Radiology

MTA-Swin: A Multi-Token Attention Swin Transformer for Brain Tumor Classification with Leakage-Free MRI Benchmarking.

Brain tumors represent a major global health challenge, and accurate classification of brain tumors is essential for effective diagnosis and treatment. Magnetic resonance imaging (MRI) is the most commonly used and reliable modality in early brain tumor detection, and numerous studies have leveraged MRI datasets to train deep learning models for classification. However, many widely adopted brain t...

Detection of rheumatoid arthritis-associated interstitial lung disease: a systematic review and meta-analysis.

BACKGROUND: Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) often has an insidious onset with few or no respiratory symptoms, so early disease may be overlooked. Timely diagnosis and monitoring are therefore crucial. High-resolution computed tomography (HRCT) is the reference standard for RA-ILD, but cost and radiation limit its use as a routine screening tool. Several lower-cos...

Artificial intelligence in the diagnosis of deep vein thrombosis: A scoping review.

Deep vein thrombosis (DVT) is the formation of thrombi in the deep venous system, most often in the lower extremities. Although usually not life-threa...

T1-weighting in Steady-State FLASH MRI-Diffusion Is Not Only Supportive but Mandatory for the Contrast.

PURPOSE: FLASH imaging is widely assumed to produce a T1-weighted steady-state contrast using RF- and gradient-spoiling. We observed substantial overe...

Coronary artery stenosis segmentation using U-Net architecture with customised loss function.

The disorders that affect our heart and blood vessels are cardiovascular disorders, and they are the leading cause of death worldwide. A significant d...

Indocyanine green angiography and machine learning analysis determine topographic distribution of peripheral hard drusen in a Chinese cohort.

BACKGROUND: Hard drusen appear as hyperfluorescent dots on indocyanine green angiography (ICGA) due to their high phospholipid content. This study aim...

Clinical pathways matter for multimodal deep learning in early Alzheimer's disease detection.

Identifying individuals at risk of Alzheimer's disease (AD), particularly in the preclinical and early stages, remains challenging. Although deep lear...

Voice-controlled super-resolution ultrasound imaging and reporting powered by multimodal large language models.

Super-resolution ultrasound imaging (SRUI) surpasses the diffraction limit of conventional ultrasound, enabling visualization of microvascular archite...

Biology-informed risk stratification of glioblastoma by integrating MRI-based intratumoral heterogeneity with clinical features: a multicenter validation study.

BACKGROUND: Refined risk stratification before randomization is clinically important for reducing prognostic imbalance across study arms when evaluati...

Integrating MRI radiomics and transcriptomics to predict IDH mutation status and prognosis in glioma.

OBJECTIVE: To investigate MRI-based radiomic features in glioma and key genes related to IDH mutations, and to analyze their correlation. METHODS: 61 ...

Path beyond the blind end-unravel the imaging spectrum of appendiceal pathologies.

The appendix is involved in a diverse spectrum of inflammatory, infectious, benign, and malignant conditions that extend far beyond acute appendicitis...

Increasing the Reliability of Functional Connectivity by Predicting Long-Scan Functional Connectivity based on Short-Scan Functional Connectivity: Model Exploration, Explanation, Validation, and Application.

Functional connectivity (FC) is a widely used metric in functional magnetic resonance imaging (fMRI) research. However, its reliability has long been ...

Automating standardization of prostate cancer biopsy and histopathology reports with privacy-preserving local large language models.

PURPOSE: Large-scale biomedical analysis in prostate cancer requires structured, tabular datasets, yet most clinical documentation remains in free-tex...

Denoising of ultra-low-dose 15O positron emission tomography images using deep image prior with anatomical information extracted through magnetic resonance segmentation.

PURPOSE: Recent deep-learning methods can recover standard-dose PET images from low-dose images. However, these methods require a large amount of data...

Accelerating the discovery of industrial PET-degrading enzymes: Evolving paradigms from bioprospecting to computational discovery.

Polyethylene terephthalate (PET) waste remains a major environmental and resource challenge, and enzymatic depolymerization offers a promising route f...

Evaluation of PET/CT Artificial Intelligence Image Reconstructions VS Harmonized Clinical Reconstruction.

BACKGROUND: This study focuses on evaluating how SubtlePET™, an artificial intelligence (AI)-based image enhancement algorithm, produced PET/CT images...

Development and multi-institutional validation of a deep learning algorithm for predicting cervical cord compression using dynamic cervical lateral radiographs.

Although magnetic resonance imaging (MRI) is the gold standard for diagnosing degenerative cervical myelopathy (DCM), its cost and limited availabilit...

Validation of aortic valve calcification quantification on contrast-enhanced computed tomography against ex vivo gravimetric analysis: comparison of fixed Hounsfield unit thresholds and deep learning segmentation.

BACKGROUND: Accurate quantification of aortic valve calcification (AVC) on contrast-enhanced computed tomography angiography (CTA) is pivotal for plan...

Validation of MRI-based nnU-Net model for automated segmentation of neck lymph nodes in head and neck squamous cell carcinoma: a multicenter study.

PURPOSE: To develop and externally validate an MRI-based deep learning framework for automated 3D segmentation of neck lymph nodes (LNs) in head and n...

Integrating 2.5D multi-modal CT imaging with serum triglycerides for early risk stratification in hypertriglyceridemia-induced acute pancreatitis: A multicenter study.

OBJECTIVES: To develop and externally validate a clinical-radiological framework that fuses 2.5D deep learning features from dual-phase computed tomog...