Identification of Viable Embryos Using Deep Learning for Medical Image

Cao, Qiang; Liao, Stephen Shaoyi; Meng, Xiangqian; Ye, Han; Yan, Zhenbin; Wang, Puxi

doi:10.1145/3309129.3309143

Cited by 10 publications

(6 citation statements)

References 18 publications

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“…In a different approach, Chen et al [48], Cao et al [52], VerMilyea et al [57], and Kragh et al [58] used embryo images to train AIs for a classification, based on different grading systems. Chen et al [48] and Kragh et al [58] used the grading system of Gardner and Schoolcraft [38].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

“…They achieved an overall better performance when comparing the two studies [48,58]. VerMilyea et al [57] and Cao et al [52] had the purpose of predicting embryo viability. Despite using the smaller database from Cao et al [52], they achieved a better result when applying the AI technique.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

“…VerMilyea et al [57] and Cao et al [52] had the purpose of predicting embryo viability. Despite using the smaller database from Cao et al [52], they achieved a better result when applying the AI technique. These differences make it clear that the size of the dataset is not always a meaningful factor in obtaining the best accuracy.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

See 2 more Smart Citations

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

Fernandez

Ferreira

Cecílio

et al. 2020

J Assist Reprod Genet

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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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Section: Convolutional Neural Networkmentioning

confidence: 99%

Section: Convolutional Neural Networkmentioning

confidence: 99%

Section: Convolutional Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

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

Fernandez

Ferreira

Cecílio

et al. 2020

J Assist Reprod Genet

View full text Add to dashboard Cite

show abstract

“…They may show different performance in various classification tasks. To validate the performance of ResNet50 in embryo evaluation, we compared the performance of our model with the SVM classifier and the random forest (RF) classifier [31]. Both of the two classifiers also use a small dataset that has 221 images.…”

Section: Deep Classification Modelmentioning

confidence: 99%

Embryo Evaluation Based on ResNet with AdaptiveGA-optimized Hyperparameters

Zhou¹,

Zhou²,

Han³

et al. 2022

Journal of Internet Technology

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<p>In vitro fertilization (IVF) embryo evaluation based on morphology is an effective method to improve the success rate of transplantation. Although convolutional neural networks (CNNs) have made great achievements in many image classifications, there are still great challenges in accurately classifying embryos due to the insufficient samples, interference of exfoliated cells, and inappropriate hyperparameter configuration in the classification network. In this paper, a residual neural network optimized by the adaptive genetic algorithm is proposed to evaluate embryos. Firstly, a novel algorithm for extracting the region of interest (ROI) is embedded in the preprocessing part of the model to eliminate exfoliated cells close to the embryo. Secondly, several kinds of specific transformation methods are established to expand the dataset based on the symmetry of embryos. In addition, an adaptive genetic algorithm is adopted to search for optimal hyperparameters. Experiments on the data set provided by Shanghai General Hospital show that the algorithm has an excellent performance in embryo evaluation. The accuracy of our model is 86.4%, the recall is 88.4%, and the AUC is 0.93. Our results indicated that the proposed model can effectively improve the classification performance of ResNet, and thus achieve the clinic requirements of embryo evaluation.</p> <p> </p>

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“…The continual evolvement of the knowledge and understanding of human embryology creates a massive demand for handling and processing medical data. A promising solution is applying machine learning (ML) systems to provide reliable predictions of ART outcomes [12]. It is confirmed that ML algorithms have an advantage over logistic regression for the prediction of the IVF outcome in assisting fertility specialists to counsel their patients and adjusting the appropriate treatment strategy [13].…”

Section: Introductionmentioning

confidence: 99%