespread: morphopathology and assisted reproductive technologies, and also indicates the limitations and difficulties that developers face when training neural networks. Conclusion: The authors also propose solutions to overcome the difficulties associated with the collection and joint marking of data, and model training: creation of a high-quality infrastructure, attraction of highly qualified specialists who mark data, an advanced scientific approach to artificial intelligence technologies; cloud platforms are offered to be used as a basis for the scalable storage and analysis of biomedical data. © 2021, Bionika Media Ltd.. All rights reserved.

Authors
Vishnyakova P.A. 1 , Kaprulevich E.A.2 , Kirillova A.O.1 , Ananiev V.V.2 , Naumov A.Yu.2 , Fatkhudinov T.Kh. 3
Publisher
ООО «Бионика Медиа»
Number of issue
10
Language
Russian
Pages
38-46
Status
Published
Volume
2021
Year
2021
Organizations
  • 1 Academician V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, Moscow, Russian Federation
  • 2 V.P. Ivannikov Institute for System Programming, Russian Academy of Sciences, Moscow, Russian Federation
  • 3 Peoples’ Friendship University of Russia (RUDN University), Ministry of Education and Higher Science of the Russian Federation, Moscow, Russian Federation
Keywords
Machine learning; Neural network; Time-lapse microscopy
Date of creation
16.12.2021
Date of change
16.12.2021
Short link
https://repository.rudn.ru/en/records/article/record/76877/
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