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1 . 2024

Evolution of predictive models for implementation of reproductive function in ART programs: from logistic regression to artificial intelligence

Abstract

The article presents data on the possibilities of predicting the outcomes of ART programs by building predictive models based on the literature, their evolution and current changes.

Keywords:IVF; assisted reproductive technologies; infertility; prediction model; computer-assisted learning; artificial intelligence

Funding. The study had no sponsor support.

Conflict of interest. The authors declare no conflict of interest.

Contribution. Concept development, editing and final approval of the manuscript – Korneeva I.E.; literature search and analysis, processing of source material, writing the text – Dashieva A.E.

For citation: Dashieva A.E., Korneeva I.E. Evolution of predictive models for implementation of reproductive function in ART programs: from logistic regression to artificial intelligence. Akusherstvo i ginekologiya: novosti, mneniya, obuchenie [Obstetrics and Gynecology: News, Opinions, Training]. 2024; 12 (1): 37–42. DOI: https://doi.org/10.33029/2303-9698-2024-12-1-37-42 (in Russian)

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CHIEF EDITORS
CHIEF EDITOR
Sukhikh Gennadii Tikhonovich
Academician of the Russian Academy of Medical Sciences, V.I. Kulakov Obstetrics, Gynecology and Perinatology National Medical Research Center of Ministry of Healthсаre of the Russian Federation, Moscow
CHIEF EDITOR
Kurtser Mark Arkadievich
Academician of the Russian Academy of Sciences, MD, Professor, Head of the Obstetrics and Gynecology Subdepartment of the Pediatric Department, N.I. Pirogov Russian National Scientific Research Medical University, Ministry of Health of the Russian Federation
CHIEF EDITOR
Radzinsky Viktor Evseevich
Corresponding Member of the Russian Academy of Sciences, MD, Professor, Head of the Subdepartment of Obstetrics and Gynecology with a Course of Perinatology of the Medical Department in the Russian People?s Friendship University

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