O-124 A new artificial intelligence (AI) system in the block: impact of clinical data on embryo selection using four different time-lapse incubators. (30th June 2022)

• Record Type: Journal Article
• Authors: Delestro, F; Nogueira, D; Ferrer-Buitrago, M; Boyer, P; Chansel-Debordeaux, L; Keppi, B; Sanguinet, P; Trebesses, L; Scalici, E; De La Fuente, A; Gómez, E; Pollet-Villard, X; Ruiz-Jorro, M; Boussommier-Calleja, A
• Is Part Of: Human reproduction. Volume 37(2022)Supplement 1
• Publication Date: 2022-06-30
• Subjects: Human reproduction -- Periodicals; 618
• DOI: 10.1093/humrep/deac105.024
• Languages: English
• ISSNs: 0268-1161

Abstract

Abstract: Study question: Can AI algorithms assist embryologists in evaluating embryos from any time-lapse system (TLS) along with clinical data to better predict pregnancy outcomes and reduce time-to-pregnancy? Summary answer: Our algorithm (Embryoly) significantly increases accuracy in predicting clinical pregnancy by 26.9% amongst embryos deemed of fair and good quality when clinical data is included. What is known already: Embryologists routinely use defined morpho-kinetic criteria to decide which embryo to transfer, and yet, many embryos deemed of good quality fail to lead to a pregnancy. Thus, AI algorithms to assist embryologists in objectively selecting the most promising embryos are in demand. To date, several reports indicate that AI algorithms are capable of predicting pregnancy clinical outcomes but to the best of our knowledge they only consider visual data (or together with a small set of clinical features) from individual TLI systems to generate their predictions. Study design, size, duration: A dataset of 6790 embryos (97.82% known clinical pregnancy outcome, 31.47% frozen transfers) from 2519 patients from 11 European fertility centers recorded with 4 different TLS (GERI-Merck, Embryoscope & EmbryoscopePlus-Vitrolife and MIRI-Esco) was used to train and validate Embryoly. Nine out of 93 clinical factors were identified as being the most predictive, including woman age, woman and man BMI and AMH levels. Performances were evaluated on a separate test dataset (393 videos). Participants/materials, setting, methods: Clinical pregnancy outcome was predicted using a 3D convolutional neural network that analyzed up to 5 days of embryo development. The output score was further analyzed considering the clinical features to generate a second clinical score. Both predictions were compared to those of 10 senior embryologists made on the same test dataset (with and without clinical features). Embryo quality was assessed as: poor, fair, good. Unless specified otherwise, McNemar test was used for statistical tests. Main results and the role of chance: Overall accuracy of embryologists in predicting clinical pregnancy based on videos alone was 57.25% (CI 95% : 52.34% - 62.16%) compared to 60.56% (CI 95% : 55.71% - 65.41%) for Embryoly (p = 0.35). When videos were analyzed together with the clinical factors, overall accuracy of embryologists was significantly lower than Embryoly (60.05% [CI 95% : 55.19% - 64.91%] vs 68.19% [CI 95% : 63.57% - 72.82%], p-value=0.015, respectively). Clinical factors significantly increased our accuracy by 7.63% (p-value=0.030). More specifically, Embryoly algorithms fared better in terms of detecting false positives (31.30% vs 19.34%) compared to embryologists, with a specificity of 74.4% vs. 58.6%, respectively. If we consider only embryos of fair and good quality (71.50% of our test dataset) Embryoly's accuracy was 13.52% higher than that of embryologists. This translates into AI having an even better ability to detect false positives for embryos that could be seen as good candidates for transfer (20.28% false positives against 42.70% for the embryologists). Embryoly performs differently across selected TLS when analyzing videos alone, but not when clinical data was also considered (chi2 test, p < 0.001 and 0.5, respectively). Further work will investigate these discrepancies across TLS. Limitations, reasons for caution: As of today, Embryoly's accuracy in predicting the outcome of poor-quality embryos is not different to that of embryologists (79.46% vs 84.96%; p-value=0.19). We are improving this by exposing Embryoly to more "poor quality" embryos, so as to also identify poor quality embryos with unexpected potential for implantation. Wider implications of the findings: Our pioneering findings support the use of AI for a standardized and couple-centered care in clinical embryology, integrating male and female factors with embryo development analyses from multiple TLS. Our approach has the potential to cost-effectively reduce time to pregnancy and is another step toward a personalized embryo transfer strategy. Trial registration number: Not applicable