مقالات پذیرفته شده در نهمین کنگره بین المللی زیست پزشکی
Effective Use of Artificial Intelligence (AI) in Pre-Implantation Embryo Selection
Effective Use of Artificial Intelligence (AI) in Pre-Implantation Embryo Selection
Mohammad Hossein Madahali,1,*Fateme Keshtparvar,2
1. PhD student of Anatomical Sciences; Isfahan University of Medical Sciences 2. Master student of Anatomical Sciences; Isfahan University of Medical Sciences
Introduction: The integration of artificial intelligence (AI) into pre-implantation embryo selection represents a transformative advancement in assisted reproductive technology (ART). By leveraging sophisticated algorithms, AI offers the potential to enhance the accuracy, objectivity, and consistency of embryo assessment, thereby improving in vitro fertilization (IVF) outcomes. However, the successful clinical adoption of AI-driven embryo selection hinges on several critical factors, including the identification of key AI features that ensure reliable performance, rigorous validation methodologies to confirm consistent results across diverse clinical settings, and the resolution of challenges related to standardizing AI performance across studies. Key AI features such as high-quality image analysis, robust segmentation, deep learning predictive modeling, and integration of clinical data are essential for developing accurate and generalizable embryo selection tools. Equally important is the validation of these AI models through multicenter datasets, expert agreement, and correlation with meaningful clinical outcomes to establish their reliability and clinical utility. Despite promising advancements, significant challenges remain in standardizing AI performance across studies due to data variability, model interpretability issues, ethical and regulatory concerns, and inconsistent study designs. This introduction sets the stage for a comprehensive exploration of these interconnected topics, emphasizing the necessity for collaborative efforts to harness AI’s full potential in improving IVF success rates while ensuring safe, transparent, and equitable clinical application.
Methods: The present study was conducted by reviewing related articles in Web of Science, Scopus, and PubMed databases.
Results: Artificial intelligence (AI) is being used more in pre-implantation embryo selection during in vitro fertilization (IVF) to improve accuracy and results. AI models assess embryo images and clinical information to predict how likely an embryo is to implant, often surpassing traditional assessment methods used by embryologists.
AI offers several benefits in embryo selection. Firstly, it provides objective assessments, helping to eliminate personal biases from embryologists and ensuring consistent evaluations. AI uses automated image analysis through deep learning and convolutional neural networks (CNNs) to standardize the grading of embryos. Secondly, AI's predictive accuracy is noted to be superior in various studies, effectively predicting embryo characteristics and pregnancy success rates. It can analyze images taken at different stages of embryo development or even videos over time, identifying details that may be missed by humans. Additionally, AI tools can be easily integrated into current lab workflows, allowing for routine image analyses without needing major changes in practice.
However, there are limitations to consider. Many AI models are trained on data from a single location, affecting their general reliability. Broader studies across multiple centers are necessary to ensure their effectiveness for diverse populations. The complexity of some AI models can also make their decision-making processes unclear. Efforts are ongoing to improve the transparency of these systems. Moreover, retrospective research might lead to biases, resulting in datasets that do not reflect actual clinical situations.
The clinical impact of AI in embryo selection is significant. AI has the potential to raise IVF success rates by enabling the identification of embryos most likely to implant, decrease the time needed to achieve pregnancy, and enhance the efficiency of clinical operations. It could also standardize embryo selection practices across various clinics, reducing variability in choices made by different specialists. Future efforts should focus on expanding research to include diverse datasets that can better represent varied patient groups, improving how data from different sources is used together, and making AI models easier to understand for practitioners.
Essential features for effective AI in embryo selection include high-quality image analysis that captures detailed embryo characteristics, robust algorithms for accurate feature identification, and deep learning techniques that allow for precise classification of embryo viability. Validation across varied datasets is critical to ensure these models work for all populations and types of clinics. AI should also allow non-invasive assessments of embryo health and should present clear and interpretable results to foster trust in clinical settings.
To achieve reliable AI models in clinical IVF practice, they need to be tested and validated using large, diverse datasets from multiple IVF centers. A step-by-step approach is advised for the development and evaluation of these models, with the results needing to align with those of experienced embryologists. Performance will be measured against real-world outcomes like implantation and successful pregnancies, ensuring that AI systems can be confidently integrated into daily IVF practices.AI models can enhance embryo selection in clinical IVF workflows through rigorous validation, leading to better patient outcomes. However, several challenges hinder the standardization of AI performance in this area.
First, there is a lack of data standardization, as different clinics use varying imaging protocols and data collection methods, impacting the comparability of models. Second, model interpretability is an issue; many AI systems are difficult for clinicians to understand, reducing trust. Ethical and regulatory barriers also pose challenges, with ongoing concerns about bias and privacy.
Additionally, differences in study design and evaluation metrics complicate comparisons, while inconsistent integration of clinical and technical data may affect model efficacy. There is a need for more multicenter, prospective studies to ensure reliability, requiring collaborative efforts to create standardized datasets and protocols.
Conclusion: Artificial intelligence holds significant promise for revolutionizing pre-implantation embryo selection by providing objective, accurate, and reproducible assessments that can enhance IVF outcomes. The development of reliable AI models depends on incorporating key features such as advanced image analysis, robust segmentation, deep learning techniques, and the integration of clinical and morpho kinetic data. Equally critical is the rigorous validation of these models through multicenter datasets, expert consensus, and correlation with clinically meaningful outcomes to ensure consistent performance in diverse real-world settings. However, the path to widespread clinical adoption is challenged by the lack of standardized data, variability in study designs, limited interpretability of AI algorithms, and evolving ethical and regulatory landscapes. Overcoming these obstacles requires coordinated efforts to establish standardized protocols, transparent and explainable AI systems, and comprehensive multicenter validation studies.
In summary, while AI-driven embryo selection is poised to improve the precision and efficiency of IVF treatments, achieving consistent and standardized performance across studies and clinical environments remains essential. Continued collaboration among researchers, clinicians, and regulatory bodies will be key to unlocking the full potential of AI in reproductive medicine, ultimately improving patient outcomes and advancing the field.