Multi-Task Deep Learning with Dynamic Programming for Embryo Early Development Stage Classification from Time-Lapse Videos

从分裂到决策：利用时间细胞阶段分割预测胚胎可转移性

From Division to Decision: Leveraging Temporal Cell-Stage Segmentation for Embryo Transferability Prediction

该研究提出TransFACT框架，利用时间 lapse 视频中的早期发育阶段信息，通过结合帧级时间特征和阶段级表示，预测胚胎可转移性，优于现有方法。

Stain-free Detection of Embryo Polarization using Deep Learning

Polarization of the mammalian embryo at the right developmental time is critical for its development to term and would be valuable in assessing the potential of human embryos. However, tracking polarization requires invasive fluorescence staining, impermissible in the in vitro fertilization clinic. Here, we report the use of artificial intelligence to detect polarization from unstained time-lapse movies of mouse embryos. We assembled a dataset of bright-field movie frames from 8-cell-stage embryos, side-by-side with corresponding images of fluorescent markers of cell polarization. We then used…

Development and validation of deep learning based embryo selection across multiple days of transfer

This work describes the development and validation of a fully automated deep learning model, iDAScore v2.0, for the evaluation of embryos incubated for 2, 3, and 5 or more days. The model is trained and evaluated on an extensive and diverse dataset including 181,428 embryos from 22 IVF clinics across the world. For discriminating transferred embryos with known outcome (KID), we show AUCs ranging from 0.621 to 0.708 depending on the day of transfer. Predictive performance increased over time and showed a strong correlation with morphokinetic parameters. The model has equivalent performance to K…

Deep learning mediated single time-point image-based prediction of embryo developmental outcome at the cleavage stage

In conventional clinical in-vitro fertilization practices embryos are transferred either at the cleavage or blastocyst stages of development. Cleavage stage transfers, particularly, are beneficial for patients with relatively poor prognosis and at fertility centers in resource-limited settings where there is a higher chance of developmental failure in embryos in-vitro. However, one of the major limitations of embryo selections at the cleavage stage is the availability of very low number of manually discernable features to predict developmental outcomes. Although, time-lapse imaging systems hav…

基于视频显微术的牛胚胎移植能力早期预测

Early prediction of the transferability of bovine embryos from videomicroscopy

针对牛胚胎移植能力早期预测面临的特征区分度低、类别模糊、标注数据少的问题，提出多通路时间多尺度3D卷积神经网络SFR，采用焦点损失训练，在二分类任务中性能优于现有方法。

辅助生殖技术中胚胎分级与妊娠预测的多模态人工智能：综述

Multi-Modal Artificial Intelligence of Embryo Grading and Pregnancy Prediction in Assisted Reproductive Technology: A Review

本文从数据模态视角综述了AI在胚胎分级与妊娠预测中的应用，分析了多模态融合、数据稀缺等挑战，并指出了未来研究方向。