Radiology

Artificial Intelligence in Tuberculosis Imaging: A Global Bibliometric Analysis of Research Trends and Collaborations.

INTRODUCTION: Tuberculosis (TB), a leading infectious cause of death, remains a global health challenge. Imaging is central to diagnosis and screening, while artificial intelligence (AI) is increasingly applied to chest X-rays (CXR) and computed tomography (CT). However, no bibliometric study has comprehensively mapped publication trends, collaborations, modalities, technological evolution, and em...

Automatic field-of-view planning for magnetic resonance shoulder imaging using Deep Learning.

INTRODUCTION: Accurate prescription of oblique coronal and oblique sagittal field of views (FOV) is essential for diagnostic shoulder MRI. Manual planning is radiographer-dependent, time-consuming, and subject to inter- and intra-operator variability, leading to inconsistent image quality and incomplete coverage. Although deep learning (DL) has advanced automated scan planning in non-oblique plane...

Deep Learning and Machine Learning for Differentiation Between Contrast Extravasation and Hemorrhagic Transformation in Post-Thrombectomy Stroke CT.

BACKGROUND: Mechanical thrombectomy (MT) improves outcomes in acute ischemic stroke (AIS) but often results in hyperdensities on non-contrast CT (NCCT...

Artificial intelligence to improve the detection and risk stratification of acute pulmonary embolism (AID-PE): protocol for a pragmatic quasi-experimental comparator study.

INTRODUCTION: Pulmonary embolism (PE) is a potentially fatal condition requiring timely diagnosis and treatment. CT pulmonary angiography (CTPA) is th...

Reinforcement learning-guided segment anything model for MRI prostate and dominant intraprostatic lesions auto-segmentation.

Objective. Accurate segmentation of the prostate and dominant intraprostatic lesions (DILs) on magnetic resonance imaging (MRI) is important for prost...

Enhancing dental clinical education through AI: insights into post-clinical summaries.

BACKGROUND: Artificial Intelligence (AI) is increasingly being introduced into clinical education, including dentistry, as a supplement to traditional...

Large-scale evaluation of multimodal large language models for pneumothorax detection.

PURPOSE: Pneumothorax requires rapid recognition and accurate interpretation of chest X-rays (CXRs), particularly in acute settings where delays can h...

Radiomics-based differentiation of benign and malignant breast masses on contrast-enhanced mammography: a reproducible workflow using open-source tools.

PURPOSE: To differentiate benign and malignant breast masses by extracting radiomic features from low-energy and recombined contrast-enhanced mammogra...

Diagnosis of Cardiac Amyloidosis on Echocardiography Using Artificial Intelligence.

BACKGROUND: Diagnosing cardiac amyloidosis (CA) on echocardiography can be challenging due to the imaging overlap between CA and more prevalent causes...

Explainable machine learning model based on clinical and radiological features for predicting hematoma expansion or rebleeding after decompressive craniectomy in traumatic brain injury: a bicentric cohort study.

BACKGROUND: Hematoma expansion or rebleeding after decompressive craniectomy (DC) is a critical determinant of poor prognosis in traumatic brain injur...

Foundation models in radiology: a primer for pediatric radiologists.

Foundation models (FMs) are large deep learning models pre-trained on vast heterogeneous datasets through self-supervised learning that are adaptable ...

[Sudden cardiac death in structural heart disease : Update on prevention].

Sudden cardiac death (SCD) remains one of the leading causes of death despite major advances in cardiovascular diagnostics and treatment. Evidence sup...

Simplifying radiology reports with large language models: privacy-compliant open- versus closed-weight models.

OBJECTIVES: Large language models (LLMs) like generative pre-trained transformer (GPT) can simplify radiology reports for medical laypersons, but priv...

Trust in AI-Supported Screening in General Practice Among Urban and Rural Citizens: Cross-Sectional Study.

BACKGROUND: The early detection of diseases is one of the tasks of general practice. Artificial intelligence (AI)-based technologies could be useful f...

Mechanistic signatures of comorbid PTSD with cognitive impairment implicate cortisol-induced neural toxicity.

The men and women who worked in rescue and recovery operations at the 9/11 World Trade Center site are developing cognitive impairment (CI) at mid-lif...

Machine Learning for MRI Classification of Systemic Lupus Erythematous Patients with and without Neuropsychiatric Events.

To provide a useful and practical Machine Learning framework to facilitate the diagnosis of Neuropsychiatric Systemic Lupus Erythematosus (NPSLE) and ...

Development and Evaluation of a Retrieval-Augmented Generation System for Radiology Guidelines.

Large language models (LLMs) have demonstrated remarkable capabilities in processing and generating domain-specific information. Their application in ...

Artificial intelligence for lung cancer: a systematic review of head‑to‑head CT, FDG PET/CT, and multimodal models across screening, staging, and prognosis.

BACKGROUND: Artificial intelligence (AI) has shown increasing potential in lung cancer imaging, particularly in detection, staging, prognosis, and rec...

Linking cortical structure and delirium in the elderly: insights from cohort study and shared genetic risk analysis.

BACKGROUND: This study aimed to assess the association between regional cortical changes measured via baseline magnetic resonance imaging (MRI) and th...

Performance of adult-trained artificial intelligence models in paediatric imaging-a scoping review.

OBJECTIVES: This scoping review aims to evaluate the performance of artificial intelligence (AI) models designed for adults when applied to paediatric...