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.Vishnyakova P.A., Karpulevich E.A., Kirillova A.O., Ananiev V.V., Naumov A.Yu., Fatkhudinov T.Kh.
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.
Keywords
neural network
machine learning
time-lapse microscopy
About the Authors
Polina A. Vishnyakova, PhD, Senior Researcher, Laboratory of Regenerative Medicine, Academician V.I. Kulakov National Medical Research Center for Obstetrics,Gynecology and Perinatology, Ministry of Health of the Russian Federation, p_vishnyakova@oparina4.ru, 117997, Russia, Moscow, Ac. Oparina str., 4.
Evgeniy A. Karpulevich, Researcher, Department of Information Systems, V.P. Ivannikov Institute for System Programming, Russian Academy of Sciences,
karpulevich@ispras.ru, 109004, Russia, Moscow, Alexander Solzhenitsyn str., 25.
Anastasia O. Kirillova, PhD, Senior Researcher of the 1st Gynecological Department, Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of the Russian Federation, stasia.kozyreva@gmail.com, 117997, Russia, Moscow, Ac. Oparina str., 4.
Vladislav V. Ananiev, programmer, Department of Information Systems, V.P. Ivannikov Institute for System Programming, Russian Academy of Sciences,
novisp53@ispars.ru, 109004, Russia, Moscow, Alexander Solzhenitsyn str., 25.
Anton Yu. Naumov, Research Assistant, Department of Information Systems, V.P. Ivannikov Institute for System Programming, Russian Academy of Sciences,
anton-naymov@yandex.ru, 109004, Russia, Moscow, Alexander Solzhenitsyn str., 25.
Timur Kh. Fatkhudinov, Dr. Med. Sci., Deputy Director, Research Institute of Human Morphology of the Russian Academy of Sciences; Head of the Department of Histology, Cytology and Embryology, Deputy Director for Research of the Medical Institute, RUDN University, tfat@yandex.ru, 117997, Russia, Moscow, Miklukho-Maklaya str., 8.
Authors' contributions: Vishnyakova P.A., Karpulevich E.A., Kirillova A.O., Ananiev V.V., Naumov A.Yu., Fatkhudinov T.Kh. – literature analysis; data summation; writing the article.
Conflicts of interest: The authors declare that there are no conflicts of interest.
Funding: The investigation was conducted within the framework of State Assignment No. 121040600436-7.
This was funded by the Russian Foundation for Basic Research and the Government of Moscow within the framework
of Research Project No. 21-315-70048.
For citation: Vishnyakova P.A., Karpulevich E.A., Kirillova A.O., Ananiev V.V., Naumov A.Yu., Fatkhudinov T.Kh. Application of machine learning algorithms
in morphopathology and in assisted reproductive technologies.
Akusherstvo i Ginekologiya/Obstetrics and Gynecology. 2021; 10: 38-46 (in Russian)
https://dx.doi.org/10.18565/aig.2021.10.38-46
Similar Articles
