Predicting the success of in vitro fertilization in patients with chronic endometritis and reproductive disorders using neural network technology (secondary analysis of the results of the TULIP-2 randomized controlled trial)

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Abstract

When assisted reproductive technologies are used, recurrent implantation failures are observed in 7.7–67.5% of patients with chronic endometritis (CE).

Objective: To develop a predictive model of the probability of clinical pregnancy and live birth in women with uterine infertility due to CE using neural network technology at the stage of selection for in vitro fertilization (IVF) programs with cryotransfer and evaluate the effectiveness of this model.

Materials and methods: The secondary analysis of the results of the TULIP-2 randomized controlled trial was carried out. A total of 188 patients who met the objectives of this analysis were selected from the electronic database. The patients were divided into two comparison groups: group I (n=102) included patients who became pregnant, group II (n=86) included those who did not become pregnant.

Results: The model of predicting the success of IVF was created on the basis of 11 most significant parameters, which were identified after obtaining the results of the logistic analysis. The model was made using neural network technology. In order to predict the outcome of IVF, the following indicators were included in the structure of the multilayer perceptron: treatment, which included a complex of antimicrobial peptides and cytokines, CD-138, pulsation index in radial arteries according to Dopplerometry, oxygenation indices, proliferative activity, structuring according to laser conversion testing, interleukins such as -4, -10, -1ß, tumor necrosis factor-α according to enzyme immunoassay. The accuracy of the prediction was 97.9% (sensitivity is 100.0%, specificity is 96.4%). The information value of the model was confirmed by ROC analysis, the area under the curve (ROC-AUC) was 0.9, p<0.001. An online calculator was developed for the practical use of the model of individual prediction of IVF success.

Conclusion: The model of predicting clinical pregnancy and live birth as a result of IVF in patients with infertility caused by chronic endometritis, using neural network technology, has a high predictive accuracy and makes it possible to determine the need for administering another course (courses) of treatment for chronic endometritis or making a decision on the IVF procedure.

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About the authors

Anton A. Sukhanov

Perinatal Medical Center; Tyumen State Medical University, Ministry of Health of Russia

Author for correspondence.
Email: saa2505anton@yandex.ru
ORCID iD: 0000-0001-9092-9136

PhD, Head of the Department of Family Planning and Reproduction; Associate Professor, Department of Obstetrics and Gynecology

Russian Federation, Tyumen; Tyumen

Galina B. Dikke

F.I. Inozemtsev Academy of Medical Education

Email: galadikke@yandex.ru
ORCID iD: 0000-0001-9524-8962

Dr. Med. Sci., Professor, Department of Obstetrics and Gynecology with a Course of Reproductive Medicine

Russian Federation, St. Petersburg

Viktor A. Mudrov

Chita State Medical University, Ministry of Health of Russia

Email: mudrov_viktor@mail.ru
ORCID iD: 0000-0002-5961-5400

Dr. Med. Sci., Associate professor, Associate professor, Department of Obstetrics and Gynecology, Faculty of Pediatrics and Faculty of Additional Professional Education

Chita

Irina I. Kukarskaya

Perinatal Medical Center; Tyumen State Medical University, Ministry of Health of Russia

Email: such-anton@yandex.ru
ORCID iD: 0000-0002-8275-3553

Dr. Med. Sci., Professor of the Department of Obstetrics, Gynecology and Reanimatology with a Course of Clinical Laboratory Diagnostics; Chief Physician; Chief Specialist in Obstetrics and Gynecology, Department of Health of the Tyumen Region

Russian Federation, Tyumen; Tyumen

References

  1. Kimura F., Takebayashi A., Ishida M., Nakamura A., Kitazawa J., Morimune A. et al. Review: Chronic endometritis and its effect on reproduction. J. Obstet. Gynaecol. Res. 2019; 45(5): 951-60. https://dx.doi.org/10.1111/jog.13937.
  2. Liu Y., Chen X., Huang J., Wang C.C., Yu M.Y., Laird S., Li T.C. Comparison of the prevalence of chronic endometritis as determined by means of different diagnostic methods in women with and without reproductive failure. Fertil. Steril. 2018; 109(5): 832-9. https://dx.doi.org/10.1016/ j.fertnstert.2018.01.022.
  3. Vaduva C.C., Sandulescu M.S., Tenovici M., Siminel M.A., Novac M.B. Results of in vitro fertilization after diagnosis and treatment of chronic endometritis. Eur. Rev. Med. Pharmacol. Sci. 2023; 27(3): 1069-76. https://dx.doi.org/10.26355/eurrev_202302_31203.
  4. van Loendersloot L., Repping S., Bossuyt P.M., van der Veen F., van Wely M. Prediction models in in vitro fertilization; where are we? A mini review. J. Adv. Res. 2014; 5(3): 295-301. https://dx.doi.org/10.1016/j.jare.2013.05.002.
  5. Kasius A., Smit J.G., Torrance H.L., Eijkemans M.J., Mol B.W., Opmeer B.C., Broekmans F.J. Endometrial thickness and pregnancy rates after IVF: a systematic review and meta-analysis. Hum. Reprod. Update. 2014; 20(4): 530-41. https://dx.doi.org/10.1093/humupd/dmu011.
  6. Moshkalova G., Karibayeva I., Kurmanova A., Mamedalieva N., Aimbetova A., Terlikbayeva A. et al. Endometrial thickness and live birth rates after IVF: a systematic review. Acta Biomed. 2023; 94(3): e2023152. https://dx.doi.org/ 10.23750/abm.v94i3.14437.
  7. Broer S.L., van Disseldorp J., Broeze K.A., Dolleman M., Opmeer B.C., Bossuyt P. et al. Added value of ovarian reserve testing on patient characteristics in the prediction of ovarian response and ongoing pregnancy: an individual patient data approach. Hum. Reprod. Update. 2013; 19(1): 26-36. https://dx.doi.org/ 10.1093/humupd/dms041.
  8. Lukaszuk K., Kunicki M., Liss J., Lukaszuk M., Jakiel G. Use of ovarian reserve parameters for predicting live births in women undergoing in vitro fertilization. Eur. J. Obstet. Gynecol. Reprod. Biol. 2013; 168(2): 173-7. https://dx.doi.org/ 10.1016/j.ejogrb.2013.01.013.
  9. Bjercke S., Fedorcsak P., Åbyholm T., Storeng R., Ertzeid G., Oldereid N. et al. IVF/ICSI outcome and serum LH concentration on day 1 of ovarian stimulation with recombinant FSH under pituitary suppression. Hum. Reprod. 2005; 20(9): 2441-7. https://dx.doi.org/10.1093/humrep/dei101.
  10. Vaegter K.K., Lakic T.G., Olovsson M., Berglund L., Brodin T., Holte J. Which factors are most predictive for live birth after in vitro fertilization and intracytoplasmic sperm injection (IVF/ICSI) treatments? Analysis of 100 prospectively recorded variables in 8,400 IVF/ICSI single-embryo transfers. Fertil. Steril. 2017; 107(3): 641-8.e2. https://dx.doi.org/10.1016/ j.fertnstert.2016.12.005.
  11. McLernon D.J., Steyerberg E.W., Te Velde E.R., Lee A.J., Bhattacharya S. Predicting the chances of a live birth after one or more complete cycles of in vitro fertilisation: population based study of linked cycle data from 113 873 women. BMJ. 2016; 355: i5735. https://dx.doi.org/10.1136/bmj.i5735.
  12. Xu T., de Figueiredo Veiga A., Hammer K.C., Paschalidis I.C., Mahalingaiah S. Informative predictors of pregnancy after first IVF cycle using eIVF practice highway electronic health records. Sci. Rep. 2022; 12(1): 839. https://dx.doi.org/10.1038/s41598-022-04814-x.
  13. Choi B., Bosch E., Lannon B.M., Leveille M.C., Wong W.H., Leader A. et al. Personalized prediction of first-cycle in vitro fertilization success. Fertil. Steril. 2013; 99(7): 1905-11. https://dx.doi.org/10.1016/ j.fertnstert.2013.02.016.
  14. Ratna M.B., Bhattacharya S., Abdulrahim B., McLernon D.J. A systematic review of the quality of clinical prediction models in in vitro fertilisation. Hum. Reprod. 2020; 35(1): 100-16. https://dx.doi.org/10.1093/humrep/dez258.
  15. Tomita S. Unlocking the potential of bioanalytical data through machine learning. Anal. Sci. 2023; 39(12): 1937-8. https://dx.doi.org/10.1007/ s44211-023-00447-w.
  16. Sfakianoudis K., Maziotis E., Grigoriadis S., Pantou A., Kokkini G., Trypidi A. et al. Reporting on the value of artificial intelligence in predicting the optimal embryo for transfer: a systematic review including data synthesis. Biomedicines. 2022; 10(3): 697. https://dx.doi.org/10.3390/biomedicines10030697.
  17. VerMilyea M., Hall J.Z.M.M., Diakiw S.M., Johnston A., Nguyen T., Perugini D. et al. Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF. Hum. Reprod. 2020; 35(4): 770-84. https://dx.doi.org/ 10.1093/humrep/deaa013.
  18. Mihara M., Yasuo T., Kitaya K. Precision medicine for chronic endometritis: computer-aided diagnosis using deep learning model. Diagnostics (Basel). 2023; 13(5): 936. https://dx.doi.org/10.3390/diagnostics13050936.
  19. Суханов А.А., Дикке Г.Б., Остроменский В.В., Кукарская И.И., Шилова Н.В. Течение и исходы беременности, наступившей в результате экстракорпорального оплодотворения, у пациенток с хроническим эндометритом, получавших комплексное лечение с использованием препарата «Суперлимф» на прегравидарном этапе (рандомизированное контролируемое испытание «ТЮЛЬПАН 2»). Акушерство и гинекология. 2023; 8: 123-34. [Sukhanov A.A., Dikke G.B., Ostromensky V.V., Kukarskaya I.I., Shilova N.V. The course and outcomes of pregnancy resulting from in vitro fertilization in patients with chronic endometritis who received complex treatment using the drug “Superlymph” at the preconception stage (randomized controlled trial “TYULPAN 2”). Obstetrics and Gynecology. 2023; (8): 123-34. (in Russian)]. https://dx.doi.org/10.18565/ aig.2023.190.
  20. Vaduva C.C., Sandulescu M.S., Tenovici M., Siminel MA., Novac M.B. Results of in vitro fertilization after diagnosis and treatment of chronic endometritis. Eur. Rev. Med. Pharmacol. Sci. 2023; 27(3): 1069-76. https://dx.doi.org/10.26355/eurrev_202302_31203.
  21. Тапильская Н.И., Толибова Г.Х., Савичева А.М., Копылова А.А., Глушаков Р.И., Будиловская О.В. и др. Эффективность локальной цитокинотерапии хронического эндометрита пациенток с бесплодием. Акушерство и гинекология. 2022; 2: 91-100. [Tapilskaya NI., Tolibova G.Kh., Savicheva A.M., Kopylova A.A., Glushakov R.I., Budilovskaya O.V., et al. Efficacy of local cytokine therapy for chronic endometritis in infertile patients. Obstetrics and Gynecology. 2022; (2): 91-100. (in Russian)]. https://dx.doi.org/10.18565/aig.2022.2.91-100.
  22. Доброхотова Ю.Э., Ганковская Л.В, Боровкова Е.И., Зайдиева З.С., Скальная В.С. Модулирование локальной экспрессии факторов врожденного иммунитета у пациенток с хроническим эндометритом и бесплодием. Акушерство и гинекология. 2019; 5: 125-32. [Dobrokhotova Yu.E., Gankovskaya L.V., Borovkova E.I., Zaidieva Z.S., Skalnaya V.S. Modulation of local expression of innate immunity factors in patients with chronic endometritis and infertility. Obstetrics and Gynecology. 2019; (5): 125-32. (in Russian)]. https://dx.doi.org/10.18565/aig.2019.5.125-132.
  23. Дикке Г.Б., Суханов А.А., Кукарская И.И., Остроменский В.В. Цитокиновый профиль пациенток с хроническим эндометритом и нарушением репродуктивной функции. Вопросы гинекологии, акушерства и перинатологии. 2021; 20(6): 82-91. [Dikke G.B., Sukhanov A.A., Kukarskaya I.I., Ostromensky V.V. Cytokine profile of patients with chronic endometritis and reproductive dysfunction. Gynecology, Obstetrics and Perinatology. 2021; 20(6): 82-91. (in Russian)]. https://dx.doi.org/10.20953/ 1726-1678-2021-6-82-91.
  24. Квашнина Е.В., Гвоздикова Т.В., Дружинина А.Ю., Мастерова И.А., Мурунова С.В., Плотавская Т.Б., Тутаков М.А., Павлюченкова С.М., Шилова Н.В., Дикке Г.Б. Роль мио-инозитола в подготовке женщин к программам вспомогательных репродуктивных технологий. Акушерство и гинекология. 2020; 11: 139-46. [Kvashnina E.V., Gvozdikova T.V., Druzhinina A.Yu., Masterova I.A., Murunova S.V., Plotavskaya T.B., Tutakov M.A., Pavlyuchenkova S.M., Shilova N.V., Dikke G.B. The role of myo-inositol in preparing women for programs assisted reproductive technologies. Obstetrics and Gynecology. 2020; (11): 139-46. (in Russian)]. https://dx.doi.org/10.18565/aig.2020.11.139-146.
  25. Liu M., Yuan Y., Qiao Y., Tang Y., Sui X., Yin P., Yang D. The effectiveness of immunomodulatory therapies for patients with repeated implantation failure: a systematic review and network meta-analysis. Sci. Rep. 2022; 12(1): 18434. https://dx.doi.org/10.1038/s41598-022-21014-9.
  26. Зиганшин А.М., Дикке Г.Б., Мудров В.А. Прогнозирование клинически узкого таза с помощью нейросетевого анализа данных. Акушерство, гинекология и pепродукция. 2023; 17(2): 210-9. [Ziganshin A.M., Dikke G.B., Mudrov V.A. Prediction of clinically narrow pelvis using neural network data analysis. Obstetrics, Gynecology and Reproduction. 2023; 17(2): 210-9. (in Russian)]. https://dx.doi.org/10.17749/2313-7347/ ob.gyn.rep.2023.382.
  27. Андрейченко А.Е., Лучинин А.С., Ившин А.А., Ермак А.Д., Новицкий Р.Э., Гусев А.В. Разработка и валидация моделей прогнозирования общего риска преэклампсии и риска ранней преэклампсии с использованием алгоритмов машинного обучения в первом триместре беременности. Акушерство и гинекология. 2023; 10: 94-107. [Andreichenko A.E., Luchinin A.S., Ivshin A.A., Ermak A.D., Novitsky R.E., Gusev A.V. Development and validation of models for predicting the overall risk of preeclampsia and the risk of early preeclampsia using machine learning algorithms in the first trimester of pregnancy. Obstetrics and Gynecology. 2023; 10: 94-107. (in Russian)]. https://dx.doi.org/10.18565/aig.2023.101.
  28. MacEachern S.J., Forkert N.D. Machine learning for precision medicine. Genome. 2020; 64(4): 416-25. https://dx.doi.org/10.1139/ gen-2020-0131.
  29. Siristatidis C., Pouliakis A., Chrelias C., Kassanos D. Artificial intelligence in IVF: a need. Syst. Biol. Reprod. Med. 2011; 57(4): 179-85. https://dx.doi.org/ 10.3109/19396368.2011.558607.
  30. Curchoe C.L., Bormann C.L. Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018. J. Assist. Reprod. Genet. 2019; 36(4): 591-600. https://dx.doi.org/10.1007/s10815-019-01408-x.
  31. Zaninovic N., Rosenwaks Z. Artificial intelligence in human in vitro fertilization and embryology. Fertil. Steril. 2020; 114(5): 914-20. https://dx.doi.org/ 10.1016/j.fertnstert.2020.09.157.
  32. Hansen K.R., He A.L., Styer A.K., Wild R.A., Butts S., Engmann L. et al. Eunice Kennedy Shriver National Institute of Child Health and Human Development Reproductive Medicine Network. Predictors of pregnancy and live-birth in couples with unexplained infertility after ovarian stimulation-intrauterine insemination. Fertil. Steril. 2016; 105(6): 1575-83.e2. https://dx.doi.org/10.1016/j.fertnstert.2016.02.020.
  33. Kuru Pekcan M., Tokmak A., Ulubasoglu H., Kement M., Özakşit G. The importance of infertility duration and follicle size according to pregnancy success in women undergoing ovulation induction with gonadotropins and intrauterine insemination. J. Obstet. Gynaecol. 2023; 43(1): 2173058. https://dx.doi.org/ 10.1080/01443615.2023.2173058.
  34. Zhang L., Cai H., Li W., Tian L., Shi J. Duration of infertility and assisted reproductive outcomes in non-male factor infertility: can use of ICSI turn the tide? BMC Womens Health. 2022; 22(1): 480. https://dx.doi.org/10.1186/s12905-022-02062-9.
  35. Abdelazim I., Purohit P., Farag R., Zhurabekova G. Unexplained infertility: prevalence, possible causes and treatment options. A review of the literature. J. Obstet. Gynecol. Investigations. 2018; 1(1): 17-22. https://dx.doi.org/10.5114/jogi.2018.74250.
  36. Liu J., Liu ZA., Liu Y., Cheng L., Yan L. Impact of antibiotic treatment for chronic endometritis on pregnancy outcomes in women with reproductive failures (RIF and RPL): A systematic review and meta-analysis. Front. Med. (Lausanne). 2022; 9: 980511. https://dx.doi.org/10.3389/ fmed.2022.980511.
  37. Buzzaccarini G., Vitagliano A., Andrisani A., Santarsiero C.M., Cicinelli R., Nardelli C. et al. Chronic endometritis and altered embryo implantation: a unified pathophysiological theory from a literature systematic review. J. Assist. Reprod. Genet. 2020; 37(12): 2897-911. https://dx.doi.org/10.1007/ s10815-020-01955-8.
  38. Зуев В.М., Александров М.Т., Хомерики Т.А., Чернышов Г., Метревели Б.Г., Попов С.Н. и др. Клиническое значение лазерного конверсионного тестирования (ЛКТ) в диагностике и лечении хронического эндометрита. Вестник РУДН. Cерия: Медицина. 2012; 6: 95-101. [Zuev V.M., Aleksandrov M.T., Khomeriki T.A., Chernyshov G., Metreveli B.G., Popov S.N. et al. Clinical significance of laser conversion testing (LCT) in diagnosis and treatment of chronic endometritis. Bulletin of RUDN University, Medicine series. 2012; 6: 95-101. (in Russian)]. Available at: https://cyberleninka.ru/article/
  39. Осипова А.Д., Зуев В.М., Александров М.Т. Значение экспресс-метода лазерной конверсионной диагностики (раман-флуоресцентной составляющей) патологических процессов эндометрия у женщин в перименопаузе. Архив акушерства и гинекологии им. В. Ф. Снегирева. 2019; 2: 85-9. [Osipova A.D., Zuev V.M., Aleksandrov M.T. The significance of the express method of laser conversion diagnostics (Raman fluorescent component) of pathological processes of the endometrium in perimenopausal women. Archive of Obstetrics and Gynecology named after. V.F. Snegirev. 2019; (2): 85-9. (in Russian)]. https://dx.doi.org/10.18821/ 2313-8726-2019-6-2-85-89.
  40. Суханов А.А., Дикке Г.Б. Дефект имплантации эмбриона. Положительный прогноз – залог успешного ЭКО. Акушерство и гинекология сегодня. 2023; 4: 10-11. [Sukhanov A.A., Dikke G.B. Embryo implantation defect. A positive prognosis is the key to successful IVF. Obstetrics and Gynecology Today. 2023; (4): 10-11. (in Russian)]. Available at: https://abvpress.ru/

Supplementary files

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2. Fig. 1. The structure of a multilayer percepton, which allows predicting the outcome of IVF in patients with infertility after treatment with cholecystectomy

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3. Fig. 2. The importance of the studied parameters in the structure of the developed neural network

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4. Fig. 3. ROC analysis of the probability of IVF outcome in patients with infertility caused by cholecystectomy after treatment

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5. Fig. 4. Interface of the Program for individual prediction of the probability of pregnancy/live birth in patients with CE before IVF (online calculator). A - example of a positive result (pregnancy with live birth); B - example of a questionable result (pregnancy with an unfavorable outcome); B - example of a negative result (no pregnancy)

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