O-243 Personalized pregnancy odds estimation can be obtained using AI embryo evaluation and individual patient characteristics

Abstract

Abstract Study question Can EMATM (AIVF, Israel) artificial intelligence (AI) platform provide personalized success estimates based on the patient’s metadata and embryonic development? Summary answer Individual patients can be given an accurate estimation of their chances for a clinical pregnancy using AI-based embryo evaluation and patient metadata. What is known already: AI models for embryo evaluation are trained on diverse datasets using data from multiple clinics and from patients with varying ages and clinical history. Precision medicine can be attained by AI models that provide individual patients with personalized success rates per embryo transfer based on their characteristics. Study design, size, duration A large dataset (9,812 embryos) from 3 geographically diverse IVF Clinics (Israel, Spain, USA) with known clinical pregnancy (fetal heartbeat) and patient characteristics obtained from Electronic Medical Records (EMR) were used to evaluate the importance of individual features contributing to pregnancy. Participants/materials, setting, methods Machine learning models were trained to predict clinical pregnancy on a large training and feature set which combines AI scores (EMATM, AIVF) with additional information obtained from EMR systems and intrinsic features that can be derived from the embryo cohort. The importance of each one of the features for the prediction ability of the models was evaluated. Main results and the role of chance AI embryo score, maternal age and cohort features were found to be the major contributing factors for the prediction models, thus improving the accuracy of the estimates for pregnancy probability. Significant contributing factors were: cohort size, number of viable embryos, patient age, number of past treatments, BMI and EMA score. The AUC of the AI embryo score model was 0.7, when factoring in the patient metadata and the cohort features, the AUC of the combined model was 0.75. Limitations, reasons for caution This study is limited by its retrospective design. A prospective study is needed to validate the results. Wider implications of the findings This study validates the use of AI-based scores for embryo evaluation, for relative grading of the embryos within the cohort, and also to estimate the true pregnancy odds of each embryo based on individual patient features. This can provide a superior decision support tool for doctors, embryologists, and patients. Trial registration number not applicable