Embryo ploidy status classification through computer-assisted morphology assessment.

BACKGROUND: Preimplantation genetic testing for aneuploidy has been proven to be effective in determining the embryo's chromosomal or ploidy status. The test requires a biopsy of embryonic cells on day 3, 5, or 6 from which complete information on the chromosomes would be obtained. The main drawbacks of preimplantation genetic testing for aneuploidy include its relatively invasive approach and the lack of research studies on the long-term effects of preimplantation genetic testing for aneuploidy. OBJECTIVE: Computer-assisted predictive modeling through machine learning and deep learning algorithms has been proposed to minimize the use of invasive preimplantation genetic testing for aneuploidy. The capability to predict morphologic characteristics of embryo ploidy status creates a meaningful support system for decision-making before further treatment. STUDY DESIGN: Image processing is a component in developing a predictive model specialized in image classification through which a model is able to differentiate images based on unique features. Image processing is obtained through image augmentation to capture segmented embryos and perform feature extraction. Furthermore, multiple machine learning and deep learning algorithms were used to create prediction-based modeling, and all of the prediction models undergo similar model performance assessments to determine the best model prediction algorithm. RESULTS: An efficient artificial intelligence model that can predict embryo ploidy status was developed using image processing through a histogram of oriented gradient and then followed by principal component analysis. The gradient boosting algorithm showed an advantage against other algorithms and yielded an accuracy of 0.74, an aneuploid precision of 0.83, and an aneuploid predictive value (recall) of 0.84. CONCLUSION: This research study proved that machine-assisted technology perceives the embryo differently than human observation and determined that further research on in vitro fertilization is needed. The study finding serves as a basis for developing a better computer-assisted prediction model.

Genetic algorithm-assisted machine learning for clinical pregnancy prediction in in vitro fertilization.

BACKGROUND: A clinical pregnancy prediction model was developed by implementing machine learning technology that uses a combination of static images and medical data to calculate the outcome of an in vitro fertilization cycle. OBJECTIVE: To provide a system that can accurately and sufficiently assist with decision making that is critical to in vitro fertilization cycles, primarily embryo selection. STUDY DESIGN: Historical medical data, which consist of clinical information and a complete transferred embryo image dataset, of 697 patients who underwent unique in vitro fertilization were collected. Various techniques of machine learning were used, namely decision tree, random forest, and gradient boosting; each technique used the same data configuration for performance comparison and was subsequently optimized using genetic algorithm. RESULTS: A prediction model with a peak accuracy of approximately 65% was achieved. Significant differences in the performances of the 3 selected algorithms were apparent. Nonetheless, additional metric measurements, such as receiver operating characteristic, area under the receiver operating characteristic curve score, accuracy, and loss, suggested that the gradient boosting model performed the best in predicting clinical pregnancy. CONCLUSION: This study served as a stepping stone toward the application of in vitro fertilization prediction models that use machine learning techniques. However, additional validation steps are required to boost the model's performance for its implementation in the clinical setting.

Review of computer vision application in in vitro fertilization: the application of deep learning-based computer vision technology in the world of IVF.

In vitro fertilization has been regarded as a forefront solution in treating infertility for over four decades, yet its effectiveness has remained relatively low. This could be attributed to the lack of advancements for the method of observing and selecting the most viable embryos for implantation. The conventional morphological assessment of embryos exhibits inevitable drawbacks which include time- and effort-consuming, and imminent risks of bias associated with subjective assessments performed by individual embryologists. A combination of these disadvantages, undeterred by the introduction of the time-lapse incubator technology, has been considered as a prominent contributor to the less preferable success rate of IVF cycles. Nonetheless, a recent surge of AI-based solutions for tasks automation in IVF has been observed. An AI-powered assistant could improve the efficiency of performing certain tasks in addition to offering accurate algorithms that behave as baselines to minimize the subjectivity of the decision-making process. Through a comprehensive review, we have discovered multiple approaches of implementing deep learning technology, each with varying degrees of success, for constructing the automated systems in IVF which could evaluate and even annotate the developmental stages of an embryo.