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@article{209375,
        author = {Ms. KALAISELVI V and Dr. S. POONGODI},
        title = {A Survey on Deep Learning for IVF Live Birth Prediction using Embryological, Imaging Based, Data Driven Approaches with Performance Metrics},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {5},
        pages = {1566-1571},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=209375},
        abstract = {Choosing the embryo most likely to result in a live birth is the focal point of the context of in vitro fertilization. Recently, deep learning strategies have shifted the emphasis from conventional grading toward objective models capable of considering a broad range of variables associated with the prediction of clinical outcomes. The aim of this paper is to review the literature on the use of deep neural networks in the prediction of live birth through the combined analysis of embryological, imaging, and clinical variables. The findings are grouped according to the type of data used for training the model and the architecture of the algorithms. For each work, a brief description of the methodology is given, and the evaluation results are underlined. In particular, models that include both imaging data and clinical information showed the best performance in most of the studies with AUC up to 0.97 and accuracy from 74% to 82% on retrospective data. The main limitations of the current evidence, as well as the directions for further research are discussed.},
        keywords = {Convolutional neural networks (CNN), Recurrent neural networks (RNN), Long short-term memory (LSTM), Vision transformers (ViT), Deep neural networks, Machine learning, Classification algorithms, Predictive modeling, Blastocyst images, Morphokinetic analysis, Image-based prediction},
        month = {October},
        }