Radiology

Correction: Clinically significant prostate cancer detection with deep learning in a multi-center magnetic resonance imaging study.

BrainFusionNet: a deep learning and XAI model to understand local, global, and sequential features of MRI images for improved brain tumour detection.

The noise of Magnetic Resonance Imaging (MRI) poses challenges for Deep Learning (DL) when tumor boundaries are obscured, tumor location and appearance are complex due to overlap between tumor and non-tumor cells, and modality identification is difficult because tumor features vanish in the later layers of the DL. Effective feature extraction from given MRI is a possible solution to overcome this ...

Image quality improvement of liver ultrasound using unsupervised deep learning.

Chronic liver disease (CLD) and subsequent liver cirrhosis (LC) are common causes of death and healthcare-related socio-economical costs worldwide. Ul...

Cognitive trajectories in early Parkinson's disease: A multimodal MRI study of brain structure and cognitive decline.

Cognitive trajectories in Parkinson's disease (PD) exhibit substantial heterogeneity, ranging from long-term stability to rapid decline. The specific ...

Automated Detection of Pediatric Pneumonia via Clinically Driven AI Analysis of Lung Ultrasound.

Lung ultrasound (LUS) is increasingly utilized for diagnosing pediatric pneumonia due to its bedside accessibility, radiation-free nature, and high di...

Realization of Long Axial Field-of-View PET Technology in the Clinic and Research Environment.

Long axial field-of-view (AFOV) PET-CT instruments have significantly higher sensitivity than conventional PET scanners allowing for reduced scan time...

Optimizing Cine Cardiac MRI: Technical Advances and Clinical Applications.

Cine cardiac magnetic resonance imaging (MRI) is the gold standard for cardiac function assessment, offering exceptional spatial and temporal resoluti...

Predicting Acute Myocardial Infarction in the Absence of Detectable Atherosclerotic Plaque With Perivascular Coronary Inflammation.

BACKGROUND: Coronary computed tomography angiography (CTA) with analysis by artificial intelligence (AI) can personalize coronary artery disease risk ...

Artificial Intelligence-Enabled Analysis of Radiology Reports: Epidemiology and Outcomes of Incidental Thyroid Findings.

CONTEXT: Incidental thyroid findings (ITFs) are increasingly detected on imaging performed for non-thyroid indications. Their prevalence, features, an...

Assessing large language models in radiation risk communication: susceptibility, cultural-linguistic effects, and ethical reasoning.

PURPOSE: To assess the extent to which large language models (LLMs) amplify or attenuate inaccurate or contested narratives in radiation contexts and ...

Deep Learning Reconstruction Versus Hybrid Iterative Reconstruction for Acute Cerebral Infarction Detection on 135 kVp Non-Contrast Brain CT.

PURPOSE: Deep learning reconstruction (DLR) is useful to reduce image noise and improve contrast resolution compared with hybrid iterative reconstruct...

Toward real-time autonomous navigation: transformer-based catheter tip tracking in fluoroscopy.

PURPOSE: Mechanical thrombectomy (MT) improves stroke outcomes, but is limited by a lack of local treatment access. Widespread distribution of reinfor...

A decade of evidence linking gut microbiome dysbiosis to depression: a computational meta-analysis of mechanistic pathways: 2014-2024.

OBJECTIVES: The gut microbiome-gut-brain axis (MGBA) has been associated in the pathophysiology of depression; however, the expanding literature remai...

AI in Point-of-Care Imaging for Clinical Decision Support: Systematic Review of Diagnostic Accuracy, Task-Shifting, and Explainability.

BACKGROUND: Artificial intelligence (AI) integrated with point-of-care imaging is a promising approach to expand access in settings with limited speci...

A methodological assessment of information complementarity between MRI and GAN-based synthetic PET for multimodal medical image analysis.

Multimodal medical imaging aims to enhance analysis by combining complementary anatomical and functional information. However, access to functional mo...

nnLoGoNet: a hybrid local-global network for retinal vessel segmentation with Skeleton Recall Loss.

Retinal vessel segmentation is a fundamental task in ophthalmic image analysis, playing a critical role in disease screening and clinical diagnosis. H...

Artificial intelligence for detecting acute heart failure on chest CT: prospective clinical proof-of-concept validation.

OBJECTIVE: Acute heart failure (AHF) is a common but underrecognized cause of dyspnea. Chest computed tomography (CT) can accurately assess pulmonary ...

Multidimensional Evaluation of AI-Generated Patient Information on Cone Beam-Computed Tomography.

This study aimed to conduct a multidimensional evaluation of artificial intelligence (AI) chatbot-generated patient information regarding cone beam-co...

Interpretable Deep Learning with Multi-Scale CT for Predicting Occult Lymph Node Metastasis in Early-Stage NSCLC: A Multicenter Study.

Accurate preoperative prediction of occult lymph node metastasis (OLNM) in early-stage non-small cell lung cancer (NSCLC) is crucial for treatment pla...

Explainable 3D VGG-style convolutional neural network for pediatric hydrocephalus detection on computed tomography: A segmentation-free and fully volumetric deep learning framework.

Pediatric hydrocephalus is commonly assessed on computed tomography (CT) using manual two-dimensional indices that incompletely reflect the ventricula...