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Obstetrics and Gynegology2024№4
Predicting the success of in...

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)

DOI
https://dx.doi.org/10.18565/aig.2024.47

Sukhanov A.A., Dikke G.B., Mudrov V.A., Kukarskaya I.I.

1) Perinatal Medical Center, Tyumen, Russia; 2) Tyumen State Medical University, Ministry of Health of Russia, Tyumen, Russia; 3) F.I. Inozemtsev Academy of Medical Education, St. Petersburg, Russia; 4) Chita State Medical University, Ministry of Health of Russia, Chita, Russia

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.

Authors’ contributions: Sukhanov A.A. – collection of clinical material, formation of an electronic database, writing fragments of the article and editing the article; Dikke G.B. – development of the concept, design and program of the study, supervision during the study, analysis of the results of statistical processing of clinical material and their interpretation, search for literary sources, writing fragments of the article and editing the article after reviewing; Mudrov V.A. – development of the study program, statistical processing of clinical material, analysis of results, development of the prognostic model using neural network technology and its interpretation, writing the program for an online calculator, writing a fragment of the article and editing the article after reviewing; Kukarskaya I.I. – conducting the study in the clinic, supervision during the study.
Conflicts of interest: The authors report no conflicts of interest and guarantee that the article is the original work of the authors.
Funding: The study was carried out using own resources. The publication of the article was supported by Pentcroft Pharma Company.
Ethical Approval: The study was approved by the Ethical Review Board of the Perinatal Medical Center, Tyumen, Russia).
Authors' Data Sharing Statement: The data supporting the findings of this study are available on request from the corresponding author after approval from the principal investigator.
For citation: Sukhanov A.A., Dikke G.B., Mudrov V.A., Kukarskaya I.I. 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).
Akusherstvo i Ginekologiya/Obstetrics and Gynecology. 2024; (4): 103-114 (in Russian)
https://dx.doi.org/10.18565/aig.2024.47

Keywords

infertility
chronic endometritis
prognosis
IVF
neural network technology
Full text (in Russian)

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Received 05.03.2024

Accepted 19.03.2024

About the Authors

Anton A. Sukhanov, PhD, Head of the Department of Family Planning and Reproduction, Tyumen Perinatal Center, 1 Daudelnaya str., Tyumen, 625002, Russia;
Associate Professor, Department of Obstetrics and Gynecology, Tyumen State Medical University, Ministry of Health of Russia, 10 Permyakov str., Tyumen, 625013, Russia, saa2505anton@yandex.ru, https://orcid.org/0000-0001-9092-9136
Galina B. Dikke, Dr. Med. Sci., Professor, Department of Obstetrics and Gynecology with a Course of Reproductive Medicine, F.I. Inozemtsev Academy of Medical Education, 22 Liter M Moskovskiy Ave., Saint Petersburg, 190013, Russia, galadikke@yandex.ru, https://orcid.org/0000-0001-9524-8962
Viktor A. Mudrov, Dr. Med. Sci., Associate professor, Associate professor, Department of Obstetrics and Gynecology, Faculty of Pediatrics and Faculty of Additional Professional Education, Chita State Medical Academy, Ministry of Health of Russia, 39a Gorkogo str., Chita, 672000, Russia, mudrov_viktor@mail.ru,
https://orcid.org/0000-0002-5961-5400
Irina I. Kukarskaya, Dr. Med. Sci., Professor of the Department of Obstetrics, Gynecology and Reanimatology with a Course of Clinical Laboratory Diagnostics,
Tyumen State Medical University, 10 Permyakov str., Tyumen, 625013, Russia; Chief Physician, Tyumen Region Perinatal Center, 1 Daudelnaya str., Tyumen, 625002, Russia;
Chief Specialist in Obstetrics and Gynecology, Department of Health of the Tyumen Region, https://orcid.org/0000-0002-8275-3553
Corresponding author: Anton A. Sukhanov, such-anton@yandex.ru

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