Application of Deep Learning to Jointly Assess Embryo Development to Improve Pregnancy Outcome of Embryo Transfer

Nanjing University

Status

Not yet enrolling

Conditions

Reproductive Medicine

Treatments

Diagnostic Test: Automatic picture recognition

Diagnostic Test: Manual Assessment Group

Study type

Observational

Funder types

Other

Identifiers

NCT05671601

wangshanshan820

Details and patient eligibility

About

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.

Full 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.

Enrollment

100 estimated patients

Sex

Female

Ages

20 to 40 years old

Volunteers

Accepts Healthy Volunteers

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.