Radiology

The evolution of T1-weighted lesion inpainting tools in patients with brain injury: A scoping review.

Focal brain lesions from Acquired Brain Injuries (ABIs) present as regions of abnormal signal intensity on T1-weighted Magnetic Resonance Imaging (MRI) scans. These can disrupt automated neuroimaging processing algorithms traditionally developed on and for healthy brains. Lesion filling (or inpainting) can replace lesioned image voxels with signal intensities approximating healthy tissue. This cre...

Optic Disc Drusen in Children: An Updated Clinical and Imaging Perspective.

BACKGROUND: The optic disc in a child with optic disc drusen (ODD) frequently mimics the optic disc in a child with papilledema, which may lead to unnecessary invasive investigations. Recent advances in imaging have transformed our understanding of pediatric ODD. METHODS: We reviewed the literature from 2018 to 2025, focusing on pediatric cohorts and novel imaging modalities, including optical coh...

Negligible impact of perifissural nodules in an AI-first reader workflow from UK lung screening trial.

OBJECTIVE: To evaluate the effect of perifissural nodules (PFNs) on radiologist workload within an AI-first reader workflow for lung cancer screening,...

Digital twins to accelerate target identification and drug development for immune-mediated disorders.

Immune-related/mediated disorders (IDs) comprise a very diverse group of diseases affecting millions worldwide. The complexity and heterogeneity of ID...

The role of digital tools and artificial intelligence in supporting antimicrobial stewardship: Study protocol for a systematic review.

INTRODUCTION: Since the post-antibiotic era, there has been significant difficulty in treating infectious diseases due to the increase in antimicrobia...

Artificial intelligence in cancer screening: a narrative review of current evidence and future directions.

INTRODUCTION: Cancer remains one of the leading causes of death worldwide, with outcomes improving significantly when the disease is detected at an ea...

Comparative study of the image quality and radiation dose in paranasal-sinus CT with different tube voltages and reconstruction algorithms.

ObjectivesTo evaluate the application of different tube voltages and image-reconstruction algorithms in paranasal-sinus computed tomography (CT) and o...

Deep learning-based identification of aberrant anterior tibial artery on knee MRI: a brazilian multicenter study.

OBJECTIVE: To develop and validate a deep learning model for the detection of aberrant anterior tibial artery (AATA) on axial T2-weighted knee MRI, gi...

Deep learning model for automated identification of ventrally positioned right hepatic artery in contrast-enhanced computed tomography of pediatric congenital biliary dilatation: development and clinical application.

BACKGROUND: Preoperative identification of a ventrally positioned right hepatic artery (vRHA) is critical in congenital biliary dilatation (CBD), as u...

The inflammation-microvascular axis in prognosis: Integrated FAI and AMR assessment for risk stratification after PCI in NSTEMI patients.

BACKGROUND: Despite successful percutaneous coronary intervention (PCI), patients with non-ST-segment elevation myocardial infarction (NSTEMI) remain ...

Enhancing pulmonary embolism diagnosis: a squeeze-and-attention U-Net for precise detection and segmentation in CT angiography.

OBJECTIVE: Pulmonary embolism (PE) is a life-threatening condition requiring rapid and accurate diagnosis. This study proposes a deep learning-based a...

Effect of PET-MPs exposure on the toxicology of PCOS: a multi-platform computational toxicology investigation.

Polyethylene terephthalate microplastics (PET-MPs) function as endocrine-disrupting agents that interfere with steroidogenesis and folliculogenesis, p...

LLM-powered prostate cancer staging from PSMA-PET/CT reports using PROMISE v2.

Accurate staging of prostate cancer is essential for guiding therapy and predicting outcomes. Prostate-specific membrane antigen (PSMA) PET/CT has bec...

High diagnostic accuracy of a resnet50-based deep learning model for osteochondral lesions of the talus on magnetic resonance imaging.

OBJECTIVES: This study aims to evaluate the diagnostic performance of a ResNet50-based convolutional neural network (CNN) in detecting osteochondral l...

From Passive Sampling to Precision Intervention: A Standardized Radiomics-Driven Workflow for Molecularly Adequate Lung Biopsy.

PURPOSE: To develop and temporally validate a predictive framework for molecular adequacy in computed tomography (CT)-guided transthoracic needle biop...

Deep learning-based computer-aided diagnosis for parotid gland tumors on MRI.

OBJECTIVE: Accurate imaging-based differentiation of benign and malignant parotid tumors is essential for determining appropriate therapy. We investig...

Diagnostic Accuracy of Artificial Intelligence for Predicting MGMT Promoter Methylation in Glioblastoma Using MR Imaging: A Systematic Review.

PURPOSE: Glioblastoma (GBM) is an aggressive brain tumor with poor prognosis. O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation is a ...

HOW GENETICS CHANGED OR WILL CHANGE THE THERAPY IN THYROID NODULES WITH INDETERMINATE CYTOLOGY.

The incidence of thyroid cancer has risen in recent decades, largely due to the widespread use of increasingly sensitive imaging techniques that have ...

MRI-based AI framework for predicting lymph node metastasis and prognosis in rectal cancer after neoadjuvant chemoradiotherapy: a multicenter study.

BACKGROUND: Early prediction of lymph node metastasis (LNM) after neoadjuvant chemoradiotherapy (nCRT) is crucial for improving treatment planning and...

Exploration of a Multimodal Machine Learning Model Integrating Ultrasound and Clinical Indicators for the Diagnosis of Diabetic Peripheral Neuropathy.

OBJECTIVES: Based on ultrasound technology and clinical indicators, this study intends to develop multiple risk prediction models for diabetic periphe...