A Novel Integrative Non-invasive Embryo Selection Approach for in Vitro Fertilization Based on Artificial Intelligence Enhanced Morphokinetic Analysis and Raman Spectra in Spent Culture Media
Trial ID: NCT05896709
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-07-17
Completion date: 2026-12
Sponsor: Chinese University of Hong Kong
Conditions: Reproductive Issues; Subfertility; IVF
Interventions: MK-RS
Locations: The Chinese University of Hong Kong
Countries: Hong Kong
Enrollment: 176
Age range: None - None
Sex: ALL

Summary:
During assisted reproductive technology treatment, embryo selection is an important process that may affect the clinical pregnancy rate. Many assisted reproductive technology units over the world have tried different approaches to increase the clinical pregnancy rate. Conventionally, the morphology of the embryo is assessed by the embryologist with naked eyes only. Nowadays, artificial intelligence (AI) has been used to assist in morphological assessment of the embryo. Our pilot study showed that the AI-enhanced morphokinetic (MK) analysis increased the accuracy in embryo selection by \~9%, while the detection rate for abnormal chromosomes in embryo has also been increased by Raman spectroscopy (RS) analysis. The combined MK-RS analysis will be able to complete embryo assessment within 5-6 days after fertilization. This method needs shorter time and is at lower cost when compared to invasive preimplantation genetic testing for aneuploidies (PGT-A).

In this study, we have combined the following non-invasive techniques to assist in embryo screening.

1. Using time-lapse imaging (i.e. images of embryo being taken every 10 minutes inside the incubator) with AI)-enhanced MK analysis to assess the entire morphological changes of the embryo.
2. As the embryo releases metabolites during its growth, the spent culture medium will be collected after culture of the embryo and then be used for RS analysis, which is a kind of metabolomics-based non-invasive PGT-A, for screening chromosomal abnormalities of the embryo.

This study will include two phases. In Phase I, it is a retrospective part. We will collect data to train the convolutional neural network (CNN)-enhanced MK with RS method on embryo selection, leading to the integrated approach (MK-RS). In Phase II, it is a randomized controlled trial and participants will be randomised into 2 groups. For the experimental group, embryo selection will be based on the MK-RS method, whereas embryo selection for the control group will rely on the traditional embryo assessment results alone. Then we will assess the clinical pregnancy rate and evaluate the efficacy of our approach finally. Patients who receive in vitro fertilisation (IVF)/ intracytoplasmic sperm injection (ICSI) treatment from The Assisted Reproductive Technology (ART) Unit of The Chinese University of Hong Kong, Prince of Wales Hospital will be recruited.

Eligibility criteria:
Inclusion Criteria:

1. Patients undergoing IVF/ ICSI treatment
2. Patients receiving the first, second or third IVF/ICSI treatment cycle
3. Patients and their partners willing to sign the informed consent agreement
4. Patients having at least three normal fertilised embryos on the day of the fertilization check
5. Consecutive women undergoing IVF treatment
6. Patients planning to use a time-lapse incubator for embryo culture

Exclusion Criteria:

1. Day one human embryos with blur imaging
2. Large obstructions in the embryo area
3. More than half of the embryo area is blocked by the well or degeneration
4. Patients with more than half of the embryos without sufficient spent culture medium for RS analysis
5. Patients with known genetic diseases

Outcomes:
Clinical pregnancy rate