Serum levels of stem cell factor for predicting embryo quality.

We evaluated whether serum stem cell factor (s-SCF) levels just prior to ovulation induction could indicate the ability to develop a top-quality (TQ) blastocyst by day 5. We investigated patients with normal ovarian reserve (NOR), polycystic ovary syndrome (PCOS), diminished ovarian reserve (DOR), or mild endometriosis. Our pilot research suggests a correlation between s-SCF levels and the ability to form TQ blastocysts in patients with mild endometriosis. This significant statistical difference (p < 0.05) was noted between mild endometriosis patients for whom a TQ blastocyst was obtained and those for whom it was not possible, as measured on the 8th day of stimulation and the day of oocyte retrieval. The mean SCF levels in the serum of these women on the 8th day were at 28.07 (± 2.67) pg/ml for the TQ subgroup and 53.32 (± 16.02) pg/ml for the non-TQ subgroup (p < 0.05). On oocyte retrieval day it was 33.47 (± 3.93) pg/ml and 52.23 (± 9.72) pg/ml (p < 0.05), respectively.

Exploring gonadotropin dosing effects on MII oocyte retrieval in ovarian stimulation.

PURPOSE: Ovarian stimulation with gonadotropins is crucial for obtaining mature oocytes for in vitro fertilization (IVF). Determining the optimal gonadotropin dosage is essential for maximizing its effectiveness. Our study aimed to develop a machine learning (ML) model to predict oocyte counts in IVF patients and retrospectively analyze whether higher gonadotropin doses improve ovarian stimulation outcomes. METHODS: We analyzed the data from 9598 ovarian stimulations. An ML model was employed to predict the number of mature metaphase II (MII) oocytes based on clinical parameters. These predictions were compared with the actual counts of retrieved MII oocytes at different gonadotropin dosages. RESULTS: The ML model provided precise predictions of MII counts, with the AMH and AFC being the most important, and the previous stimulation outcome and age, the less important features for the prediction. Our findings revealed that increasing gonadotropin dosage did not result in a higher number of retrieved MII oocytes. Specifically, for patients predicted to produce 4-8 MII oocytes, a decline in oocyte count was observed as gonadotropin dosage increased. Patients with low (1-3) and high (9-12) MII predictions achieved the best results when administered a daily dose of 225 IU; lower and higher doses proved to be less effective. CONCLUSIONS: Our study suggests that high gonadotropin doses do not enhance MII oocyte retrieval. Our ML model can offer clinicians a novel tool for the precise prediction of MII to guide gonadotropin dosing.

Evaluating the risk of endometriosis based on patients' self-assessment questionnaires.

BACKGROUND: Endometriosis is a condition that significantly affects the quality of life of about 10 % of reproductive-aged women. It is characterized by the presence of tissue similar to the uterine lining (endometrium) outside the uterus, which can lead lead scarring, adhesions, pain, and fertility issues. While numerous factors associated with endometriosis are documented, a wide range of symptoms may still be undiscovered. METHODS: In this study, we employed machine learning algorithms to predict endometriosis based on the patient symptoms extracted from 13,933 questionnaires. We compared the results of feature selection obtained from various algorithms (i.e., Boruta algorithm, Recursive Feature Selection) with experts' decisions. As a benchmark model architecture, we utilized a LightGBM algorithm, along with Multivariate Imputation by Chained Equations (MICE) and k-nearest neighbors (KNN), for missing data imputation. Our primary objective was to assess the model's performance and feature importance compared to existing studies. RESULTS: We identified the top 20 predictors of endometriosis, uncovering previously overlooked features such as Cesarean section, ovarian cysts, and hernia. Notably, the model's performance metrics were maximized when utilizing a combination of multiple feature selection methods. Specifically, the final model achieved an area under the receiver operator characteristic curve (AUC) of 0.85 on the training dataset and an AUC of 0.82 on the testing dataset. CONCLUSIONS: The application of machine learning in diagnosing endometriosis has the potential to significantly impact clinical practice, streamlining the diagnostic process and enhancing efficiency. Our questionnaire-based prediction approach empowers individuals with endometriosis to proactively identify potential symptoms, facilitating informed discussions with healthcare professionals about diagnosis and treatment options.

Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data.

Controlled ovarian stimulation is tailored to the patient based on clinical parameters but estimating the number of retrieved metaphase II (MII) oocytes is a challenge. Here, we have developed a model that takes advantage of the patient's genetic and clinical characteristics simultaneously for predicting the stimulation outcome. Sequence variants in reproduction-related genes identified by next-generation sequencing were matched to groups of various MII oocyte counts using ranking, correspondence analysis, and self-organizing map methods. The gradient boosting machine technique was used to train models on a clinical dataset of 8,574 or a clinical-genetic dataset of 516 ovarian stimulations. The clinical-genetic model predicted the number of MII oocytes better than that based on clinical data. Anti-Müllerian hormone level and antral follicle count were the two most important predictors while a genetic feature consisting of sequence variants in the GDF9, LHCGR, FSHB, ESR1, and ESR2 genes was the third. The combined contribution of genetic features important for the prediction was over one-third of that revealed for anti-Müllerian hormone. Predictions of our clinical-genetic model accurately matched individuals' actual outcomes preventing over- or underestimation. The genetic data upgrades the personalized prediction of ovarian stimulation outcomes, thus improving the in vitro fertilization procedure.