RNNs gathering information from other data points in a sequence can reliably predict the outcome. Recurrent neural networks (RNN) are designed to define temporal or sequential information. Its architecture enables a reduction in the number of parameters and computations involved in the network. The common type of ANN is the convolutional neural network (CNN) employing spatial and configuration information that processes two dimensional (2D) or 3D images as input. There are several types of ANNs, which are defined by the architecture of the layers. Artificial neural networks (ANNs) are the most commonly employed method of AI. AI prediction models are defined as the development of algorithms, employing big data, with the ability to learn and display intelligent behavior. Īrtificial intelligence (AI) is a broad term including specific sub-areas, such as artificial neural networks (ANNs), deep learning, and machine learning.
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Although the majority of grading systems are detailed and employ a series of morphological parameters, the process of embryo evaluation involves the interpretation and application of these criteria by the clinical embryologist and their respective individualized evaluation. Despite the abundance of proposed embryo grading and evaluation systems buttressed by thorough validation, a consensus has not yet been reached, and research is ongoing to indicate an accurate and universally applied method. A number of national and international societies, associations, and committees on reproductive medicine have proposed standardized criteria for embryo grading. Undoubtedly, a major determining factor for a successful IVF outcome is embryo quality. Outcomes of in vitro fertilization (IVF) depend on multiple parameters and their respective intertwined associations. The reduction in fertility rates in recent years has led to increased implementation of assisted reproduction techniques (ART). While AI models appear marginally more effective, they still have some way to go before they can claim to significantly surpass the clinical embryologists’ predictive competence.
This review attempted to compare between AI and human prediction capabilities, and although studies do not allow for a meta-analysis, this systematic review indicates that the AI-based prediction models perform rather similarly to the embryologists’ evaluations.
According to this data synthesis, the majority of the AI-based prediction models are successful in accurately predicting the IVF outcome regarding live birth, clinical pregnancy, clinical pregnancy with fetal heartbeat, and ploidy status. Regarding clinical pregnancy with fetal heartbeat, the AUC of the SROC was 0.722, while the pAUC was 0.774. Regarding live-birth, the Area Under the Curve (AUC) of the Summary Receiver Operating Characteristics (SROC) was 0.905, while the partial AUC (pAUC) was 0.755. Following a systematic search of the literature in Pubmed/Medline, Embase, and Cochrane Central Library, 18 studies were identified as eligible for inclusion. The study has been registered in PROSPERO (CRD42021242097). This systematic review and data synthesis aims to evaluate and report on the predictive capabilities of AI-based prediction models regarding IVF outcome. Despite the promising existing data, AI cannot yet claim gold-standard status, which serves as the rationale for this study. Artificial intelligence (AI) has been gaining support in the field of in vitro fertilization (IVF).