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


Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription or Fee Access

Abstract

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.

Full Text

Restricted Access

About the authors

Polina A. Vishnyakova

Academician V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia

Email: p_vishnyakova@oparina4.ru
PhD, Senior Researcher, Laboratory of Regenerative Medicine

Evgeniy A. Kaprulevich

V.P. Ivannikov Institute for System Programming, Russian Academy of Sciences

Researcher, Department of Information Systems

Anastasia O. Kirillova

Academician V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia

Email: stasia.kozyreva@gmail.com
PhD, Senior Researcher of the 1st Gynecological Department

Vladislav V. Ananiev

V.P. Ivannikov Institute for System Programming, Russian Academy of Sciences

programmer, Department of Information Systems

Anton Yu. Naumov

V.P. Ivannikov Institute for System Programming, Russian Academy of Sciences

Research Assistant, Department of Information Systems

Timur Kh. Fatkhudinov

Peoples' Friendship University of Russia

Email: tfat@yandex.ru
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

References

  1. Ker J., Bai Y., Lee H.Y., Rao J., Wang L. Automated brain histology classification using machine learning. J. Clin. Neurosci. 2019; 66: 239-45. https://dx.doi.org/10.1016/j.jocn.2019.05.019.
  2. Alom M.Z., Yakopcic C., Nasrin M.S., Taha T.M., Asari V.K. Breast cancer classification from histopathological images with inception recurrent residual convolutional neural network. J. Digit. Imaging. 2019; 32(4): 605-17. https://dx.doi.org/10.1007/s10278-019-00182-7.
  3. Yan R., Ren F., Wang Z., Wang L., Zhang T., Liu Y. et al. Breast cancer histopathological image classification using a hybrid deep neural network. Methods. 2019; 173: 52-60. https://dx.doi.org/10.1016/j.ymeth.2019.06.014.
  4. Hekler A., Utikal J.S., Enk A.H., Solass W., Schmitt M., Klode J. et al. Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images. Eur. J. Cancer. 2019; 118: 91-6. https://dx.doi.org/10.1016/j.ejca.2019.06.012.
  5. Hannun A.Y., Rajpurkar P., Haghpanahi M., Tison G.H., Bourn C., Turakhia M.P., Ng A.Y. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat. Med. 2019; 25: 65-9. https://dx.doi.org/10.1038/s41591-018-0268-3.
  6. Korbar B., Olofson A., Miraflor A., Nicka C., Suriawinata M., Torresani L. et al. Deep learning for classification of colorectal polyps on whole-slide images. J. Pathol. Inform. 2017; 8: 30. https://dx.doi.org/10.4103/jpi.jpi_34_17.
  7. Wei J.W., Jackson C.R., Ren B. , Suriawinata A.A., Hassanpour S. Automated detection of celiac disease on duodenal biopsy slides: A deep learning approach. J. Pathol.Inform. 2019; 10: 7.https://dx.doi.org/10.4103/jpi.jpi_87_18.
  8. Martin D.R., Hanson J.A., Gullapalli R.R., Schultz F.A., Sethi A., Clark D.P. A deep learning convolutional neural network can recognize common patterns of injury in gastric pathology. Arch. Pathol. Lab. Med. 2020; 144(3): 370-8. https://dx.doi.org/10.5858/arpa.2019-0004-0A.
  9. İnik Ö., Ceyhan A., Balcıoğlu E., Ülker E. A new method for automatic counting of ovarian follicles on whole slide histological images based on convolutional neural network. Comput. Biol. Med. 2019; 112: 103350. https://dx.doi.org/10.1016/j.compbiomed.2019.103350
  10. Sun H., Zeng X., Xu T., Peng G., Ma Y. Computer-aided diagnosis in histopathological images of the endometrium using a convolutional neural network and attention mechanisms. IEEE J. Biomed. Heaalth Inform. 2020; 24(6): 1664-76. https://dx.doi.org/10.1109/JBHI.2019.2944977.
  11. Deng J., Dong W., Socher R., Li L.-J., Li K., Fei-Fei L. ImageNet: A large-scale hierarchical image database. In: 2009 IEEE Conference on computer vision and pattern recognition. Miami, FL, USA 20-25 June 2009: 248-55. https://dx.doi.org/10.1109/cvpr.2009.5206848.
  12. Kaufmann S.J., Eastaugh J.L., Snowden S., Smye S.W., Sharma V. The application of neural networks in predicting the outcome of in-vitro fertilization. Hum. Reprod. 1997; 12(7): 1454-7. https://dx.doi.org/10.1093/humrep/127.1454.
  13. Raef B., Ferdousi R. A review of machine learning approaches in assisted reproductive technologies. Acta Inform. Med. 2019; 27(3): 205-11. https://dx.doi.org/10.5455/aim.2019.27.205-211.
  14. Balaban B., Brison D., Calderon G., Catt J., Conaghan J., Cowan L. et al. Istanbul consensus workshop on embryo assessment: Proceedings of an expert meeting, Reprod. Biomed. Online. 2011; 22(6): 632-46. https://dx.doi.org/10.1016/j.rbmo.2011.02.001.
  15. Cummins J.M., Breen T.M., Harrison K.L., Shaw J.M., Wilson L.M., Hennessey J.F. A formula for scoring human embryo growth rates in in vitro fertilization: its value in predicting pregnancy and in comparison with visual estimates of embryo quality. J. In Vitro Fert. Embryo Transf. 1986; 3(5): 284-95. https:/dx./doi.org/10.1007/bf01133388.
  16. Fragouli E., Alfarawati S., Spath K., Wells D. Morphological and cytogenetic assessment of cleavage and blastocyst stage embryos. Mol. Hum. Reprod. 2014; 20(2): 117-26. https://dx.doi.org/10.1093/MOLEHR/GAT073.
  17. Gardner D.K., Lane M., Stevens J., Schoolcraft W.B. Noninvasive assessment of human embryo nutrient consumption as a measure of developmental potential. Fertil. Steril. 2001; 76(6): 1175-80. https://dx.doi.org/10.1016/S0015-0282(01)02888-6.
  18. Leese H.J. Metabolism of the preimplantation embryo: 40 years on. Reproduction. 2012; 143(4): 417-27. https://dx.doi.org/10.1530/REP-11-0484.
  19. Сысоева А.П., Макарова Н.П., Калинина Е.А., Скибина Ю.С., Занишевская А.А., Янчук Н.О., Грязнов А.Ю. Повышение эффективности вспомогательных репродуктивных технологий с помощью искусственного интеллекта и машинного обучения на эмбриологическом этапе. Акушерство и гинекология. 2020; 7: 28-36. https://dx.doi.org/10.18565/aig.2020.7.28-36.
  20. Валиахметова Э.З., Кулакова Е.В., Скибина Ю.С., Грязнов А.Ю., Сысоева А.П., Макарова Н.П., Калинина Е.А. Неинвазивное тестирование преимплантационных эмбрионов человека in vitro как способ прогнозирования исходов программ экстракорпорального оплодотворения. Акушерство и гинекология. 2021; 5: 5-16. https://dx.doi.org/10.18565/aig.2021.5.5-16.
  21. Montag M., Toth B., Strowitzki T. New approaches to embryo selection, Reprod. Biomed. Online. 2013; 27(5): 539-46. https://dx.doi.org/10.1016/j.rbmo.2013.05.013.
  22. Ahlstrom A., Wikland M., Rogberg L., Barnett J.S., Tucker M., Hardarson T. Cross-validation and predictive value of near-infrared spectroscopy algorithms for day-5 blastocyst transfer. Reprod. Biomed. Online. 2011; 22(5): 477-84. https://dx.doi.org/10.1016/j.rbmo.2011.01.009.
  23. Johnson M.H., Day M.L. Egg timers: how is developmental time measured in the early vertebrate embryo? Bioessays. 2000; 22: 57-63. https://dx.doi.org/10.1002/(SICI)1521-1878(200001)22:1<57:: AID-BIES10>3.0.m;2-L.
  24. Castello D., Motato Y., Basile N., Remohi J., Espejo-Catena M., Meseguer M. How much have we learned from time-lapse in clinical IVF? Mol. Hum. Reprod. 2016; 22(10): 719-27. https://dx.doi.org/10.1093/MOLEHR/GAW056.
  25. Lemmen J.G., Agerholm I., Ziebe S. Kinetic markers of human embryo quality using time-lapse recordings of IVF/ICSI-fertilized oocytes. Reprod. Biomed. Online. 2008; 17(3): 385-91. https://dx.doi.org/10.1016/S1472-6483(10)60222-2.
  26. Adamson G.D., Abusief M.E., Palao L., Witmer J., Palao L.M., Gvakharia M. Improved implantation rates of day 3 embryo transfers with the use of an automated time-lapse-enabled test to aid in embryo selection. Fertil. Steril. 2016; 105(2): 369-75.e6. https://dx.doi.org/10.1016/j.fertnstert.2015.10.030.
  27. Pribenszky C., Nilselid A.M., Montag M. Time-lapse culture with morphokinetic embryo selection improves pregnancy and live birth chances and reduces early pregnancy loss: a meta-analysis. Reprod. Biomed. Online. 2017; 35(5): 511-20. https://dx.doi.org/10.1016/j.rbmo.2017.06.022.
  28. Wong C.C., Loewke K.E., Bossert N.L., Behr B., De Jonge C.J., Baer T.M., Pera R.A.R. Non-invasive imaging of human embryos before embryonic genome activation predicts development to the blastocyst stage. Nat. Biotechnol. 2010; 28(10): 1115-21. https://dx.doi.org/10.1038/nbt.1686.
  29. Meseguer M., Herrero J., Tejera A., Hilligsoe K.M., Ramsing N.B., Remoh J. The use of morphokinetics as a predictor of embryo implantation. Hum. Reprod. 2011; 26(10) : 2658-71. https://dx.doi.org/10.1093/humrep/der256.
  30. Polanski L.T., Coelho Neto M.A., Nastri C.O., Navarro P.A., Ferriani R.A., Raine-Fenning N., Martins W.P. Time-lapse embryo imaging for improving reproductive outcomes: systematic review and meta-analysis. Ultrasound Obstet. Gynecol. 2014; 44(4): 394-401. https://dx.doi.org/10.1002/uog.13428.
  31. Fishel S., Campbell A., Foad F., Davies L., Best L., Davis N. et al. Evolution of embryo selection for IVF from subjective morphology assessment to objective time-lapse algorithms improves chance of live birth. Reprod. Biomed. Online. 2020; 40: 61-70. https://dx.doi.org/10.1016/j.rbmo.2019.10.005.
  32. Gardner D.K., Lane M., Stevens J., Schlenker T., Schoolcraft W.B. Blastocyst score affects implantation and pregnancy outcome: Towards a single blastocyst transfer. Fertil. Steril. 2000; 73(6): 1155-8. https://dx.doi.org/10.1016/S0015-0282(00)00518-5.
  33. Rienzi L., Cimadomo D., Delgado A., Minasi M.G., Fabozzi G., del Gallego R. et al. Time of morulation and trophectoderm quality are predictors of a live birth after euploid blastocyst transfer: a multicenter study. Fertil. Steril. 2019; 112(6): 1080-93.e1. https://dx.doi.org/10.1016/j.fertnstert.2019.07.1322.
  34. Романов А.Ю., Ковальская Е.В., Макарова Н.П., Сыркашева А.Г., Долгушина Н.В. Использование цейтраферной съемки для оценки качества эмбрионов человека в программах экстракорпорального оплодотворения. Цитология. 2017; 59(7): 462-6.
  35. Benchoufi M., Matzner-Lober E., Molinari N., Jannot A.S., Soyer P. Interobserver agreement issues in radiology. Diagn. Interv. Imaging. 2020; 101(10): 639-41. https://dx.doi.org/10.1016/j.diii.2020.09.001.
  36. Kang J.H., Choi S.H., Lee J.S., Park S.H., Kim K.W., Kim S.Y. et al. Interreader agreement of liver imaging reporting and data system on MRI: a systematic review and meta-analysis. J. Magn. Reson. Imaging. 2020; 52(3): 795-804. https://dx.doi.org/10.1002/jmri.27065.
  37. Lu M.Y., Chen R.J., Wang J., Dillon D., Mahmood F. Semi-supervised histology classification using deep multiple instance learning and contrastive predictive coding. Available at: http://arxiv.org/abs/1910.10825 Accessed June 10, 2021.

Supplementary files

Supplementary Files
Action
1. JATS XML

Copyright (c) 2021 Bionika Media

This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies