Radiology

AI-Based Personalized Therapy With Clinical Intelligence and Radiomics (SPOILS) for Patients With Low Back Pain: Prospective Observational Study.

BACKGROUND: Low back pain (LBP) is a leading cause of disability worldwide, affecting people of all ages while showing increasing prevalence among younger demographics. Patients may present with different symptoms and treatment responses despite identical magnetic resonance imaging results, making it difficult to determine whether surgical and medical interventions are appropriate. OBJECTIVE: This...

CSSL-ISRVN: consistency self-supervised learning integrating ISTANet and sensitivity refinement-enhanced variational network for accelerated MRI reconstruction.

OBJECTIVE: Magnetic resonance imaging is essential in clinical practice due to its non-invasive nature and superior soft-tissue contrast. However, long acquisition times remain a major limitation, leading to motion artifacts and patient discomfort. Most deep learning approaches rely on fully sampled datasets, which are often difficult to obtain. This study aims to develop a self-supervised framewo...

Imaging in the fast lane: Challenges and opportunities for expanding access to MRI.

MRI technology has continuously advanced since its inception in the 1980s, yet access to MRI remains restricted due to its high cost and traditionally...

Rethinking "Useful" and "Useless" AI in radiology.

Artificial Intelligence (AI) is reaching a pivotal moment in radiology, with rapidly expanding applications that often overwhelm clinicians and fuel s...

Computer-aided diagnosis of papillary thyroid carcinoma based on deep learning technology.

This article examines global thyroid cancer（TC） incidence trends, with emphasis on epidemiological patterns in China, where papillary thyroid carcinom...

A lightweight CNN for enhanced non-small cell lung cancer classification using CT scan image.

Lung cancer is a leading cause of cancer-related mortality worldwide, and its early and accurate detection is critical for improving patient outcomes....

Diagnostic accuracy, fairness and clinical implementation of AI for breast cancer screening: results of multicenter retrospective and prospective technical feasibility studies.

Artificial intelligence (AI) promises to enhance breast cancer screening. Here we evaluated Google's mammography AI system (version 1.2) across two ph...

Intratumoral and peritumoral radiomics-based machine learning models for the postoperative survival prediction in esophageal squamous cell carcinoma.

OBJECTIVES: This study aimed to develop and validate machine learning (ML) models to predict survival following oesophagectomy in oesophageal squamous...

Artificial intelligence for detecting orthodontic root resorption: A systematic review and meta-analysis of diagnostic accuracy.

BACKGROUND: External root resorption (ERR) and external cervical resorption (ECR) are common orthodontic complications with prognostic impact. Early, ...

Contralateral language network integration predicts and protects against naming decline after temporal lobe resection.

OBJECTIVE: Anterior temporal lobe resection (ATLR) is an effective treatment for drug-resistant temporal lobe epilepsy (TLE) but carries a substantial...

Hybrid Multi-View MRI Fusion for csPCa Diagnosis via Intra- and Inter-View Transformers.

Accurate diagnosis of clinically significant prostate cancer (csPCa) from multi-view MRI scans (axial, sagittal, and coronal) is essential for effecti...

Quantitative AI Volumetry Profiling Regional Muscle Hypotrophy in Chronic Limb-Threatening Ischemia.

BACKGROUND: Chronic limb-threatening ischemia (CLTI) leads to severe functional impairment and degeneration of lower-limb musculature. Although muscle...

Clinical Impact of Postrecanalization Hemorrhagic Transformation and Its Prediction Using Baseline Noncontrast CT.

BACKGROUND: Hemorrhagic transformation (HT) after recanalization therapy remains a critical concern in acute ischemic stroke management. While severe ...

Hybrid Aquila optimizer-Harris Hawks optimization for CNN hyperparameter tuning in brain tumor classification.

Magnetic resonance imaging (MRI) is hard to categorize properly in terms of interclass similarity, there is data imbalance, and sensitive clinical dec...

Diagnostic Accuracy of Deep Learning for Automated Detection of Spinal Degenerative Disease on MRI: A Systematic Review and Meta-Analysis.

This study aims to estimate the diagnostic accuracy of deep learning (DL) models for automated detection/classification of spinal degenerative disease...

SwinPix: A Swin Transformer-based Pix2Pix Framework for Low-Dose PET Denoising Using Multi-level Inputs Toward Standard-Dose Quality.

This study introduces SwinPix, a novel network architecture designed to explore the effectiveness of multi-level low-dose (LD) PET inputs as prior kno...

Machine learning-based predictive model for the diagnosis of PH pulmonary hypertension diagnosis integrating cardiac magnetic resonance imaging and clinical biomarkers.

Multiphase CT-based deep learning radiomics nomogram models for preoperative WHO/ISUP grading of clear cell renal cell carcinoma: a two-center validation study.

BACKGROUND: Preoperative determination of clear cell renal cell carcinoma (ccRCC) World Health Organization/International Society of Urological Pathol...

Clinical evaluation of a motion correction software based on partial angle reconstruction in coronary CT angiography.

To evaluate a new deep learning (DL) motion correction (MC) software based on partial angle reconstruction (PAR) to reduce motion artifacts in patient...

Comparative assessment of non-invasive imaging methods for coronary artery disease: coronary computed tomography angiography, computed tomography-derived fractional flow reserve and instantaneous wave-free ratio computed tomography.

Coronary artery disease remains a major worldwide health threat, requiring consistent developments in non-invasive diagnostic tools. Coronary Computed...