An investigation was conducted to determine the influence of two sperm selection modalities, IMSI and ICSI, on the morphokinetics, dynamic development and ploidy status of embryos derived from males with sub‐optimal sperm profiles during IVF program. A total of 209 PGTA‐tested top‐quality blastocysts (IMSI = 129, ICSI = 80) from 84 couples (IMSI = 51, ICSI = 33) were assessed retrospectively. This study found that both IMSI and ICSI yielded comparable embryo morphokinetics, except for the T7, TEB and CC3 parameters (p < 0.05). A significant lower incidence of multinucleation was observed in the IMSI group when compared to the ICSI group (48.8% vs. 71.3%, p = 0.002), while other parameters of embryo development such as direct cleavage, distorted cytoplasmic movement, reverse cleavage and vacuole(s) appearance did not differ (p > 0.05). No differences were noticed in the proportion of generating chromosomally euploid embryos (44.2% vs. 51.3%, p = 0.394, respectively, for IMSI and ICSI). The implementation of IMSI or ICSI in couples with sub‐optimal sperm profiles resulted in embryos with comparatively similar morphokinetics. Furthermore, the incidence of multinucleation at the two‐ to four‐cell stage was lower following the practice of IMSI, although the method did not improve the proportion of gaining euploid embryos.
Objective: Hidden knowledge could be discovered within a large practical data of in vitro fertilization (IVF) practice. In this study, Machine learning-based data mining techniques were utilized to construct a reliable prediction model for clinical pregnancy in IVF. Study Design: A retrospective cohort multicenter study involving 4.570 IVF cycles. All patients underwent fresh embryo transfer at either the cleavage or blastocyst stage between January 2015 and December 2019. The experiment focused on utilizing tree-based classifiers to generate and compare the most effective prediction model that could predict a clinical pregnancy through clinical data. Additionally, each classifier is optimized via a genetic algorithm technique, along with the selection of variables. Results: Both the decision tree and random forest showed similar performance that was much better than the gradient boost. The two superior classifiers achieved a balanced accuracy of roughly 0.62. Additionally, each prediction model was shown to work optimally with different combinations of variables, with some variables being consistently included, such as female age, and some consistently excluded, which provides an insight into the relationship between the variables and each prediction model. Conclusion: Machine learning algorithm remains effective for the purpose of data mining and knowledge extraction in IVF clinical datasets through which a relatively reliable prediction system for clinical pregnancy could be constructed, provided the available data is sufficient.
Background:Luteinizing hormone (LH) supplementation may have beneficial effect on the maturity and fertilizability of oocytes in poor ovarian reserve (POR) and may influence the progesterone level, thus increasing the pregnancy rate. However, previous studies on the effect of LH activity supplementation on poor responders have shown conflicting results. This study aimed to compare the clinical effectiveness of two different forms of gonadotropin (highly purified human menopausal gonadotropin (HP-HMG) vs. recombinant human follicle-stimulating hormone (r-hFSH)-only) in Indonesian population. Methods: Women diagnosed with poor ovarian response who received gonadotropin-releasing hormone (GnRH) antagonist protocol with either HP-HMG or r-hFSH-only were investigated. Women who underwent freeze all cycles, mini stimulation, and natural stimulation were excluded. Multiple logistic regression was performed to assess the effect of follicle-stimulating hormone (FSH) + human chorionic gonadotropin (HCG)-driven LH activity combination in HP-HMG to pregnancy event adjusting for progesterone level, demographic variables, and clinical characteristic variables. Results: A total of 101 subjects in the HP-HMG treatment group and 89 subjects in r-hFSH-only treatment group were involved in the study. There was no significant difference of clinical pregnancy rate between HP-HMG group and r-hFSH-only group (adjusted OR: 0.94, 95% CI: 0.39–2.25; p-value: 0.890). Conclusion: Compared to r-hFSH-only group, combination of FSH + HCG-driven LH activity in HP-HMG group had similar effectiveness in poor responders undergoing in vitro fertilization (IVF) using the antagonist protocol.
Background: The purpose of the current study was to reduce the risk of human bias in assessing embryos by automatically annotating embryonic development based on their morphological changes at specified time-points with convolutional neural network (CNN) and artificial intelligence (AI).
Methods: Time-lapse videos of embryo development were manually annotated by the embryologist and extracted for use as a supervised dataset, where the data were split into 14 unique classifications based on morphological differences. A compilation of homogeneous pre-trained CNN models obtained via TensorFlow Hub was tested with various hyperparameters on a controlled environment using transfer learning to create a new model. Subsequently, the performances of the AI models in correctly annotating embryo morphologies within the 14 designated classifications were compared with a collection of AI models with different built-in configurations so as to derive a model with the highest accuracy.
Results: Eventually, an AI model with a specific configuration and an accuracy score of 67.68% was obtained, capable of predicting the embryo developmental stages (t1, t2, t3, t4, t5, t6, t7, t8, t9+, tCompaction, tM, tSB, tB, tEB).
Conclusion: Currently, the technology and research of artificial intelligence and machine learning in the medical field have significantly and continuingly progressed in an effort to develop computer-assisted technology which could potentially increase the efficiency and accuracy of medical personnel’s performance. Nonetheless, building AI models with larger data is required to properly increase AI model reliability.
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