Abstract:Full-grown mouse antral oocytes are classified as surrounding nucleolus (SN) or not-surrounding nucleolus (NSN), depending on the respective presence or absence of a ring of Hoechst-positive chromatin surrounding the nucleolus. In culture, both types of oocytes resume meiosis and reach the metaphase II (MII) stage, but following insemination, NSN oocytes arrest at the two-cell stage whereas SN oocytes may develop to term. By coupling time-lapse bright-field microscopy with image analysis based on particle imag… Show more
“…Once again, this highlights the importance and predictive power of fertilisation events, as reported in recently published studies (Coticchio et al, 2018). Alternatively, the present approach could be extended to the cytoplasmic activity of human unfertilized oocytes, mirroring our previous mouse study (Bui et al, 2017).…”
Research QuestionProgress in artificial intelligence (AI) and advanced image analysis offers unique opportunities to develop novel embryo assessment approaches. In this study, we tested the hypothesis that such technologies can extract and harness novel information derived from cytoplasmic movements of the early human embryo to predict development to blastocyst.DesignIn a proof-of principle study, an artificial neural network (ANN) approach was undertaken to assess retrospectively 230 human preimplantation embryos. After ICSI, embryos were subjected to time-lapse monitoring for 44 hours. For comparison as a standard embryo assessment methodology, a single senior embryologist assessed each embryo to predict development to blastocyst stage (BL) based on a single picture frame taken at 42 hours of development. In the experimental approach, in embryos that developed to blastocyst or destined to arrest (NoBL), cytoplasm movement velocity (CMV) was recorded by time-lapse monitoring during the first 44 hours of culture and analysed with a Particle Image Velocimetry (PIV) algorithm to extract quantitative information. Three main AI approaches, the k-Nearest Neighbor (k-NN), the Long-Short Term Memory Neural Network (LSTM-NN) and the hybrid ensemble classifier (HyEC) were employed to classify the two embryo classes.ResultsBlind operator assessment classified each embryo in terms of ability of development to blastocyst, reaching a 75.4% accuracy, 76.5% sensitivity, 74.3% specificity, 74.3% precision and 75.4% F1 score. After integration of results from AI models together with the blind operator classification, the performance metrics improved significantly, with a 82.6% accuracy, 79.4% sensitivity, 85.7% specificity, 84.4% precision and 81.8% F1 score.ConclusionsThe present study suggests the possibility to predict human blastocyst development at early cleavage stages by detection of CMV and AI analysis. This indicates the importance of the dynamics of the cytoplasm as a novel and valuable source of data to assess embryo viability.
“…Once again, this highlights the importance and predictive power of fertilisation events, as reported in recently published studies (Coticchio et al, 2018). Alternatively, the present approach could be extended to the cytoplasmic activity of human unfertilized oocytes, mirroring our previous mouse study (Bui et al, 2017).…”
Research QuestionProgress in artificial intelligence (AI) and advanced image analysis offers unique opportunities to develop novel embryo assessment approaches. In this study, we tested the hypothesis that such technologies can extract and harness novel information derived from cytoplasmic movements of the early human embryo to predict development to blastocyst.DesignIn a proof-of principle study, an artificial neural network (ANN) approach was undertaken to assess retrospectively 230 human preimplantation embryos. After ICSI, embryos were subjected to time-lapse monitoring for 44 hours. For comparison as a standard embryo assessment methodology, a single senior embryologist assessed each embryo to predict development to blastocyst stage (BL) based on a single picture frame taken at 42 hours of development. In the experimental approach, in embryos that developed to blastocyst or destined to arrest (NoBL), cytoplasm movement velocity (CMV) was recorded by time-lapse monitoring during the first 44 hours of culture and analysed with a Particle Image Velocimetry (PIV) algorithm to extract quantitative information. Three main AI approaches, the k-Nearest Neighbor (k-NN), the Long-Short Term Memory Neural Network (LSTM-NN) and the hybrid ensemble classifier (HyEC) were employed to classify the two embryo classes.ResultsBlind operator assessment classified each embryo in terms of ability of development to blastocyst, reaching a 75.4% accuracy, 76.5% sensitivity, 74.3% specificity, 74.3% precision and 75.4% F1 score. After integration of results from AI models together with the blind operator classification, the performance metrics improved significantly, with a 82.6% accuracy, 79.4% sensitivity, 85.7% specificity, 84.4% precision and 81.8% F1 score.ConclusionsThe present study suggests the possibility to predict human blastocyst development at early cleavage stages by detection of CMV and AI analysis. This indicates the importance of the dynamics of the cytoplasm as a novel and valuable source of data to assess embryo viability.
“…To compare our approach to manual analysis, we presented the same 131 oocyte images to three reproductive biologists during their training period. These biologists had different levels of training experience (i.e., beginner, medium, and good) and their classification accuracy, compared to that of the expert, ranged from 78.7 to 84.5% 19 , quite lower than the accuracy of the automated approach.Fig. 3Supervised data analysis of 131 mouse oocyte images with surrounded (SN) or not surrounded (NSN) chromatin organization.…”
Analysis of biomedical images requires computational expertize that are uncommon among biomedical scientists. Deep learning approaches for image analysis provide an opportunity to develop user-friendly tools for exploratory data analysis. Here, we use the visual programming toolbox Orange (http://orange.biolab.si) to simplify image analysis by integrating deep-learning embedding, machine learning procedures, and data visualization. Orange supports the construction of data analysis workflows by assembling components for data preprocessing, visualization, and modeling. We equipped Orange with components that use pre-trained deep convolutional networks to profile images with vectors of features. These vectors are used in image clustering and classification in a framework that enables mining of image sets for both novel and experienced users. We demonstrate the utility of the tool in image analysis of progenitor cells in mouse bone healing, identification of developmental competence in mouse oocytes, subcellular protein localization in yeast, and developmental morphology of social amoebae.
“…Two main different types of chromatin organization are present in antral mouse oocytes: Surrounded Nucleolus (SN) oocytes, with a ring of Hoechst-positive chromatin that surrounds the nucleolus, and non-surrounded nucleolus (NSN) oocytes, which lack the ring and exhibit a more dispersed chromatin . A recent study reported that 17.4% of NSN oocytes were arrested at the GV-stage compared with SN oocytes, which resumed meiosis completely [33].…”
Psychological stress, which exerts detrimental effects on human reproduction, may compromise the meiotic competence of oocytes. Meiotic resumption, germinal vesicle breakdown (GVBD), is the first milestone to confer meiotic competence to oocytes. In the practice of assisted reproductive technology (ART), the timing for GVBD is associated with the rates of cleavage and blastocyst formation. However, whether chronic stress compromises oocyte competence by influencing GVBD and the underlying mechanisms are unclear. In the present study, a chronic restraint stress (CRS) mouse model was used to investigate the effects of stress on oocyte meiotic resumption, as well as the mechanisms. Following a 4-week chronic restraint stress in female mice, the percentage of abnormal bipolar spindles increased and indicated compromised oocyte competence in the CRS group. Furthermore, we identified a decreased percentage of GVBD and prolonged time of GVBD in the CRS mouse oocytes compared with the control group. CRS simultaneously reduced the expression of cyclin B1 (CCNB1), which represents a regulatory subunit of M-phase/mature promoting factor (MPF). However, MG132, an inhibitor of anaphase-promoting complex/cyclosome (APC/C), could rescue the prolonged time of GVBD and increase the expression level of CCNB1 of oocytes from the CRS mice. Collectively, our results demonstrated that stress disturbed meiotic resumption through APC/C-mediated CCNB1 degradation, thus providing a novel understanding for stress-related oocyte quality decline; moreover, it may provide a non-invasive approach to select high-quality gametes and novel targets for molecular therapy to treat stress-related female infertility.
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