Radiology

Development and Real-World Evaluation of an NLP-Assisted Quality Control System for Radiology Reports.

Maintaining the quality and consistency of radiology reports has become increasingly challenging with the growing volume of imaging examinations. This study aimed to develop and evaluate an NLP-assisted quality control system for routine radiology reports in a real-world clinical setting. An NLP-assisted quality control module was integrated into the HIS-PACS workflow at Tangdu Hospital for real-t...

Argus-Net: Anatomy-Guided Hierarchical Learning for Pulmonary Vasculature Segmentation in Contrast and Non-Contrast CT.

Automatic segmentation of the pulmonary vasculature from thoracic CT is essential for pulmonary embolism assessment, surgical planning, and disease monitoring. However, accurate segmentation remains challenging due to low contrast, anatomical noise, and multi-scale vascular complexity, particularly in non-contrast-enhanced CT. To address these challenges, we propose an anatomically informed hierar...

VesselVision-Net: Hybrid Global-Local Context Learning for Liver Vessel Segmentation in Hepatic Surgery Preplanning.

Accurate segmentation of liver vessels on contrast-enhanced CT is essential for safe hepatic surgery preplanning. It is still challenging because vess...

Tumor spread through air spaces in non-small-cell lung cancer: 18F-fluorodeoxyglucose PET-based tumoral and peritumoral radiomics analysis with machine learning.

OBJECTIVE: To investigate the utility of tumoral and peritumoral [18F]-fluorodeoxyglucose PET-based radiomics models for predicting tumor spread throu...

The Positive Impact of School Dental Prevention Programs: A Systematic Review and Meta-Analysis.

Although preventable oral diseases are still very common among school-aged children worldwide, dental caries remains a significant public health conce...

Clinical Validation of Artificial Intelligence (AI)-based Cartilage Segmentation Predicting Knee Replacement.

OBJECTIVE: For cartilage morphology to serve as a scalable endpoint in clinical trials, analyses should be performed automatically without human inter...

Deep Learning-Driven Discovery and Engineering of an Efficient PETase for Depolymerization and Detoxification of PET Microplastics Under Physiological Conditions.

Microplastics (MPs) accumulation in ecosystem and human organs poses urgent environmental and health risks, yet few enzymes efficiently degrade polyet...

Integrating artificial intelligence and bioimpedance spectroscopy for enhanced pancreatic disease diagnosis.

The purpose of this study is to develop bioimpedance spectroscopy methods for diagnosing pancreatic diseases. We developed a descriptor approach using...

Artificial Intelligence and Interval Cancers in Screening Mammography.

Interval breast cancers are diagnosed between screening rounds. Because they portend a worse diagnosis than screen-detected cancers, decreasing the in...

Self-supervised multimodal swin UNETR for PET/CT segmentation of diffuse large B-cell lymphoma.

Accurate segmentation of diffuse large B-cell lymphoma (DLBCL) is critical for reliable PET/CT-based quantification and total metabolic tumor volume (...

Connectome-based modeling of the suicidal brain: neurobiological substrates, clinical translation and future perspectives.

Suicide represents a global public health crisis, claiming over 700,000 lives worldwide every year. Deep understanding of the neurobiological mechanis...

Retrieval-Augmented Claude Opus 4.7 and GPT-5.5 Surpass Human Performance on the Nuclear Cardiology Board Preparation Exam (and Claude Drafts a Paper About it).

BACKGROUND: Previous studies evaluated large language model (LLM) performance on the American Society of Nuclear Cardiology (ASNC) Board Preparation E...

Evaluating the impact of segmentation strategies on radiomic feature stability for paediatric brain tumour diagnosis using diffusion weighted imaging.

OBJECTIVES: This research aims to comprehensively assess the impact of both conservatively and extensively delineated Regions of Interest (ROI) on sub...

Deep learning applications in osteosarcoma MRI: A systematic review of recent advances in AI-based osteosarcoma diagnosis.

Primary malignant bone tumours of the skeleton have a great diversity in their biological behaviour, and the most common in adolescence is osteosarcom...

Deep-Learning Based Contrast Boosting: A Multi-Center Multi-Reader Study on Clinical Performance With Standard Contrast Enhanced Brain MRI.

BACKGROUND: Gadolinium-based contrast agents are used in brain MRI to improve the visualization of disorders and improve the delineation of lesions. H...

B-mode ultrasound radiomics of the masseter muscle for differentiating individuals with and without clinically defined probable bruxism.

OBJECTIVES: To determine whether two-dimensional B-mode ultrasound radiomics of the masseter muscle can differentiate individuals with a clinically de...

An explainable biomedical foundation model via large-scale concept-enhanced vision-language pretraining.

Artificial intelligence for medical imaging is required to be accurate and interpretable to clinicians. However, current multimodal biomedical foundat...

Radiomics Integrated with Deep Learning Based on Contrast-Enhanced MRI for Predicting Short-Term Efficacy of Drug-Eluting Beads Transarterial Chemoembolization in Hepatocellular Carcinoma from Multicenter Retrospective Study.

We aimed to develop and validate a predictive model combining radiomics, deep learning, and clinical features for the preoperative prediction of the s...

Integrating Cardiovascular Screening in Lung Cancer Screening.

Cardiovascular risk assessment is a natural extension of lung cancer screening (LCS) because individuals eligible for low-dose computed tomography oft...

Computational wireless indices for the assessment of myocardial ischaemia.

The assessment of myocardial ischaemia is entering a new computational era. Beyond the traditional boundaries of anatomical imaging and invasive fract...