Identifying embryos with the highest likelihood of successful implantation is a critical component of the in vitro fertilization (IVF) process. Visual assessments are limited by the subjectivity of embryologists, making consistent evaluation of embryo health challenging with traditional methods. Recent advances in artificial intelligence (AI)—particularly in computer vision and deep learning—have enabled the automated analysis of embryo morphology images, reducing subjectivity and improving evaluation efficiency. Through an extensive literature search using keywords such as “embryo health assessment” and “artificial intelligence,” the present review focuses on AI-driven approaches for automated embryo evaluation. It examines AI techniques applied to embryo assessment across the early development, blastocyst, and full developmental stages. This review indicated the promising potential of AI technologies in enhancing the precision, consistency, and speed of embryo selection. AI models have been reported to outperform manual evaluations across several parameters, offering promising opportunities to improve success rates and operational efficiency in reproductive medicine. Additionally, this review discusses the current limitations of AI implementation in clinical settings and explores future research directions. Overall, the review provides insight into AI’s growing role in advancing embryo selection and highlights the path toward fully automated evaluation systems in assisted reproductive technology.

A research team demonstrates that combining unmanned aerial vehicle (UAV) multispectral imagery with a three-dimensional radiative transfer model (3D RTM) and machine learning can overcome shadow interference, enabling precise, orchard-scale mapping of leaf and canopy chlorophyll content.

A research team has demonstrated that consumer-grade digital cameras, paired with machine learning, can rapidly and accurately identify waterlogging-tolerant chrysanthemum varieties.

Ever wondered how nature’s waterproof leaves and self-cleaning surfaces could inspire new materials? A recent study in Engineering explores how starch, a common kitchen ingredient, can be used to create advanced superwettable systems for packaging, water treatment, and even food taste enhancement. Discover how this eco-friendly solution is shaping the future of material science!

Looking to improve zinc production? Scientists have developed a new model that can quickly and accurately predict temperatures in industrial roasters using minimal data. This innovation could enhance efficiency and product quality in zinc smelting. Find out how it works and its potential impact in our latest report!

Scientists have developed a pioneering framework that translates human brain activity into editable visual imagery, opening up new possibilities for creative design and human–computer interaction. Named DreamConnect, the system employs a dual-stream diffusion model to directly interpret functional magnetic resonance imaging (fMRI) signals and refine them with natural language instructions. By progressively aligning brain activity with user-directed prompts, the method allows for manipulation of imagined scenes—such as transforming a mental picture of a lake into a vivid sunset. This breakthrough demonstrates the potential of brain-to-image technologies to actively shape human “dreams,” suggesting future applications in design, therapy, and communication.

Middle-ear effusion (MEE)—fluid trapped behind the eardrum—can quietly erode hearing, often without pain or fever. In a breakthrough simulation study, researchers used a finely tuned finite element (FE) model of the human ear to mimic six levels of MEE, from barely present to completely filling the cavity. The results reveal a tipping point: when fluid occupies less than half the middle ear space, hearing loss is minimal, averaging about 3 dB. But once it passes the 50% mark, sound transmission plummets, energy absorbance (EA) rates collapse below 20%, and hearing loss can soar to nearly 46 dB. This “fluid threshold” could sharpen diagnostic accuracy and guide timely treatment.