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| Name | Class |
|---|---|
| Czech Academy of Sciences | OTHER |
| Czech Technical University in Prague | OTHER |
| General University Hospital, Prague | OTHER |
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The assisted reproduction success rate is affected by several factors including the age of the women, oocyte quality and maturation state, as well as sperm quality. Imaging of the meiotic spindle may be crucial for determining the oocyte maturation. Artificial intelligence (AI) will be applied to establish the complex oocyte quality, embryo ploidy and pregnancy success probability from the sequence of data, starting with the recording of the meiotic spindle in polarized light, through paternal factors up to the time lapse recording of early embryo development. This strategy should reduce the cost of fertility treatment thanks to increased efficiency in choosing the most promising candidates and reducing the need for costly laboratory analyses.
One of the main strategies of infertility treatment is in vitro fertilization (IVF). The IVF success rate is affected by several key factors including the age of the women, oocyte quality and maturation state, as well as sperm quality. It has been suggested that the presence, position and retardance of the optically birefringent meiotic spindle (MS) are related to oocyte developmental competence, affecting the quality of fertilization and embryo development. Artificial intelligence (AI) will be applied to establish the complex oocyte quality, embryo ploidy and pregnancy success probability from the sequence of data, starting with the recording of the meiotic spindle in polarized light, through paternal factors up to the time lapse recording of early embryo development.
Synergic approaches will be used to increase the quality of embryos for implantation: image analysis and machine learning techniques will be applied to the oocyte microscopic images to perform the MS analysis fully automatically and to determine whether some other aspects of the oocyte appearance might correlate with the optimal timing and fertilization and pregnancy success, or genetic defects. An automatic method of embryo evaluation based on time-lapse videos after ICSI and MS imaging plus other scalar parameters (extracted features can be used as inputs for the downstream tasks, e.g. features extracted from oocytes and sperm can serve as additional inputs to the embryo classifier) will be used. This strategy should reduce the cost of fertility treatment thanks to increased efficiency in choosing the most promising candidates and reducing the need for costly laboratory analyses.
The analysis will be performed in cooperation with Czech Technical University and Institute of Physics Academy of Sciences of the Czech Republic who will create a software tool capable of predicting the probability of pregnancy and embryo ploidy status from oocyte images plus time-lapse video of a developing embryo after ICSI. It will be determined whether some other aspects of the oocyte appearance correlate with the fertilization and pregnancy success, or genetic defects.
Time lapse sequences of embryonic development and oocyte images will be acquired from VFN and from cooperating IVF centres (Gynem, s.r.o., Repromeda, s.r.o.). The sequences will be stored and paired with outcome (ploidy status, pregnancy) and also with previously acquired oocyte images. BIOCEV (Academy of sciences of the Czech Republic) will evaluate sperm parameters with respect to oocyte fertilization rate and early embryonic development.
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| arteficial inteligence evaluation of oocyte and embryo development | Procedure | apply AI to find out the complex oocyte quality, embryo development, embryo ploidy and pregnancy success probability from the sequence of the data starting from the recording of the meiotic spindle in polarized light, through paternal factors up to the time lapse record of early embryo development. |
|
| Measure | Description | Time Frame |
|---|---|---|
| The relative number of embryos whose ploidy was correctly predicted by AI | Using an AI based non-invasive method of selecting a high-quality and genetically healthy embryos will undoubtably improve clinical and diagnostic practice and reduce costs in the field of infertility treatment. Both the segmentation and classification training will be based on expert annotations. The approach should lead to a classification accuracy at least 70%. | 1 hour |
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Inclusion Criteria:
Exclusion Criteria:
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Infertility couples, where ICSI and PDT are indicated
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Jaromir Masata, MD | Contact | +420603444662 | masata@volny.cz |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| General University Hospital in Prague | Recruiting | Prague | 128 08 | Czechia |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 20581128 | Background | van Loendersloot LL, van Wely M, Limpens J, Bossuyt PM, Repping S, van der Veen F. Predictive factors in in vitro fertilization (IVF): a systematic review and meta-analysis. Hum Reprod Update. 2010 Nov-Dec;16(6):577-89. doi: 10.1093/humupd/dmq015. Epub 2010 Jun 25. | |
| 25119191 | Background | Wu B, Shi J, Zhao W, Lu S, Silva M, Gelety TJ. Understanding reproducibility of human IVF traits to predict next IVF cycle outcome. J Assist Reprod Genet. 2014 Oct;31(10):1323-30. doi: 10.1007/s10815-014-0288-y. Epub 2014 Aug 15. |
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Sperms
| Czech Technical University in Prague | Recruiting | Prague | Czechia |
|
| Institute of Physics AS CR | Recruiting | Prague | Czechia |
|
| Biocev As Cr | Recruiting | Vestec | Czechia |
|
| 35355060 | Background | Hanevik HI, Hessen DO. IVF and human evolution. Hum Reprod Update. 2022 Jun 30;28(4):457-479. doi: 10.1093/humupd/dmac014. |
| 12773461 | Background | Rienzi L, Ubaldi F, Martinez F, Iacobelli M, Minasi MG, Ferrero S, Tesarik J, Greco E. Relationship between meiotic spindle location with regard to the polar body position and oocyte developmental potential after ICSI. Hum Reprod. 2003 Jun;18(6):1289-93. doi: 10.1093/humrep/deg274. |
| 35165782 | Background | Innocenti F, Fiorentino G, Cimadomo D, Soscia D, Garagna S, Rienzi L, Ubaldi FM, Zuccotti M; SIERR. Maternal effect factors that contribute to oocytes developmental competence: an update. J Assist Reprod Genet. 2022 Apr;39(4):861-871. doi: 10.1007/s10815-022-02434-y. Epub 2022 Feb 15. |
| ID | Term |
|---|---|
| D007246 | Infertility |
| D007247 | Infertility, Female |
| D007248 | Infertility, Male |
| ID | Term |
|---|---|
| D000091662 | Genital Diseases |
| D000091642 | Urogenital Diseases |
| D005831 | Genital Diseases, Female |
| D052776 | Female Urogenital Diseases |
| D005261 | Female Urogenital Diseases and Pregnancy Complications |
| D005832 | Genital Diseases, Male |
| D052801 | Male Urogenital Diseases |
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| ID | Term |
|---|---|
| D047108 | Embryonic Development |
| C008848 | 1-phenyl-3,3-dimethyltriazene |
| ID | Term |
|---|---|
| D005314 | Embryonic and Fetal Development |
| D009024 | Morphogenesis |
| D048788 | Growth and Development |
| D010829 | Physiological Phenomena |
| D012098 | Reproduction |
| D055703 | Reproductive Physiological Phenomena |
| D012101 | Reproductive and Urinary Physiological Phenomena |
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