Radiology

FSCL-BC: Federated supervised contrastive learning for breast cancer diagnosis with high sensitivity.

BACKGROUND AND OBJECTIVE: Accurate diagnosis of breast cancer in dense breasts requires expert radiologists to examine multiple ultrasound images per patient. This diagnosis procedure is tedious, time-consuming, and prone to misdiagnosis due to human fatigue. AI-aided diagnosis systems can help alleviate this burden. However, vast amounts of data from multiple hospitals, diverse patient demographi...

Unsupervised multimodal deformable medical image registration based on feature perceptual contrast learning.

Objective.Multimodal medical image registration has extensive applications in clinical diagnosis and is fundamental for a series of medical analysis tasks. However, the presence of modality differences makes the registration process challenging. Existing methods often employ modality-independent feature descriptors that are sensitive to noise, or attempt to bridge differences within networks, whic...

Wavelet Spectrum in a Multi-Channel Network May Reduce Biopsy Rates on Diagnosis of Breast Tumors with BI-RADS Category 4a or Higher.

OBJECTIVES: BI-RADS 4a+ breast tumors have a high rate of unnecessary biopsies due to ambiguous diagnostic features. This study aims to develop and ev...

Automated Machine Learning Frameworks for Radiomics: Comparative Evaluation Study.

BACKGROUND: Automated machine learning (AutoML) frameworks can lower technical barriers for predictive and prognostic model development in radiomics b...

Functional Imaging in Hereditary Endocrine Neoplasms: Evolving Modalities and Clinical Implications.

Hereditary endocrine neoplastic syndromes require structured, lifelong surveillance owing to their multisystem involvement, variable penetrance, and h...

Spinal cord FDG PET uptake in healthy adults: an AI-driven segmentation study.

PURPOSE: 18F-fluorodeoxyglucose (FDG) Positron Emission Tomography (PET)/Computerized Tomography (CT) is an important imaging modality in oncology, bu...

Integrating artificial intelligence into paediatric interventional radiology: a review of emerging applications and future directions.

Artificial intelligence (AI) is heralded to revolutionise healthcare by improving efficiency, personalising care, and enhancing clinical outcomes. Alt...

On the Use and Application of Virtual Reality in Diagnostic Radiology.

Immersive technologies, particularly virtual reality (VR), have significantly advanced since their inception in the mid-twentieth century. In recent y...

A Spatiotemporal Transformer Framework on 4D-CT for Early Prediction of Progressive Pulmonary Fibrosis.

Progressive pulmonary fibrosis (PPF) remains difficult to predict because static imaging may not fully capture regional respiratory motion, ventilatio...

Patient Cognitive Bias in Large Language Model-Supported Health Consultations: Simulation-Based Comparative Study.

BACKGROUND: Large language models (LLMs) are increasingly used by patients for health information and preliminary medical advice. In patient-facing co...

Expeditionary Point-of-Care Ultrasound for Combat and Austere Environments.

BACKGROUND: Point-of-care ultrasound (POCUS) enhances combat survivability, yet civilian standards often fail to address battlefield constraints. This...

Predicting Intramyocardial Hemorrhage Before Reperfusion in STEMI Patients With Intrinsically Explainable Artificial Intelligence.

BACKGROUND: Intramyocardial hemorrhage (IMH) complicates approximately 40% of reperfused ST-segment elevation myocardial infarctions (STEMIs) and is a...

[Optical Coherence Tomography - State of the Art, Case Examples and Future Perspectives].

Optical coherence tomography (OCT) is an essential imaging modality in modern ophthalmology, enabling high-resolution visualization of retinal microst...

Clinical Importance of All the Characteristics of Late Gadolinium Enhancement from Acquisition to Expert and Artificial Intelligence Analysis: State-of-the-Art.

Late gadolinium enhancement (LGE) assessed by cardiovascular magnetic resonance is the cornerstone in the assessment of myocardial tissue characteriza...

Clinical relevance of intracranial stenosis as false-positive findings of a deep learning algorithm trained to detect large vessel occlusions: a retrospective cohort study of a supraregional stroke centre.

OBJECTIVES: This study aims to explore the ability to identify high-grade intracranial arterial stenosis (ICAS) by an artificial intelligence (AI) des...

Multimodal assessment of minimally conscious state and cognitive motor dissociation in neurocritical care: A critical review.

Minimally conscious state (MCS) is characterized by inconsistent but clearly discernible clinical and behavioral evidence of consciousness. Cognitive ...

Benchmarking GPT-5, Gemini 2.5 Pro, Grok 4, and other LLMs on pediatric dentistry questions from a dental specialization exam.

Artificial intelligence (AI), particularly large language models (LLMs), is an increasingly prominent tool in medical and dental education. Trained wi...

A machine learning model integrating two-dimensional ultrasound radiomics and clinical parameters for differentiating membranous nephropathy from IgA nephropathy.

BACKGROUND: Membranous nephropathy (MN) and IgA nephropathy (IgAN) are the two most common primary glomerular diseases in China, with distinct pathoph...

Advancing high-altitude medicine: a model for the future.

With rising global high-altitude travel, occupational exposure, and permanent habitation, the health burden of high-altitude-specific diseases and hig...

Clinical Subgroups and Treatment Outcomes in Idiopathic Normal Pressure Hydrocephalus: Application of Machine Learning-Based Clustering.

BACKGROUND AND OBJECTIVES: The variability in clinical phenotype, radiological features and treatment outcomes of idiopathic normal pressure hydroceph...