P-618 Artificial intelligence (AI) to optimize ovarian stimulation for IVF: from stimulation protocols to total and mature oocyte yield

Abstract

Abstract Study question Can AI, based on demographic and clinical data, enhance IVF success by choosing the optimal controlled ovarian stimulation (COS) to maximize total and mature oocyte yield? Summary answer Using patient demographics, routine preliminary blood tests and antral follicle count, this AI tool enables personalized COS protocol to optimize total/mature oocyte yield (±3.5 oocytes). What is known already Number of retrieved oocytes and mature oocyte yield are major factors in IVF success. When planning an IVF cycle, doctors decide the stimulation protocol based on certain demographics and basic clinical data. Yet, the treatment recommendations based on these parameters vary globally. AI, an umbrella term for multiple data-driven disciplines, has recently been employed to assist in various stages of the IVF process, particularly in embryo selection for transfer, for COS drug dosing, and for tracking follicular growth to better predict the trigger date. Can AI optimize treatment regimen choice and predict oocyte quantity and quality? Study design, size, duration Training of the AI aimed to build a model capable of predicting mature oocyte yield for a given COS protocol. An initial retrospective, anonymized dataset of 1463 autologous cycles from 757 patients collected between 2017-2021 was reduced to 769 cycles with complete data from 461 patients and used as input for the AI model. A multilayer perceptron deep learning network was implemented 100 times to validate statistical predictions of total/mature oocyte yield. Participants/materials, setting, methods Patients underwent IVF treatment at a private ART center using antagonist (77%), micro-dose Lupron flare (20%) or minimal-stimulation (3%) COS protocols. Demographic data including age, diagnoses, ethnicity, body mass index (BMI), anti-Müllerian hormone (AMH), Day-3 AFC, estradiol, and progesterone levels were used as input along with protocol type. An 80%, 10%, 10% split of the data was used for AI training, validation, and testing, respectively. Mean patient age was 36.8±4.3 years. Main results and the role of chance The AI model predictions were generated from baseline data, representing the clinical scenario prior to the start of a COS cycle. The mean absolute error (MAE) for the number of mature oocytes retrieved per cycle was 3.5. To assess the role of chance, performance was compared to a random model that assigned each true mature oocyte yield to a random patient from the dataset, resulting in an MAE of 6.1. (p < 0.001; Mann-Whitney U test). When predicting total oocyte count, MAE was 4.6 and 7.3 for the AI and random models, respectively (p < 0.001). The Spearman correlation coefficient revealed a strong positive correlation between the actual numbers of mature oocyte retrieved and mean predicted oocyte yield (0.748; p < 0.0001). Using this model, a preliminary AI tool was developed which can predict total and mature oocyte yield based on different planned COS protocols. Limitations, reasons for caution The results were generated from a model trained using retrospective, single-center data. To further validate the model, additional testing is needed using data from different ART centers, with additional COS protocols, and analyses comparing the results of the first COS cycle to the subsequent cycles of the same patient. Wider implications of the findings This oocyte yield prediction model, once validated, can be used by infertility specialists worldwide as a data-driven approach to individualized COS, with the potential to assist cycle programing and optimize lab preparations. Further training with data concerning COS drug dosages and complications, including ovarian hyperstimulation syndrome, will increase clinical application. Trial registration number N/A