Summary
In vitro fertilization (IVF) treatments are markedly inefficient since a significant fraction of embryos lacks the capacity to implant. Even for successfully implanted embryos, one out of nine clinical pregnancies is terminated due to first trimester miscarriage. In general, embryos that harbor certain chromosomal abnormalities leading to early pregnancy loss often permit “normal preimplantation development” that cannot be detectable via human visualization of time lapse images. Since current state-of-the-art algorithms fail to accurately assess the developmental potential of embryos to reach live birth, reasonable pregnancy rates are obtained by transferring more than one embryo to the uterus. However, multiple pregnancy (of more than one embryo), which account for 30-to-40% of all pregnancies (>95% are twins), are associated with a higher health risk to the mother and to the newborn. Here I propose to develop an embryo-transfer decision-support tool (ET-DST) that will analyze all possible embryo-transfer strategies that include multiple transfer cycles of embryos of the same patient. The ET-DST will identify the transfer strategy that optimizes the potential to reach live-birth and shorten time-to-pregnancy by combining multiple classifiers that evaluate the potential of embryos to implant, assess the risk of miscarriage, and predict the optimal day of transfer. The physician/embryologist will be able to set both embryo-specific parameters and maternal labels as follows: (1) a penalty of multiple pregnancies, (2) a cryopreservation factor that reflects the decrease in embryo quality due to verification and thawing compared with fresh transfer, (3) a maternal factor that reflects the clinical background of the patient, and (4) a penalty of multiple transfer cycles. The integration of patient specific parameters will minimize health risks while optimizing live-birth rates and shortening time-to-pregnancy.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/966830 |
| Start date: | 01-05-2021 |
| End date: | 30-04-2023 |
| Total budget - Public funding: | - 150 000,00 Euro |
Cordis data
Original description
In vitro fertilization (IVF) treatments are markedly inefficient since a significant fraction of embryos lacks the capacity to implant. Even for successfully implanted embryos, one out of nine clinical pregnancies is terminated due to first trimester miscarriage. In general, embryos that harbor certain chromosomal abnormalities leading to early pregnancy loss often permit “normal preimplantation development” that cannot be detectable via human visualization of time lapse images. Since current state-of-the-art algorithms fail to accurately assess the developmental potential of embryos to reach live birth, reasonable pregnancy rates are obtained by transferring more than one embryo to the uterus. However, multiple pregnancy (of more than one embryo), which account for 30-to-40% of all pregnancies (>95% are twins), are associated with a higher health risk to the mother and to the newborn. Here I propose to develop an embryo-transfer decision-support tool (ET-DST) that will analyze all possible embryo-transfer strategies that include multiple transfer cycles of embryos of the same patient. The ET-DST will identify the transfer strategy that optimizes the potential to reach live-birth and shorten time-to-pregnancy by combining multiple classifiers that evaluate the potential of embryos to implant, assess the risk of miscarriage, and predict the optimal day of transfer. The physician/embryologist will be able to set both embryo-specific parameters and maternal labels as follows: (1) a penalty of multiple pregnancies, (2) a cryopreservation factor that reflects the decrease in embryo quality due to verification and thawing compared with fresh transfer, (3) a maternal factor that reflects the clinical background of the patient, and (4) a penalty of multiple transfer cycles. The integration of patient specific parameters will minimize health risks while optimizing live-birth rates and shortening time-to-pregnancy.Status
CLOSEDCall topic
ERC-2020-POCUpdate Date
27-04-2024
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