Reproductive Medicine
Conditions
Brief 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.
Detailed description
This study is an observational prospective study after a retrospective analysis. It is a single-center study without randomization or blindness. In the early stage, 1000 patients are collected from three periods of embryo culture through Time-lapse to establish an automated joint evaluation system for the whole process of embryo development. At the later stage, the patients are divided into two groups: Time-Lapse imaging (TLI) +Artificial Intelligence(AI) assessment group and morphological assessment group. 100 patients with Day 5 single blastocyst transplantation are carried out to follow up the pregnancy outcome.
Interventions
DIAGNOSTIC_TESTAutomatic picture recognition
A machine that processes photographs automatically taken
DIAGNOSTIC_TESTManual Assessment Group
Manual recognition of pictures
Sponsors
The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Age
20 Years to 40 Years
Healthy volunteers
Yes
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| implantation rate | 2022-2023 | the probability of successful implantation of the embryo into the uterus |
Outcome results
None listed