Radiology

Interpretation, extrapolation and perturbation of single cells.

Single-cell analyses have transitioned from descriptive atlasing towards inferring causal effects and mechanistic relationships that capture cellular logic. Technological advances and the growing scale of observational and interventional datasets have fuelled the development of machine learning methods aimed at identifying such dependencies and extrapolating perturbation effects. Here, we review a...

The role of artificial intelligence in estimating stroke events in Moyamoya patients: A systematic review and meta-analysis of diagnostic test accuracy.

INTRODUCTION: Moyamoya disease (MMD) and syndrome (MMS) are rare cerebrovascular arteriopathies marked by progressive internal carotid stenosis, fragile collateral networks, and a five-year stroke risk near 10% despite optimal care. Artificial-intelligence (AI) models integrating angiographic, perfusion, and clinical data show promise for risk stratification, but their diagnostic accuracy and clin...

ADHTransNet-based radiomics on multimodal pituitary MRI for non-invasive hormone prediction in children.

BACKGROUND AND OBJECTIVE: Growth hormone deficiency (GHD) and idiopathic central precocious puberty (ICPP) are typically diagnosed through invasive st...

Deep Laplacian Coordinates: End-to-end deeply guided anisotropic diffusion for COVID-19 pulmonary lesion segmentation.

Despite notable advances in deep learning, accurately segmenting lung lesions in computed tomography remains a significant challenge due to the scarci...

Coronary artery segmentation in non-contrast calcium scoring CT images using deep learning.

Precise localization of coronary arteries in Computed Tomography (CT) scans is critical from the perspective of medical assessment of various heart pa...

Imaging Evaluation for Steatotic Liver Disease.

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously known as nonalcoholic fatty liver disease, is the fastest-growing cause o...

Artificial intelligence performance in image-based biliary atresia identification: a systematic review and meta-analysis.

BACKGROUND: Biliary atresia (BA) is a rare condition that can lead to serious health complications. Artificial Intelligence (AI)- based medical imagin...

Research progress and clinical utility of multi-parameter orbital MRI in thyroid-associated ophthalmopathy.

Thyroid-associated ophthalmopathy (TAO), the most common orbital disease in adults, is a specific autoimmune condition closely associated with thyroid...

Preoperative prediction of lymph node metastasis and prognosis in gastric cancer patients: A machine learning model based on 18F-FDG metabolic parameters.

OBJECTIVE: To construct and validate a model for predicting lymph node metastasis (LNMs) of gastric cancer (GC) based on 18F-FDG PET/CT multi-paramete...

Evaluating the efficacy and diagnostic reliability of computer vision artificial intelligence algorithm in automating anatomical classification for second-trimester fetal anomaly ultrasound scans: A prospective cohort study.

OBJECTIVE: To evaluate the diagnostic accuracy and workflow efficiency of BioticsAI-anatomyUNet-0.1-2022 software in identifying 18 standard fetal ana...

Identifying diagnostic neuroimaging biomarkers for adolescent major depressive disorder.

BACKGROUND: The increasing incidence of adolescent depression represents a serious public health concern. Despite clear diagnostic criteria, the wide ...

SupReMix: Supervised contrastive learning for medical imaging regression with mixup.

In medical image analysis, regression plays a critical role in computer-aided diagnosis. It enables quantitative measurements such as age prediction f...

Development and preliminary evaluation of a machine learning model for predicting low birth weight using placental IVIM-MRI and maternal clinical characteristics.

OBJECTIVE: To identify key placental intravoxel incoherent motion (IVIM) MRI parameters and maternal factors associated with low birth weight (LBW), a...

ChatGPT-5-Based large language model analysis versus an FDA-approved AI-CAD system for thyroid nodule ultrasound evaluation.

PURPOSE: Recent advances in multimodal large language models (LLMs) have demonstrated promising potential for medical image analysis, yet their diagno...

Hitting the Bull's AI: Artificial Intelligence-derived Imaging Features and their Association with Outcomes in CT-guided Lung Biopsy, a Retrospective Study.

PURPOSE: This study aims to evaluate whether quantitative imaging features analyzed by an artificial intelligence (AI) tool are associated with succes...

Machine Learning-Enhanced Hyperspectral Raman Imaging for Label-Free Molecular Atlas of Alzheimer's Brain.

Label-free molecular imaging that enables the construction of a molecular atlas of biological tissues is vital for understanding complex physiological...

Game-Changing Advances in Infant Hip Dysplasia Evaluation With Ultrasound.

Developmental dysplasia of the hip (DDH) causes preventable morbidity when diagnosis is delayed. We review advances that address screening gaps: 3-dim...

Radiologist- and computer-based chest imaging quantification at 12 months post transplant correlates with baseline lung allograft dysfunction.

BACKGROUND: Baseline lung allograft dysfunction (BLAD), defined as failure to achieve ≥ 80% predicted spirometry after lung transplant, is associated ...

FDA-Recon: Feature and data alignment reconstruction for sparse-view CBCT.

Cone-beam computed tomography (CBCT) enables real-time three-dimensional imaging for patients, which is of great significance in improving the precisi...

Artificial Intelligence-Enhanced Quantitative 3D Analysis of Distal Radioulnar Ligament Insertion Footprints of the Triangular Fibrocartilage Complex With Interactive Validation.

OBJECTIVES: The distal radioulnar ligaments (DRULs) serve as primary stabilizers to the distal radioulnar joint (DRUJ). Existing cadaveric studies rep...