NCT05671601

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

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 100 (estimated)
• Sponsor: The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School · Academic / Other
• Sex: Female
• Age: 20 Years – 40 Years
• Healthy volunteers: Accepted

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.

Conditions

• Reproductive Medicine

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	Automatic picture recognition	A machine that processes photographs automatically taken
DIAGNOSTIC_TEST	Manual Assessment Group	Manual recognition of pictures

Timeline

• Start date: 2022-12-30
• Primary completion: 2023-12-15
• Completion: 2024-06-15
• First posted: 2023-01-04
• Last updated: 2023-01-04

Regulatory

• FDA-regulated device study