Deep learning classification integrating embryo images with associated clinical information from ART cycles

Nature·May 21

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Researchers are exploring the use of deep learning classification to improve embryo selection in assisted reproductive technology (art) cycles. Artificial intelligence models are being developed to analyze embryo images and associated clinical information to predict implantation potential and euploidy. These models and frameworks are being studied for their potential to assist in in vitro fertilization procedures, including embryo evaluation and endometrial receptivity assessment.

Full coverage

AI prediction models based on time-lapse imaging for good embryos with implantation potential and euploidy

Nature·Feb 19

Breaking barriers in male infertility: the power of artificial intelligence-driven solutions

Frontiers·Jan 26

Artificial intelligence models and combined scoring approaches for endometrial receptivity assessment in in vitro fertilization

Frontiers·Dec 29

Ensemble deep learning with advanced feature engineering for embryo evaluation on in-vitro fertilisation procedures using biomedical images

Nature·Dec 18

Federated task-adaptive learning for personalized selection of human IVF-derived embryos

Nature·Nov 18

MAIA platform for routine clinical testing: an artificial intelligence embryo selection tool developed to assist embryologists

Nature·Sep 2

Multimodal intelligent prediction model for in vitro fertilization

Nature·Jan 10

A multi-task deep learning and radiomics framework for fetal anatomical structure detection and classification in ultrasound imaging

Nature·Mar 2

View original report at Nature ↗

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