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Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data

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Abstract

Over the past years, the assisted reproductive technologies (ARTs) have been accompanied by constant innovations. For instance, intracytoplasmic sperm injection (ICSI), time-lapse monitoring of the embryonic morphokinetics, and PGS are innovative techniques that increased the success of the ART. In the same trend, the use of artificial intelligence (AI) techniques is being intensively researched whether in the embryo or spermatozoa selection. Despite several studies already published, the use of AI within assisted reproduction clinics is not yet a reality. This is largely due to the different AI techniques that are being proposed to be used in the daily routine of the clinics, which causes some uncertainty in their use. To shed light on this complex scenario, this review briefly describes some of the most frequently used AI algorithms, their functionalities, and their potential use. Several databases were analyzed in search of articles where applied artificial intelligence algorithms were used on reproductive data. Our focus was on the classification of embryonic cells and semen samples. Of a total of 124 articles analyzed, 32 were selected for this review. From the proposed algorithms, most have achieved a satisfactory precision, demonstrating the potential of a wide range of AI techniques. However, the evaluation of these studies suggests the need for more standardized research to validate the proposed models and their algorithms. Routine use of AI in assisted reproduction clinics is just a matter of time. However, the choice of AI technique to be used is supported by a better understanding of the principles subjacent to each technique, that is, its robustness, pros, and cons. We provide some current (although incipient) and potential uses of AI on the clinic routine, discussing how accurate and friendly it could be. Finally, we propose some standards for AI research on the selection of the embryo to be transferred and other future hints. For us, the imminence of its use is evident, providing a revolutionary milestone that will impact the ART.

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Acknowledgements

Grant # 2017 / 19323-5, 2018 / 24252-2 and 2018 / 19371-2 São Paulo Research Foundation (FAPESP).

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Authors and Affiliations

  1. Laboratory of Applied Mathematics, Department of Biological Sciences, São Paulo State University (UNESP), Campus Assis, Av. Dom Antônio, São Paulo, 2100, Brazil

    Eleonora Inácio Fernandez, André Satoshi Ferreira, Matheus Henrique Miquelão Cecílio, Dóris Spinosa Chéles, Rebeca Colauto Milanezi de Souza & José Celso Rocha

  2. Laboratory of Embryonic Micromanipulation, Department of Biological Sciences, São Paulo State University (UNESP), Campus Assis, Av. Dom Antônio, São Paulo, 2100, Brazil

    Dóris Spinosa Chéles & Marcelo Fábio Gouveia Nogueira

  3. Universidade Estadual Paulista Julio de Mesquita Filho, Assis, São Paulo, Brazil

    José Celso Rocha

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  1. Eleonora Inácio Fernandez
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Glossary

Activation function

Mathematical function that determines the output value of the layer or the node. Used to introduce nonlinearity to the neural networks.

Directed acyclic graph

Type of graph utilized in Bayesian networks aiming to detect the influence of variables has on the solution of a problem.

Hidden layers

Layer(s) between the input and output layers, where the nodes process the input and output signals weighting them accordingly their importance.

Hyperplane

It consists of some decision boundaries that help to classify the information into different classes or categories.

Nodes

The basic information processing unit in a neural network, where inputs coming from entered variables are processed and outputs are released from the node.

Split of dataset

It is the way in which the whole dataset is split into three subsets (i.e., training, validation, and test datasets).

Test

The last step of the training process. After the model is trained and validated, it is subjected to the test dataset (completely different sample from the training and validation data). This process aims to measure the final accuracy of trained AI, since this step is no more supervised as the training step.

Training

It is the first step to train an artificial neural network. It consists on the application of a sample (called training dataset), from which the machine will learn. The training dataset contains the features that characterize the subject to be evaluated and the targets to be predicted.

Validation

The second step of the training process. Using the validation dataset, this step evaluates the model (i.e., the artificial neural network being trained) to ensure that AI training takes place correctly. With a different dataset from the training step, validation ensures that the model is not overfitted.

Weights

Weights are assigned to represent the relative importance of the input coming to the node.

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Fernandez, E.I., Ferreira, A.S., Cecílio, M.H.M. et al. Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data. J Assist Reprod Genet 37, 2359–2376 (2020). https://doi.org/10.1007/s10815-020-01881-9

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