P-172 Retrospective comparison of deep learning versus logistic regression for selecting the best embryo for transfer

Abstract

Abstract Study question How does the performance of a deep learning model compare to a logistic regression model for embryo ranking? Summary answer Top-ranked embryos via both deep learning and logistic regression showed higher pregnancy rates, but deep learning showed a greater improvement in pregnancy success. What is known already Artificial intelligence (AI) algorithms are now being utilized for embryo selection in IVF. However, there remains a debate about whether interpretable machine learning models are more suitable versus deep learning models, which are often considered “black-box” due to their complexity. Two previously developed models were compared in this study: 1) A deep learning model which utilizes CNNs to automatically analyze a static image of an embryo, and 2) A logistic regression model that incorporates embryo morphology grade (ie 5AB), embryo day (5, 6, or 7), and patient age. Both models were developed using 10,000+ embryo images from 11 U.S. clinics. Study design, size, duration A total of 4543 images and morphology grades of individual embryos were collected prospectively from 870 patients at two U.S. clinics using an embryo image capture software, from January - December 2024. Of these, 406 embryos were transferred. 90% of the transferred embryos were genetically tested. None of these embryos were used for training or testing either AI models. Participants/materials, setting, methods After removing aneuploid embryos, embryos were ranked within each patient’s cohort using both the deep learning and logistic regression models. We then compared pregnancy rates of embryos that were top-ranked in their cohort versus those that were lower-ranked. To reduce bias, we included only patients with multiple viable embryos to choose from and only considered first transfers. Differences in biochemical pregnancy and fetal heartbeat were compared for both approaches. Main results and the role of chance Retrospectively, the top-ranked deep learning embryo was transferred 43% of the time, whereas the top-ranked logistic regression embryo was transferred 76% of the time. Transferring the top-ranked embryo by deep learning was associated with an 8.9% higher pregnancy rate (76.1% vs. 67.2%, p = 0.08) and a 6.2% higher fetal heartbeat (60.0% vs 53.8%, p = 0.38). For logistic regression, the top-ranked embryo selection was associated with a 4.1% higher pregnancy rate (71.3% vs. 67.2%, p = 0.51) and a 4.1% higher fetal heartbeat (56.8% vs 52.7%, p = 0.45). P-values were >0.05 for all comparisons, indicating statistical non-significance. For all comparisons, there were no statistical or clinical differences in the average age of the patients between the two groups, nor were there differences in the average AI score of the top-ranked embryo in the cohort, suggesting that these comparisons did not introduce significant biases. Limitations, reasons for caution As this was a retrospective study, clinical decision making about which embryo to transfer was not influenced by either model rankings. The dataset was limited to two clinics, so further prospective validation is needed. Wider implications of the findings Both deep learning and logistic regression models show promise for selecting the top ranked embryo in a patient’s cohort. The simplicity and interpretability of the logistic regression may allow for faster adoption and clinical trust, while deep learning may further enhance success rates. Trial registration number No

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