bibtext: @InProceedings{ Kim_Multimodal_MICCAI2024,
                   author = { Kim, Junsik and Shi, Zhiyi and Jeong, Davin and Knittel, Johannes and Yang, Helen Y. and Song, Yonghyun and Li, Wanhua and Li, Yicong and Ben-Yosef, Dalit and Needleman, Daniel and Pfister, Hanspeter },
                   title = { { Multimodal Learning for Embryo Viability Prediction in Clinical IVF } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15005 },
                   month = {October},
                   pages = { pending },
                }
abstract: In clinical In-Vitro Fertilization (IVF), identifying the most viable embryo for transfer is important to increasing the likelihood of a successful pregnancy. Traditionally, this process involves embryologists manually assessing embryos’ static morphological features at specific intervals using light microscopy. This manual evaluation is not only time-intensive and costly, due to the need for expert analysis, but also inherently subjective, leading to variability in the selection process. To address these challenges, we develop a multimodal model that leverages both time-lapse video data and Electronic Health Records (EHRs) to predict embryo viability. A key challenge of our research is to effectively combine time-lapse video and EHR data, given their distinct modality characteristic. We comprehensively analyze our multimodal model with various modality inputs and integration approaches. Our approach will enable fast and automated embryo viability predictions in scale for clinical IVF.
title: Multimodal Learning for Embryo Viability Prediction in Clinical IVF
authors: Kim, Junsik Shi, Zhiyi Jeong, Davin Knittel, Johannes Yang, Helen Y. Song, Yonghyun Li, Wanhua Li, Yicong Ben-Yosef, Dalit Needleman, Daniel Pfister, Hanspeter