Infertility is a major global health issue, and while in-vitro fertilization has improved treatment outcomes, embryo selection remains a critical bottleneck. Time-lapse imaging enables continuous, non-invasive monitoring of embryo development, yet most automated assessment methods rely solely on conventional morphokinetic features and overlook emerging biomarkers. Cytoplasmic Strings, thin filamentous structures connecting the inner cell mass and trophectoderm in expanded blastocysts, have been associated with faster blastocyst formation, higher blastocyst grades, and improved viability. However, CS assessment currently depends on manual visual inspection, which is labor-intensive, subjective, and severely affected by detection and subtle visual appearance. In this work, we present, to the best of our knowledge, the first computational framework for CS analysis in human IVF embryos. We first design a human-in-the-loop annotation pipeline to curate a biologically validated CS dataset from TLI videos, comprising 13,568 frames with highly sparse CS-positive instances. Building on this dataset, we propose a two-stage deep learning framework that (i) classifies CS presence at the frame level and (ii) localizes CS regions in positive cases. To address severe imbalance and feature uncertainty, we introduce the Novel Uncertainty-aware Contractive Embedding (NUCE) loss, which couples confidence-aware reweighting with an embedding contraction term to form compact, well-separated class clusters. NUCE consistently improves F1-score across five transformer backbones, while RF-DETR-based localization achieves state-of-the-art (SOTA) detection performance for thin, low-contrast CS structures. The source code will be made publicly available at: https://github.com/HamadYA/CS_Detection.

Infertility is a common reproductive health problem that affects millions of people worldwide, causing social, psychological, physical and economic distress to the ones seeking to conceive [7]. In the coming years infertility rates are projected to grow due to environmental and lifestyle factors [18, 37]. In vitro fertilization (IVF) technology is used to overcome infertility, it involves the fertilization of an egg with sperm in the laboratory, followed by the transfer of the resulting embryos into the patient's uterus. The main challenge of IVF is the selection of the embryos that will be either selected for implantation, frozen (for later implantation) or discarded (if they exhibit undesirable features). This selection is to be performed during the early hours after embryo insemination, typically between three and five days after. During this time, embryos are monitored in time-lapse imaging incubators (TLI), facilitating uninterrupted embryo growth within stable culture conditions. This technology offers a dynamic perspective on in vitro embryonic development, augmenting the clinical effectiveness of IVF [29]. To assess quality, embryologists evaluate different morphological characteristics depending on the embryo development phase.

The last decade has seen an explosion of machine learning applications in healthcare, with mixed and sometimes harmful results despite much promise and associated hype. A significant reason for the reversal in the reported benefit of these applications is the premature implementation of machine learning algorithms in clinical practice. This paper argues the critical need for ‘data solidarity’ for machine learning for embryo selection. A recent Lancet and Financial Times commission defined data solidarity as ‘an approach to the collection, use, and sharing of health data and data for health that safeguards individual human rights while building a culture of data justice and equity, and ensuring that the value of data is harnessed for public good’ (Kickbusch et al., 2021).

Abstract. Artificial intelligence (AI) techniques are starting to be used in IVF, in particular for selecting which embryos to transfer to the woman. AI has the

One in four couples in developing countries is impacted by infertility. About 48.5 million couples experience infertility worldwide. Today, infertility is rapidly becoming an epidemic. In vitro fertilization (IVF) is a technique that helps people facing fertility problems have a baby. Despite IVF's potential, the outcomes are unpredictable. To make matters worse, access to fertility care is abysmal.

An Australian Femtech company with US headquarters in San Francisco announced new technology to help couples get pregnant via artificial intelligence-assisted in vitro fertilization (IVF). Life Whisperer is the fertility arm of Presagen, a global artificial intelligence company. The company, whose US headquarters is in San Francisco, announced in a press release new women's health technology applying artificial intelligence to the IVF embryo selection process. IVF clinics around the world can add an artificial intelligence platform to help doctors select the healthiest embryos with the best chance of success. Embryo selection is an important part of the IVF process, where the healthiest embryos are chosen for implantation.

AI has the potential to revolutionize many areas of healthcare. Radiology, dermatology, and ophthalmology are some of the areas most likely to be impacted in the near future, and they have received significant attention from the broader research community. But AI techniques are now also starting to be used in in vitro fertilization (IVF), in particular for selecting which embryos to transfer to the woman. The contribution of AI to IVF is potentially significant, but must be done carefully and transparently, as the ethical issues are significant, in part because this field involves creating new people. We first give a brief introduction to IVF and review the use of AI for embryo selection. We discuss concerns with the interpretation of the reported results from scientific and practical perspectives. We then consider the broader ethical issues involved. We discuss in detail the problems that result from the use of black-box methods in this context and advocate strongly for the use of interpretable models. Importantly, there have been no published trials of clinical effectiveness, a problem in both the AI and IVF communities, and we therefore argue that clinical implementation at this point would be premature. Finally, we discuss ways for the broader AI community to become involved to ensure scientifically sound and ethically responsible development of AI in IVF.

We know of humans that we consider super-intelligent, but we don't yet know how to engineer that intelligence - or exceed it - in people. But some researchers think that advances in genomic science and machine learning are going to open up new avenues of possibility in that realm, potentially leading to individuals whose cognitive abilities leave the greatest minds of history in the dust. That future of superintelligent humans may be upon us sooner than we think. Consider individuals that we consider the smartest of all time, those like Carl Friedrich Gauss or John von Neumann, says Stephen Hsu, a physicist who is the vice president for research and graduate studies at Michigan State University and an advisor to the genomics researchers at BGI. Hsu is a member of BGI's Cognitive Genomics Lab, a research group that's trying to unlock the genetic codes that account for complex traits like height, susceptibility to conditions like obesity, and - perhaps most controversially - intelligence. Most researchers believe that intelligence is influenced by genes and environment, and when it comes to genes, we think a large number of genetic variants all make very small contributions.