Blastocyst selection through an interpretable artificial intelligence method versus traditional morphology grading: study protocol for a randomised controlled trial.

Abstract

The quality of the blastocyst (day 5/6 embryo) selected for transfer is critical for the success of in vitro fertilisation (IVF) treatment. Embryologists perform blastocyst evaluation by observing the morphology of each blastocyst. Human assessment is subjective and inconsistent in predicting which blastocyst can result in a successful pregnancy or birth. Several artificial intelligence (AI) methods have been proposed to predict IVF outcomes from blastocyst images. However, the reasoning processes of these AI methods are uninterpretable, causing epistemic and ethical concerns that prevent their implementation in clinical practice. To address this issue, the authors developed a novel interpretable AI method for blastocyst selection. The method is clinically applicable because it is transparent to embryologists and allows them to understand its reasoning processes. This randomised controlled trial (RCT) aims to test the effectiveness of this blastocyst selection method with the aim of improving IVF outcomes. In this single-centre, single-blind RCT, we will enrol 1100 women aged 20-35 years undergoing their first cycle of IVF, with or without intracytoplasmic sperm injection. The study will be conducted at Nanjing Drum Tower Hospital, a public class A tertiary hospital in China. On the fifth day of embryo culture, participants with two or more usable blastocysts will be randomised in a 1:1 ratio to either the conventional morphology group or the AI group. The primary outcome is ongoing pregnancy, defined as a viable intrauterine pregnancy of 12 weeks gestation or more. The research ethics committee of the Nanjing Drum Tower Hospital approved this study (approval number: 2023-259-02). All participants will provide written informed consent prior to enrolment. The findings will be presented at international conferences and published in peer-reviewed journals. ChiCTR2300076851.