Abstract
Appropriate ovarian responses to the controlled ovarian stimulation strategy is the premise for a good outcome of the in vitro fertilization cycle. With the booming of artificial intelligence, machine learning is becoming a popular and promising approach for tailoring a controlled ovarian stimulation strategy. Nowadays, most machine learning-based tailoring strategies aim to generally classify the controlled ovarian stimulation outcome, lacking the capacity to precisely predict the outcome and evaluate the impact features. Based on a clinical cohort composed of 1365 women and two machine learning methods of artificial neural network and supporting vector regression, a regression prediction model of the number of oocytes retrieved is trained, validated, and selected. Given the proposed model, an index called the normalized mean impact value is defined and calculated to reflect the importance of each impact feature. The proposed models can estimate the number of oocytes retrieved with high precision, with the regression coefficient being 0.882% and 89.84% of the instances having the prediction number ≤ 5. Among the impact features, the antral follicle count has the highest importance, followed by the E2 level on the human chorionic gonadotropin day, the age, and the Anti-Müllerian hormone, with their normalized mean impact value > 0.3. Based on the proposed model, the prognostic results for ovarian response can be predicted, which enables scientific clinical decision support for the customized controlled ovarian stimulation strategies for women, and eventually helps yield better in vitro fertilization outcomes.
Highlights
Since the first successful in vitro fertilization–embryo transfer (IVF-ET) in 1978, humanassisted reproductive technology (ART) has made rapid progress in the past decades and helped many infertile couples obtain offspring
The 14 potential impact features that have a close relationship with the controlled ovarian stimulation (COS) process are considered when building the proposed models, which are the age, infertility type, infertility duration, BMI, as the correlation coefficient (AFC), bFSH, E2, AMH, infertility cause, therapeutic regimen, days of Gn, dosage of Gn, E2 level on the human chorionic gonadotropin (HCG) day, and the number of oocytes retrieved
Given the two trained models, we can get the predicted oocytes retrieving number and compare them to the actual value, with the results shown in Figure 3A,B, respectively
Summary
Since the first successful in vitro fertilization–embryo transfer (IVF-ET) in 1978, humanassisted reproductive technology (ART) has made rapid progress in the past decades and helped many infertile couples obtain offspring. The success of IVF is related to the laboratory culture conditions and the personnel operation [1,2] but is closely related to the quality and quantity of oocytes. The ovarian response to controlled ovarian stimulation (COS) is an essential factor, as the retrieving of multiple oocytes stimulated by gonadotropin (Gn) is the fundamental operation for the high-quality embryo’s formation, selection, and transfer in a successful ART process. Studies revealed that the retrieval of 15–18 oocytes could yield the optimal IVF outcomes [7]; tailoring the COS strategy for a target oocyte number is meaningful, which can be accomplished by customizing treatment strategies, such as the ovarian stimulation regimens, types of drugs, and the dosages. The full potential of the ovary is expressed only when a large amount of Gn is applied, but this condition normally does not occur to avoid life-threatening complications, such as the severe ovarian hyperstimulation syndrome (OHSS) [7,8]
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