P-201 Embryo prioritization through an AI-powered evaluation system improves the clinical outcome in IVF cycles
Authors: Tang Y  A , Chien C  W , Lee S  J , Pan H  A , Sun S 
Published: July 2024

Abstract

Study question
How can the implantation rate be improved through the prioritization of embryos, particularly in the context of elective single embryo transfer (eSET)?

Summary answer
Based on the retrospective data of IVF treatment using 965 embryos, the AI-powered embryo-evaluation system, icONE, was developed to provide unbiased selection with promising accuracy.

What is known already
Over the past decade, numerous predictive models guiding IVF treatments for frozen-thawed embryo transfer (FET) success have emerged. However, most are unidimensional, focusing on factors like patient characteristics (e.g., age, reproductive hormones), embryo quality (e.g., morphokinetics), or biomarkers (e.g., genes, microRNAs). Despite efforts, many models exhibit limited AUROC (area under the receiver operating characteristic curve) values of 0.70∼0.77, suggesting a need for comprehensive consideration of maternal and embryonic factors to enhance predictive accuracy.

Study design, size, duration
The current study aims to develop an AI-assisted embryo prediction system. Nine hundred and sixty-five embryos from 232 patients (ages ranging from 24 to 50 years old, mean age = 36.9 years) who underwent IVF treatment during 2015–2018 were enrolled for developing an embryo prediction system. The clinical information and detail of IVF procedures of each patient and embryo were recorded and integrated to explore their impact on clinical outcomes.

Participants/materials, setting, methods
Pregnant outcomes were followed, including hCG examination, sac formation, and live birth. Whole genome sequencing (WGS) analysis using TE biopsy was performe. Genetic features extracted from WGS data and maternal clinicopathological characteristics were integrated and calculated by decision tree algorithm and machine learning algorithms, including Random Forest, XGBoost, LightGBM, Tabnet, etc.

Main results and the role of chance
To reduce the number of IVF cycles, diminish the risk associated with multiple pregnancy, and maximize the efficacy of IVF treatment, we analyzed genetic data generated from the preimplantation genetic screenings and developed a prototype of an AI-assisted program to evaluate the viability and quality of each embryo. The “intelligent cOmputing Noble Embryo” (icONE) program integrates genetic data and maternal clinical features. icONE showed classification accuracy and prediction specificity of 0.81 and 0.83, respectively, and the overall prediction accuracy of successful implantation reached to 92%. Random Forest outperformed other machine learning tools with overall positive predictive value, negative predictive value, specificity, and sensitivity of 91.8%, 91.0%, 90.3%, and 92.4%, respectively. The overall prediction accuracy of successful implantation reaches was 91.4%. Notably, the preliminary results of a randomized prospective clinical trial showed that the clinical pregnancy rate reached 77.3% (n = 81) based on the icONE’s evaluation, which was significantly higher than that of 50% (n = 85) in the non-icONE group. Patients of both advanced and young age benefited from icONE. Taken together, these results endorse the efficacy of icONE in prioritizing embryos for clinicians in IVF treatment.

Limitations, reasons for caution
It is noticed that our study is limited by the small sample size and all the data are collected from a single IVF center. The uniform and controlled operation of IVF cycles in a single center may cause selection bias.

Wider implications of the findings
Given the shift towards eSET in reproductive medicine, prioritizing embryos for implantation potential is vital in infertility treatment. The AI-powered model established in this study enhances clinical practice, optimizing parenthood chances for those facing fertility issues.

Trial registration number
not applicable