Application of Spectral Methods to Assess Gametes, Embryos, and Human Reproductive Capabilities


Family Planning Center of Women's Welfare Clinic #44 of Pushkin District






Other: making artificial intelligence based decisions of gamete, embryo and endometrial potential

Study type


Funder types




Details and patient eligibility


Relevance of the research topic: At present, in the world, a kind of "plateau" in the efficiency of assisted reproductive technologies has been achieved, which ensures a birth rate of 30% per embryo transfer. At the same time, a relatively high (15-20%) and stable rate of miscarriages is preserved. Until now, no effective methods for assessing the potential of gametes and embryos, as well as human reproductive capabilities, have been offered. In these conditions, to increase the rate of births after IVF, clinicians have to increase the number of transferred embryos at a time, however, this leads to a sharp increase in complications of IVF, such as multiple pregnancy. In addition, until today, the clinical effectiveness of assessing the potential of endometrium using gene expression determination methods has not been shown. Therefore, to ensure the effectiveness and safety of infertility treatment, it is necessary to develop methods for predicting the potential of gametes and embryos, as well as human reproductive capabilities. For this purpose, the investigators assume to use Raman spectroscopy of the environment obtained from the objects of research, as well as fluorescent spectroscopy of endometrium. The objects of the research are gametes (spermatozoa) and embryos, used culture medium, endometrium. The subject of the study is the set of factors, that exists in the objects of research and their ability to determine the outcomes of infertility treatment.

Full description

The goal of the planned work is to build a system for assessing and predicting the potential of gametes and embryos, as well as human reproductive capabilities, based on spectral data obtained from the objects of investigation, followed by prospective validation of the developed system. The study protocol consists of a retrospective and prospective stages. The task of the retrospective stage is to study gametes, embryos, and endometrium using the declared methods and build a machine learning model, determine the predictive capabilities of the obtained models. The task of the prospective stage is to determine the practical efficiency of applying models for making clinically significant decisions in infertility treatment with IVF. Hypothesis of the study: at the moment, a large number of approaches and protocols for deselecting and selecting embryos / gametes, assessing endometrial receptivity has been proposed. Approaches related to deselection are mainly based on determining the genetic constitution (aneuploidy) of the investigated object. However, there are no models linking such testing results and the outcome of infertility treatment with clinically significant effectiveness. There are many publications when, after transferring aneuploid embryos, pregnancy develops with a healthy fetus. It is known that the concordance of aneuploidy test results between the internal cell mass and trophoblasts is about 60%. Moreover, when using PGT-a, the birth rate among women with a single available blastocyst is reduced twice. Approaches related to selection, i.e. predicting a positive outcome of treatment, are built on morphological, morphometric, metabolic, and gene expression approaches. However, their effectiveness either has not been proven, or has (if it has) relatively low predictive importance. This is due to the fact that, from the point of modern views on reproductive biology, for the occurrence and development of successful pregnancy, it is necessary to combine factors that belong to gametes, embryos, and the maternal organism. Also, other undetectable technical or other circumstances may play a role in influencing the chance of ongoing pregnancy. Therefore, for effective prediction of a positive outcome, it is necessary to develop and apply complex models that take into account variables from different sources, from all parties involved. However, there will always be additional variability, caused by a series of unspecified or difficult to specify factors, which makes the task of such prediction quite challenging. In this connection, predicting a negative outcome of treatment (deselecting objects) seems more sensible, as it is entirely feasible for cases, where the cause of the negative outcome is attributable to this object (for example, the state of the embryo). This will not only optimize patient care protocols (for example, not to transfer obviously incapable to implant embryos), but also determine the possible cause of the negative outcome in each specific case, and in a population scale determine the share of variability of the phenomenon (development of ongoing pregnancy), which may be related to a specific object. The last one is necessary for adequate development and testing of new therapeutic and diagnostic methods


1,064 estimated patients




20 to 44 years old


No Healthy Volunteers

Inclusion criteria

  • Embryos: embryos that have reached the blastocyst stage
  • Sperm: samples used for IVF during infertility treatment
  • Endometrium: endometrial spectra in cases where an embryo transfer was performed into the uterus

Exclusion criteria

  • For all groups: ectopic pregnancy
  • Embryos: presence of only one blastocyst, and that embryo prognosed by the model as negative
  • Sperm: a cycle where less than 3 oocytes suitable for fertilization were obtained; total pathological fertilization; 60% or more immature oocytes at the time of fertilization registration

Trial design

Primary purpose




Interventional model

Parallel Assignment


Single Blind

1,064 participants in 2 patient groups

Experimental group
People undergoing IVF treatment with developed medical decisions support models
Other: making artificial intelligence based decisions of gamete, embryo and endometrial potential
No Intervention group
People undergoing IVF treatment without developed medical decisions support models

Trial contacts and locations



Central trial contact

Alexey Gryaznov

Data sourced from

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