Assessment of the impact of male factor infertility on the outcomes of assisted reproductive technology programs using machine learning techniques

Мұқаба

Дәйексөз келтіру

Толық мәтін

Ашық рұқсат Ашық рұқсат
Рұқсат жабық Рұқсат берілді
Рұқсат жабық Рұқсат ақылы немесе тек жазылушылар үшін

Аннотация

Background: The interpretation of spermogram parameters in dynamic observation remains debatable, and investigation of the significance and impact of some parameters on the effectiveness of infertility treatment using assisted reproductive technologies (ART) under the circumstances of increasing rate of male factor infertility is extremely relevant. Data analysis using machine learning (ML) enables more accurate and targeted determination of most significant correctable and non-correctable predictors of pregnancy after using ART programs.

Objective: The purpose of the study was determination of the significance and impact of each parameter characterizing the quality of the ejaculate on pregnancy rate, as well as the impact of these indicators on the embryonic stage of ART programs using linear regression and machine learning techniques.

Materials and methods: The retrospective study included 1021 married couples. The study analyzed spermogram data on the day of transvaginal ovarian puncture depending on the clinical and embryological outcomes in ART programs using decision tree and linear regression algorithms.

Results: The analysis of linear regression and decision tree models showed different results of the significance of each factors of spermogram in determining the outcomes of the embryonic stage and pregnancy rate. It is noteworthy that the decision tree demonstrated high significance of the indicator “sperm concentration in 1 ml, mln”.

Conclusion: The results of the study reflect not only perspectives for further research in this area, but also the need to optimize the readiness of men for ART programs. Linear regression models not always capture hidden trends in the large volume of the analyzed information.

Толық мәтін

Рұқсат жабық

Авторлар туралы

Yulia Drapkina

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

Хат алмасуға жауапты Автор.
Email: yu_drapkina@oparina4.ru
ORCID iD: 0000-0002-0545-1607

PhD, Researcher at the Department of IVF named after Prof. B.V. Leonov

Ресей, Moscow

Natalya Makarova

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

Email: np_makarova@oparina4.ru

PhD, Leading Researcher at the Department of IVF named after Prof. B.V. Leonov

Ресей, Moscow

Elena Kulakova

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

Email: e_kulakova@oparina4.ru
ORCID iD: 0000-0002-4433-4163

PhD, Senior Researcher at the IVF Department named after Prof. B.V. Leonov

Ресей, Moscow

Elena Kalinina

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

Email: e_kalinina@oparina4.ru
ORCID iD: 0000-0002-8922-2878

Dr. Med. Sci., Professor, Head of the IVF Department named after Prof. B.V. Leonov

Ресей, Moscow

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