O-122 Reporting on the value of Artificial Intelligence in predicting the optimal embryo for transfer: A systematic review and meta-analysis

Abstract

Abstract Study question Are Artificial Intelligence (AI) based models effective in robustly predicting in vitro fertilization (IVF) outcome by assessing embryo quality? Summary answer The majority of the AI-based models could provide an accurate prediction regarding live birth, clinical pregnancy, clinical pregnancy with fetal heartbeat and embryo ploidy status. What is known already Precision and consistency in embryo quality evaluation are of paramount importance regarding the outcome of an IVF cycle. Numerous embryo grading and evaluation systems, employing morphological and morphokinetical assessment, have been proposed but without reaching a consensus yet. The main limitation of the aforementioned assessment systems is that they depend on human evaluation, which may be subject to subjectivity and interobserver variation. Thus, automated prediction models may be essential to optimize objectivity and reliability of embryo grading. Artificial neural network models may process microscopy images or time-lapse videos as input to predict the embryos’ potential competency. Study design, size, duration A systematic review and meta-analysis including 18 published studies. The population consists of preimplantation embryos suitable for embryo transfer in IVF/ICSI cycles following employment of an AI-based prediction model. The outcome measures are prediction of live birth, clinical pregnancy, clinical pregnancy with heartbeat and ploidy status. Participants/materials, setting, methods A systematic search of the literature was performed in the databases of Pubmed/Medline, Embase, and Cochrane Central Library limited to articles published in English up to August 2021. The initial search yielded a total of 694 studies with 97 of them being duplicates and other 579 being excluded on the grounds of not fulfilling inclusion criteria. Following full-text screening and citation mining a total of 18 studies were identified to be eligible for inclusion. Main results and the role of chance Four studies reported on prediction of live birth. The sensitivity was 70.6% (95%C.I.: 38.1-90.4%) and specificity was 90.6% (95%C.I.:79.3-96.1%). The Area Under the Curve (AUC) of the Summary Receiver Operating Characteristics (SROC) curve was 0.905, while the partial AUC (pAUC) was 0.755. Employing the Bayesian approach, the total Observed:Expected ratio (O:E) was 1.12 (95%CI: 0.26–2.37; 95%PI:0.02-6.54). Ten studies reported on prediction of clinical pregnancy. The sensitivity and the specificity were 71% (95%C.I.: 58.1-81.2%) and 62.5% (95%C.I.: 47.4-75.5%) respectively. The AUC was 0.716, while pAUC was 0.693. Moreover, the total O:E ratio was 0.92 (95%CI: 0.61–1.28; 95%PI:0.13-2.43). Eight studies reported on prediction of clinical pregnancy with fetal heartbeat the sensitivity was 75.2% (95%C.I.: 66.8-82%) and the specificity was 55.3% (95%C.I.: 41.2-68.7%). The AUC was 0.722, while the pAUC was 0.774. The O:E ratio was 0.77 (95%CI: 0.54 – 1.05; 95%PI: 0.21-1.62). Four studies reported on the ploidy status of the embryo. The sensitivity and specificity were 59.4% (95%C.I.: 45.0-73.1%) and 79.2% (95%C.I.: 70.1-86.1%) respectively. The AUC was 0.751 and the pAUC was 0.585. The total O:E ratio was 0.86 (95%CI: 0.42 – 1.27; 95%PI: 0.03-1.83). Limitations, reasons for caution The limited number of studies fulfilling inclusion criteria, along with the different designs applied when developing AI models which may lead to increased heterogeneity, stand as limitations. Inclusion of women regardless of their age presents as another limitation, as advanced maternal age has been associated with diminished IVF outcomes. Wider implications of the findings Albeit, our findings support that AI is a highly promising tool in the era of personalized medicine providing precise predictions it does not appear to considerably surpass human prediction capabilities. More studies and more collaborations between the developers are of paramount importance prior to AI becoming the gold standard. Trial registration number Not applicable