Application of Deep Learning to Jointly Assess Embryo Development to Improve Pregnancy Outcome of… (NCT05671601) | Clinical Trial Compass
UnknownNot Applicable
Application of Deep Learning to Jointly Assess Embryo Development to Improve Pregnancy Outcome of Embryo Transfer
100 participantsStarted 2022-12-30
Plain-language summary
Aim of this research is to apply the deep learning automation based on Time-lapse imaging to jointly assess embryo development,so that it can ensure the consistency of embryo evaluation and improve the accuracy of evaluation.
Who can participate
Age range20 Years – 40 Years
SexFEMALE
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Inclusion Criteria:
* (1) Age \< 40 years old; (2) Routine IVF cycles; (3) Period number ≤ 2; (4) The number of ova collected is 5-15; (5) BMI: 18-25 kg/m 2, follicle stimulating hormone(FSH) ≤ 12 IU/L on the third day; (6) Patients with more than 3 high-quality embryos on Day3 and performed single blastocyst transplantation on day 5. (7) Patient without endometrial factors.
Exclusion Criteria:
* (1) Preimplantation Genetic Testing(PGT) is needed due to male infertility, ovulation cycle and chromosome abnormalities; (2) there are systemic diseases of clinical significance; (3) Pictures of blastocysts are not formed or available; (4) Incomplete or unclear image collection in prokaryotic, mitotic and blastocyst phases affected AI evaluation.
What they're measuring
1
implantation rate
Timeframe: 2022-2023
Trial details
NCT IDNCT05671601
SponsorThe Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School