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

Application of Deep Learning Automation Based on Time-lapse Imaging to Jointly Assess Embryo Development to Improve Pregnancy Outcome of Single Blastocyst Transfer

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Reproductive Medicine
• Intervention / Treatment: Diagnostic test: Automatic picture recognition; Diagnostic test: Manual Assessment Group

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.

• Study Type: Observational
• Enrollment (Anticipated): 100

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 20 years to 40 years (Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: Female

• Sampling Method: Non-Probability Sample

Study Population

Patients are under 40 years old and need artificial intervention to have children

Description

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.

Study Plan

How is the study designed?

Design Details

• Observational Models: Other
• Time Perspectives: Prospective

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
TLI+AI Assessment Group	Diagnostic test: Automatic picture recognition; A machine that processes photographs automatically taken
Morphological Assessment Group	Diagnostic test: Manual Assessment Group; Manual recognition of pictures

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
implantation rate	the probability of successful implantation of the embryo into the uterus	2022-2023

Collaborators and Investigators

• Sponsor: The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School

Study record dates

Study Major Dates

• Study Start (Anticipated): December 30, 2022
• Primary Completion (Anticipated): December 15, 2023
• Study Completion (Anticipated): June 15, 2024

Study Registration Dates

• First Submitted: December 5, 2022
• First Submitted That Met QC Criteria: December 26, 2022
• First Posted (Estimate): January 4, 2023

Study Record Updates

• Last Update Posted (Estimate): January 4, 2023
• Last Update Submitted That Met QC Criteria: December 26, 2022
• Last Verified: November 1, 2022

More Information

Terms related to this study

• Other Study ID Numbers: wangshanshan820

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: Yes