O-220 An annotation-free embryo scoring system (iDAScore®) based on deep learning shows high performance for pregnancy prediction after single-vitrified blastocyst transfer

Study question How is the performance of an automated embryo scoring system for pregnancy prediction after single-vitrified blastocyst transfer (SVBT) compared to other, annotation-dependent blastocyst grading systems? Summary answer Automatic embryo ranking by iDAScore shows a higher or equal performance, with regards to pregnancy prediction after SVBT, compared to manual, annotation-dependent grading systems. What is known already Blastocyst viability can be assessed by blastocyst morphology grades and/or morphokinetic parameters. However, morphological and morphokinetic embryo assessment is prone to both inter- and intra-observer variation. Recently, embryo ranking models have been developed based on artificial intelligence (AI) and deep learning. Such models rank embryos according to their potential for pregnancy only based on images and do not require any user-dependent annotation. So far, no study has independently assessed the performance of AI models compared to other embryo scoring models, including traditional morphological grading. Study design, size, duration A total of 3,014 SVBT cycles were retrospectively analysed. Embryos were stratified according to SART age groups. The quality and scoring of embryos were assessed by iDAScore v1.0 (iDAS, Vitrolife, Sweden), KIDScoreTM D5 v3 (KS; Vitrolife), and Gardner criteria. The performance of the pregnancy prediction for each embryo scoring model was compared using the area under curve (AUC) of the receiver operating characteristic curve for each maternal age group. Participants/materials, setting, methods Embryos were cultured in the EmbryoScope+ and EmbryoScopeFlex (Vitrolife). iDAS was automatically calculated using the iDAScore model running on the EmbryoViewer (Vitrolife). KS was calculated in EmbryoViewer after annotation of the required parameters. ICM and TE were annotated according to the Gardner criteria. The degree of expansion in all blastocysts was Grade 4 due to our freezing policy. Furthermore, Gardner’s scores were stratified into four grades (Excellent: AA, Good: AB BA, Fair: BB, Poor: others). Main results and the role of chance The AUCs of the < 35 years age group (n = 389) for pregnancy prediction were 0.72 for iDAS, 0.66 for KS and 0.64 for Gardner criteria. The AUC of iDAS was significantly higher (P < 0.05) compared to the other two models. For the 35–37 years age group (n = 514) the AUCs were 0.68, 0.68, and 0.65 for iDAS, KS and Gardner, respectively, and were not significantly different. The AUCs of the 38–40 years age group (n = 796) were 0.67 for iDAS, 0.65 for KS and 0.64 for Gardner criteria and where was not significantly different. The AUCs of the 41–42 years age group (n = 636) were 0.66, 0.66, and 0.63 for iDAS, KS and Gardner, respectively, and there was no significant difference among the pregnancy prediction models. For the > 42 years age group (n = 389) AUCs were 0.76 for iDAS, 0.75 for KS and 0.75 for Gardner criteria and not significantly different. Thus, for all age groups, iDAS was either highest or equal to the highest AUC, although a significant difference was only observed for the youngest age group. Limitations, reasons for caution In this study, SVBT was performed after minimal stimulation and natural cycle in vitro fertilisation (IVF). Therefore, we had only few cycles with elective blastocyst transfer. However, there was also no bias in selecting the embryos for SVBT. Wider implications of the findings Our results showed that objective embryo assessment by a completely automatic and annotation-free model, iDAScore, does perform as good or even better than more traditional embryo assessment or an annotation-dependent ranking tool. iDAS could be an optimal pregnancy prediction model after SVBT, especially in young and advanced age patients. Trial registration number not applicable