AI tops density in predicting breast cancer risk
Reports and Proceedings
Updates every hour. Last Updated: 26-Nov-2025 06:11 ET (26-Nov-2025 11:11 GMT/UTC)
An image-only artificial intelligence (AI) model for predicting the five-year risk of breast cancer provided stronger and more precise risk stratification than breast density assessment, according to a new study being presented next week at the annual meeting of the Radiological Society of North America (RSNA).
In an exciting exploration of environmental sustainability, researchers at Zhaoqing University, China, have uncovered groundbreaking insights into the carbon dynamics of waterlogged pond fields. Led by Dr. Guodong Yuan from the Guangdong Provincial Key Laboratory of Eco-Environmental Studies and Low-Carbon Agriculture in Peri-Urban Areas and the Guangdong Technology and Equipment Research Center for Soil and Water Pollution Control, this study, titled "Unveiling Carbon Dynamics in Year-Round Waterlogged Pond Fields: Insights into Soil Organic Carbon Accumulation and Sustainable Management," offers a fresh perspective on how these unique ecosystems can contribute to carbon sequestration and sustainable land management.
Researchers have demonstrated a new approach to building quantum convolutional neural networks (QCNNs) using photonic circuits, paving the way for more efficient quantum machine learning. The method, reported in Advanced Photonics, introduces an adaptive step called “state injection,” allowing the circuit to adjust its behavior based on real-time measurements. Using single photons and integrated quantum photonic processors, the team achieved over 92 percent classification accuracy on simple image patterns, closely matching theoretical predictions. This proof-of-concept shows that QCNNs can be implemented with existing photonic technology and highlights a path toward scalable quantum processors for future applications in AI and data processing.