To develop and evaluate a femoral neck fracture (FNF) pipeline model for diagnosing fracture stability and aiding surgical decision-making.

To provide a context-aware evaluation of deep learning algorithms for vertebral fracture detection by disentangling subject-level from vertebra-level approaches, quantifying the influence of key technical and methodological factors, and generating evidence to guide task-specific clinical use and standardized reporting. MATERIALS AND METHODS: In this PRISMA‑compliant review (PROSPERO CRD42024523301), five databases were searched to February 2025 for English‑language studies reporting accuracy metrics. Risk of bias was assessed with QUADAS‑AI. Hierarchical summary ROC models pooled sensitivity, specificity, and AUC for each analytical level; subgroup analysis and meta‑regression explored heterogeneity by test‑set origin, imaging modality, and scanner vendor.

To compare long-term efficacy and safety of ultrasound-guided radiofrequency ablation (RFA) and microwave ablation (MWA) in patients with papillary thyroid carcinoma (PTC) in the thyroid danger triangle.

This study evaluates a fully automated deep learning framework to enhance the efficiency and accuracy of cardiac MRI planning.

Access to diagnostic imaging in low- and middle-income countries (LMICs) is limited by scarce equipment, geographic barriers, weak digital infrastructure, and shortages of trained personnel. Artificial intelligence (AI) has emerged as a promising tool to mitigate these gaps by improving diagnostic accuracy, assisting non-specialist health workers, and optimizing workflows. This scoping review aimed to synthesize current evidence on the use of AI for interpreting radiological diagnostic images in LMICs.

Dynamic chest radiography (DCR) is an emerging functional X-ray imaging technique utilizing flat-panel detectors to capture time-resolved radiographic sequences of the entire thorax. By analyzing subtle respiration and cardiac-induced pixel value fluctuations, DCR enables noninvasive, quantitative assessment of pulmonary ventilation, perfusion, and diaphragmatic kinetics without contrast agents. Key applications include evaluating regional ventilation heterogeneity in obstructive diseases (e.g., chronic obstructive pulmonary disease, asthma), detecting perfusion defects in pulmonary embolism, quantifying diaphragmatic dysfunction, and assessing pleural adhesions or lung motion abnormalities. DCR offers high diagnostic performance, portability, low radiation dose, and flexibility in patient positioning. This technique represents a significant advance in functional pulmonary imaging, providing a safe and accessible tool for precision assessment in diverse clinical settings, including critical care. This article elucidates the mechanisms and analytical methodologies of DCR, and comprehensively reviews its clinical applications in respiratory physiology and pathophysiology.

The glymphatic system and neuroinflammation have been implicated in the pathogenesis of Parkinson's disease (PD). This study aimed to investigate the relationships among enlarged perivascular spaces (EPVS) burden, cerebrospinal fluid (CSF) glial fibrillary acidic protein (GFAP) levels, and clinical symptoms in early PD.

Preoperative differentiation of follicular thyroid carcinoma (FTC) from follicular thyroid adenoma (FTA) remains challenging. This study aimed to develop and validate a multimodal prediction model integrating clinical parameters, immunological markers, peritumoral radiomics, and habitat-based features for accurate preoperative FTC diagnosis.

To determine if student exposure to an endovascular simulator can increase confidence performing procedures and interest in interventional radiology (IR) and to assess if past and present video game experience confers improved procedural skills.

This study investigates the efficacy of a deep learning-based artificial intelligence (AI) model in detecting pediatric fractures on musculoskeletal (MSK) radiographs and assesses the impact of AI-assistance on the performance of radiologists.

It remains uncertain whether all patients with oligometastatic non-small cell lung cancer (NSCLC) benefit from the combination of third-generation EGFR-TKIs and TRT. This study aimed to identify which patients are most likely to benefit from combined third-generation EGFR-TKI and TRT, and which patients may safely omit TRT, thereby guiding clinical decision-making and optimizing prognosis.

Detecting Early Ischemic Changes (EIC) on noncontrast computed tomography (NCCT) is essential in patient selection for reperfusion therapy in acute ischemic stroke (AIS). However, identifying these subtle changes remains challenging due to their variable presentation, dependence on reader expertise, and significant interobserver variability. Therefore, an objective method for identifying and quantifying early ischemic brain damage is needed to assist clinicians, particularly in resource-limited settings. Recent advances in artificial intelligence (AI), including machine learning (ML) and deep learning (DL), have opened new opportunities to enhance stroke diagnosis by enabling fast, consistent, and accurate analysis of NCCT images. This review summarizes current AI applications in detecting EIC on NCCT images, focusing on two major developments: (1) the automatic calculation of the Alberta Stroke Program Early Computed Tomography Score (ASPECTS), which facilitates automated tracing of regions of interest (ROIs) and quantification of hypoattenuation to objectively assess ischemic damage, and (2) DL-based EIC detection approaches, supported by large-scale datasets. We highlight the potential of these innovations to complement clinical expertise, streamline workflows, and improve patient outcomes. We discuss the methodologies, performance metrics, and limitations of existing AI models. By synthesizing the latest research, this paper explores AI's transformative role in AIS management and outlines future directions for innovation in this rapidly evolving field.

This study reports outcomes of patients undergoing transarterial radioembolization (TARE) utilizing yttrium-90 (Y90) for androgen-independent prostate cancer liver metastasis.

Spondylolisthesis, a vertebral displacement condition affecting 5-26% of adults, poses a significant health risk to the population. Artificial Intelligence (AI), has emerged as a tool for enhancing diagnostic accuracy. However, the heterogeneity in model performances requires a synthesis of existing evidence.

RATIONALE AND OBJECTIVES: A critical challenge is identifying why some high-risk plaques cause stroke while others remain quiescent. This study aimed to determine if dual-energy CT (DECT) can detect subtle parametric differences in radiologically similar high-risk plaques that may help predict future acute ischemic stroke (AIS) risk.

This study aimed to assess the current multimodal capabilities of leading multimodal large language models (MLLMs) using a 2024 radiology board examination, evaluate their proficiency in utilizing medical image content, compare their performance against human examinees, and consider their cost-effectiveness.