A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

Cavalera, Federica; Zanoni, Mario; Merico, Valeria; Bui, Thi Thu Hien; Belli, Martina; Fassina, Lorenzo; Garagna, Silvia; Zuccotti, Maurizio

doi:10.3791/56668

Cited by 15 publications

(13 citation statements)

References 13 publications

(7 reference statements)

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“…The A.I. application in reproductive medicine has focused mainly on oocytes evaluation and selection ( 43 ), sperm analysis and selection ( 44 ), and embryo selection ( 45 ). A few studies have attempted to establish models for IVF outcome prediction ( 23 , 46 ).…”

Section: Discussionmentioning

confidence: 99%

Using Deep Learning in a Monocentric Study to Characterize Maternal Immune Environment for Predicting Pregnancy Outcomes in the Recurrent Reproductive Failure Patients

et al. 2021

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Recurrent reproductive failure (RRF), such as recurrent pregnancy loss and repeated implantation failure, is characterized by complex etiologies and particularly associated with diverse maternal factors. It is currently believed that RRF is closely associated with the maternal environment, which is, in turn, affected by complex immune factors. Without the use of automated tools, it is often difficult to assess the interaction and synergistic effects of the various immune factors on the pregnancy outcome. As a result, the application of Artificial Intelligence (A.I.) has been explored in the field of assisted reproductive technology (ART). In this study, we reviewed studies on the use of A.I. to develop prediction models for pregnancy outcomes of patients who underwent ART treatment. A limited amount of models based on genetic markers or common indices have been established for prediction of pregnancy outcome of patients with RRF. In this study, we applied A.I. to analyze the medical information of patients with RRF, including immune indicators. The entire clinical samples set (561 samples) was divided into two sets: 90% of the set was used for training and 10% for testing. Different data panels were established to predict pregnancy outcomes at four different gestational nodes, including biochemical pregnancy, clinical pregnancy, ongoing pregnancy, and live birth, respectively. The prediction models of pregnancy outcomes were established using sparse coding, based on six data panels: basic patient characteristics, hormone levels, autoantibodies, peripheral immunology, endometrial immunology, and embryo parameters. The six data panels covered 64 variables. In terms of biochemical pregnancy prediction, the area under curve (AUC) using the endometrial immunology panel was the largest (AUC = 0.766, accuracy: 73.0%). The AUC using the autoantibodies panel was the largest in predicting clinical pregnancy (AUC = 0.688, accuracy: 78.4%), ongoing pregnancy (AUC = 0.802, accuracy: 75.0%), and live birth (AUC = 0.909, accuracy: 89.7%). Combining the data panels did not significantly enhance the effect on prediction of all the four pregnancy outcomes. These results give us a new insight on reproductive immunology and establish the basis for assisting clinicians to plan more precise and personalized diagnosis and treatment for patients with RRF.

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Section: Discussionmentioning

confidence: 99%

Using Deep Learning in a Monocentric Study to Characterize Maternal Immune Environment for Predicting Pregnancy Outcomes in the Recurrent Reproductive Failure Patients

et al. 2021

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“…The hereby work focuses on the use of deep learning methods. Approaches to automatic classification of oocytes and embryos involving this kind of methods are known in the literature [ 14 , 22 , 32 , 33 ]. This approach consists of providing a picture to the network which then classifies and assigns the picture to a given quality group.…”

Section: Methodsmentioning

confidence: 99%

“…Another research also relates to the appearance of oocytes. For instance, Cavalera et al [ 14 ] combine time-lapse analysis with image anemometry and with use of artificial neural network to determine the movement of cytoplasm in maturing mouse oocytes, thus determining also their development potential with 91.03 accuracy. Research studies are also underway to develop a method for detecting embryos in the image.…”

Section: Introductionmentioning

confidence: 99%

Semantic segmentation of human oocyte images using deep neural networks

et al. 2021

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BackgroundInfertility is a significant problem of humanity. In vitro fertilisation is one of the most effective and frequently applied ART methods. The effectiveness IVF depends on the assessment and selection of gametes and embryo with the highest developmental potential. The subjective nature of morphological assessment of oocytes and embryos is still one of the main reasons for seeking effective and objective methods for assessing quality in automatic manner. The most promising methods to automatic classification of oocytes and embryos are based on image analysis aided by machine learning techniques. The special attention is paid on deep neural networks that can be used as classifiers solving the problem of automatic assessment of the oocytes/embryos.MethodsThis paper deals with semantic segmentation of human oocyte images using deep neural networks in order to develop new version of the predefined neural networks. Deep semantic oocyte segmentation networks can be seen as medically oriented predefined networks understanding the content of the image. The research presented in the paper is focused on the performance comparison of different types of convolutional neural networks for semantic oocyte segmentation. In the case study, the merits and limitations of the selected deep neural networks are analysed.Results71 deep neural models were analysed. The best score was obtained for one of the variants of DeepLab-v3-ResNet-18 model, when the training accuracy (Acc) reached about 85% for training patterns and 79% for validation ones. The weighted intersection over union (wIoU) and global accuracy (gAcc) for test patterns were calculated, as well. The obtained values of these quality measures were 0,897 and 0.93, respectively.ConclusionThe obtained results prove that the proposed approach can be applied to create deep neural models for semantic oocyte segmentation with the high accuracy guaranteeing their usage as the predefined networks in other tasks.

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“…They reached an excellent result to forecast treatment failure with almost a 90% probability rate. Recently, (Cavalera et al 2018) developed a novel classification method. They predicted the developmental ability of mouse gametes using various models.…”

Section: Overview Of the Ai In Reproductive Medicinementioning

confidence: 99%

“…Thus, the use of AI methods for oocyte selection in IVF programs may bring new opportunities. Cavalera et al (2018) observed mouse oocytes during their in vitro maturation from the germinal vesicle (GV) to the metaphase II stage and took pictures for time-lapse analysis. They calculated the profile of cytoplasmic movement velocities by analyzing the images using the particle image velocimetry (PIV) method, and then the data were analyzed with a feed-forward artificial neural network to identify the competent or incompetent oocytes with an accuracy of 91.03%.…”

Section: Ai Applications In Reproductive Medicinementioning

confidence: 99%

Artificial intelligence in reproductive medicine

et al. 2019

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Artificial intelligence (AI) has experienced rapid growth over the past few years, moving from the experimental to the implementation phase in various fields, including medicine. Advances in learning algorithms and theories, the availability of large datasets and improvements in computing power have contributed to breakthroughs in current AI applications. Machine learning (ML), a subset of AI, allows computers to detect patterns from large complex datasets automatically and uses these patterns to make predictions. AI is proving to be increasingly applicable to healthcare, and multiple machine learning techniques have been used to improve the performance of assisted reproductive technology (ART). Despite various challenges, the integration of AI and reproductive medicine is bound to give an essential direction to medical development in the future. In this review, we discuss the basic aspects of AI and machine learning, and we address the applications, potential limitations and challenges of AI. We also highlight the prospects and future directions in the context of reproductive medicine.

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A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

Cited by 15 publications

References 13 publications

Using Deep Learning in a Monocentric Study to Characterize Maternal Immune Environment for Predicting Pregnancy Outcomes in the Recurrent Reproductive Failure Patients

Using Deep Learning in a Monocentric Study to Characterize Maternal Immune Environment for Predicting Pregnancy Outcomes in the Recurrent Reproductive Failure Patients

Semantic segmentation of human oocyte images using deep neural networks

Artificial intelligence in reproductive medicine

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