Summary of Multimodal Learning For Embryo Viability Prediction in Clinical Ivf, by Junsik Kim et al.
Multimodal Learning for Embryo Viability Prediction in Clinical IVF
by Junsik Kim, Zhiyi Shi, Davin Jeong, Johannes Knittel, Helen Y. Yang, Yonghyun Song, Wanhua Li, Yicong Li, Dalit Ben-Yosef, Daniel Needleman, Hanspeter Pfister
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to predicting embryo viability in clinical In-Vitro Fertilization (IVF), aiming to improve the likelihood of a successful pregnancy. The traditional method involves manual evaluation by embryologists, which is time-consuming, costly, and subjective. To overcome these limitations, the authors develop a multimodal model that combines time-lapse video data and Electronic Health Records (EHRs) to predict embryo viability. The primary challenge is effectively integrating these two modalities with inherent differences. The study comprehensively analyzes various modality inputs and integration approaches for their multimodal model. This approach has the potential to enable fast and automated embryo viability predictions at scale for clinical IVF. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers are working on a new way to predict which embryos will work best in a special type of pregnancy called In-Vitro Fertilization (IVF). Right now, doctors have to look at the embryos under a microscope and make a decision based on what they see. This can be time-consuming and not always accurate. The scientists developed a computer program that looks at both video footage of the embryos growing and information from the patient’s medical records. They tested different ways of combining this data and found that their approach could predict which embryo would work best quickly and accurately. |
Keywords
* Artificial intelligence * Likelihood
