Machine learning
Research by the University of Surrey has developed an AI system that can predict what a patient's knee X-ray will look like a year in the future, giving doctors and patients a visual forecast of how the condition may develop over time. This technology uses advanced machine learning
Machine learning
A comprehensive understanding of artificial intelligence in generating radiological reports from medical images remains unclear, despite recent advancements in the field. A team of researchers from the University of Geneva is conducting a scoping review to map the existing literature and provide an overview of the current state of AI
Machine learning
Researchers at Umea University have developed a novel approach to improve the effectiveness of Generative Adversarial Networks (GANs) in denoising medical images. The study, published in the journal AI Open, introduces a new loss function that captures complex relationships within images, enabling the GANs to generate high-quality images from noisy
Machine learning
Artificial Intelligence (AI) has emerged as a powerful tool in forensic neuropathology, enabling accurate diagnoses and aiding in post-mortem computed tomography (PMCT) assessments of cause of death. A systematic review by researchers at the University of Rome Tor Vergata highlights the key advances in AI's role in forensic
Machine learning
Researchers from Malatya Turgut Ozal University have developed a hybrid model to detect knee osteoarthritis using deep learning and content-based image retrieval (CBIR) techniques. The novel approach aims to overcome the limitations of existing deep learning models in distinguishing similar disease groups. The study highlights the challenges of increasing class
Machine learning
Research from Shenzhen University has made new findings in the area of Information Technology and Data Privacy. The study, focusing on Continual Learning (CL) and Federated Learning (FL), has introduced a novel framework that combines Graph Convolutional Networks (GCNs) and Vision Transformers (ViTs) with Family-based CL (FCL). This framework, named
Machine learning
Scientists have been working on developing a method to aid medical imaging navigation using artificial intelligence. This technology aims to alleviate the problem of skilled sonographers and technicians being in high demand. The current approach uses a simulation environment, where a synthetic image is generated from a CT scan. However,
Machine learning
Artificial intelligence (AI) applications hold great promise for improving accuracy and efficiency in medical imaging diagnostics, but widespread adoption is progressing slower than expected due to technological, organizational, and regulatory obstacles, as well as user-related barriers. Researchers at the University of Bayreuth conducted a study to identify measures to enable
Machine learning
Siemens Healthineers AG has been issued a patent for an AI-powered computed tomography (CT) system designed to detect pregnancy in patients before undergoing a CT scan. The system involves using a trained machine learning model to analyze a two-dimensional radiograph of the patient and determine whether they are pregnant. If
Machine learning
Researchers from Amsterdam University Medical Center have reported findings from a machine learning challenge aimed at predicting progression-free survival in patients with diffuse large B-cell lymphoma using a baseline F-FDG PET/CT radiomics dataset. While some radiomic-based machine learning models showed better performance than simple linear or logistic regression models,
Machine learning
Researchers from the Indian Institute of Technology (IIT) Jodhpur have proposed a new framework, FLAME, which integrates masked autoencoders and mean-prototypes embedding to improve the accuracy and convergence speed of federated learning for medical imaging tasks. This breakthrough has significant implications for medical diagnostics, particularly in applications where data is
Machine learning
Vall d'Hebron Institute of Oncology's Radiomics Group has developed SALSA, a deep learning-driven tool for the precise and automated detection and monitoring of liver tumors, also known as hepatocellular carcinoma. This innovative tool uses medical images, including computed tomography (CT) scans, to detect and delineate liver
