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Revolutionising Osseous Biopsy: The Impact of Artificial Intelligence in the Era of Personalised Medicine.
Br J Radiol
January 2025
2nd Department of Radiology, University General Hospital "ATTIKON", Medical School, National and Kapodistrian University of Athens, Greece.
In a rapidly evolving healthcare environment, artificial intelligence (AI) is transforming diagnostic techniques and personalised medicine. This is also seen in osseous biopsies. AI applications in radiomics, histopathology, predictive modelling, biopsy navigation, and interdisciplinary communication are reshaping how bone biopsies are conducted and interpreted.
View Article and Find Full Text PDFLearning About Their Condition: The Gradual Path of Young People With Childhood-Onset Motor Disabilities.
Acta Paediatr
January 2025
Paediatric Neurology and Neurorehabilitation Unit, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Aim: Young people with childhood-onset motor disabilities face unique challenges in understanding and managing their condition. This study explored how they learnt about their condition.
Method: A descriptive qualitative study was conducted in 2023-2024 at a Swiss paediatric neurorehabilitation unit.
Internet of Things (IoT) is one of the most important emerging technologies that supports Metaverse integrating process, by enabling smooth data transfer among physical and virtual domains. Integrating sensor devices, wearables, and smart gadgets into Metaverse environment enables IoT to deepen interactions and enhance immersion, both crucial for a completely integrated, data-driven Metaverse. Nevertheless, because IoT devices are often built with minimal hardware and are connected to the Internet, they are highly susceptible to different types of cyberattacks, presenting a significant security problem for maintaining a secure infrastructure.
View Article and Find Full Text PDFLearning model combined with data clustering and dimensionality reduction for short-term electricity load forecasting.
Sci Rep
January 2025
Department of Industrial Engineering/Graduate School of Data Science/Research Center for Electrical and Information Science, Seoul National University of Science and Technology, Seoul, South Korea.
Electric load forecasting is crucial in the planning and operating electric power companies. It has evolved from statistical methods to artificial intelligence-based techniques that use machine learning models. In this study, we investigate short-term load forecasting (STLF) for large-scale electricity usage datasets.
View Article and Find Full Text PDFA large synthetic dataset for machine learning applications in power transmission grids.
Sci Data
January 2025
School of Engineering, University of Applied Sciences and Arts of Western Switzerland HES-SO, 1951, Sion, Switzerland.
With the ongoing energy transition, power grids are evolving fast. They operate more and more often close to their technical limit, under more and more volatile conditions. Fast, essentially real-time computational approaches to evaluate their operational safety, stability and reliability are therefore highly desirable.
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