Radiology

Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.

Retinotopic tuning of neural populations is a key organizing principle of human visual cortex. However, state-of-the-art models that predict neural recordings based on task-optimized Convolutional Neural Networks (CNNs) do not take this retinotopic organization into account. Furthermore, while retinotopic tuning in visual cortex has been studied extensively using functional magnetic resonance imag...

Beyond the tumor: Recurrence-prone radiomics for prognostication in negative PSMA PET/CT scans of prostate cancer.

Prostate-specific membrane antigen (PSMA) PET/CT is routinely used to restage prostate cancer (PCa) in patients with biochemical recurrence (BCR), yet negative scans may still harbor subclinical disease. This study investigated whether radiomics features extracted from recurrence-prone organs on negative [¹⁸F]DCFPyL PET/CT can predict clinical progression (CP) and clinical progression-free surviva...

Lung ultrasound in interstitial lung disease: from bedside screening to prognostic integration and AI-assisted standardization.

Interstitial lung diseases (ILDs) require early recognition and longitudinal assessment, yet repeated high-resolution computed tomography (HRCT) is of...

Automated deep learning-based retinoschisis and detachment volume measurement in pathological myopia with posterior scleral contraction.

PURPOSE: This study developed an automated deep learning-based system to quantify retinoschisis and detachment volume (RDV) in pathological myopia (PM...

Region-Specific Evaluation of Plaque Segmentation in Cross-sectional Projections of Carotid Ultrasound Images Using Deep Learning Models in a Sub-clinical Atherosclerosis Cohort.

OBJECTIVE: Detection of atherosclerotic plaque in the carotid arteries is essential for early cardiovascular risk assessment. While B-mode ultrasound ...

[Before the examination: digital check-in].

BACKGROUND: The check-in before imaging is an often underestimated but clinically critical step in the radiological patient journey. In computed tomog...

Hybrid deep learning framework for cardiovascular risk prediction in post-COVID-19 patients.

Cardiovascular complications associated with Post-COVID-19 Patients remain difficult to identify at early stages due to heterogeneous physiological ma...

AI-driven model for early failure prediction after HIFU integrating immediate post-ablation ultrasound and clinical data.

PURPOSE: High-Intensity Focused Ultrasound (HIFU) is an emerging focal therapy for localized prostate cancer, offering an alternative to radical prost...

Clinical artificial intelligence (AI) in Japan: Regulatory pathways, domain-specific evidence, and its data infrastructure from an international perspective.

Artificial intelligence (AI) has advanced rapidly across clinical domains, generating both a growing evidence base and dedicated regulatory frameworks...

FedFound: a federated foundation model for lifespan brain morphological connectome analysis.

The brain morphological connectome derived from structural MRI reflects inter-regional morphological relationships, providing a powerful representatio...

Prediction modeling in transdiagnostic risk: results from the PROCAN study.

Identifying biomarkers for serious mental illnesses (SMI) has significant implications for early intervention and prevention. The current study uses m...

Magnetic resonance imaging-based Alzheimer's disease detection using an EfficientNet-CMSACCN framework.

Alzheimer's Disease (AD) is a degenerative neurological condition characterized by memory loss, cognitive deterioration, and brain tissue shrinkage. D...

Secondary prevention of recurrent ischemic stroke: from guidelines to precision medicine.

Recurrent ischemic stroke remains a major global health challenge, accounting for substantial disability and mortality despite advances in acute manag...

Proteomic signatures as biomarkers of atherosclerosis burden.

AIMS: Atherosclerosis is currently evaluated by imaging, but scalable circulating biomarkers to detect its presence and quantify overall burden are la...

Artificial intelligence in the imaging diagnosis of gallbladder and bile duct stones: a systematic review.

BACKGROUND: Gallbladder and bile duct stones (cholelithiasis and choledocholithiasis) represent a major global health burden. Conventional imaging mod...

Comparative study of radiomics features between ASL and DSC perfusion-weighted imaging in distinguishing radiation-induced brain injury from glioma recurrence.

OBJECTIVE: To compare the radiomics features of pseudocontinuous arterial spin labeling (ASL) and dynamic susceptibility contrast (DSC) perfusion-weig...

A novel deep learning-based grading system for assessing breast arterial calcification on mammograms, as an independent risk factor for predicting adverse cardiovascular events.

PURPOSE: To develop a deep learning-based framework for automated detection and grading of breast arterial calcification (BAC) on mammograms, and to e...

Diagnostic performance of bpMRI versus mpMRI and AI-assisted bpMRI in prostate cancer detection: a multi-reader study.

PURPOSE: To compare biparametric MRI (bpMRI) and multiparametric MRI (mpMRI) for detecting clinically significant prostate cancer (csPCa), and to asse...

A Clinically Interpretable AI System for Real-Time Quality Control of Transthoracic Echocardiography: Development, Validation, and Deployment.

BACKGROUND: Quality control (QC) in echocardiography is crucial but is often subjective, retrospective, and labor-intensive. Artificial intelligence (...

Interpretable machine learning for optimization of ultrasound-assisted polysaccharide extraction using natural deep eutectic solvents: From yield enhancement to structural and antioxidant activity validation.

Traditional methods for polysaccharide extraction are inefficient and environmentally harmful. To develop an environmentally friendly extraction proce...