Radiology

Lesion stratification with intracoronary imaging.

Intracoronary (IC) imaging-guided percutaneous coronary intervention (PCI) improves clinical outcomes in patients with high clinical and anatomical risk when compared to interventions guided by angiography alone. Recent Class I recommendations for the use of IC imaging guidance when performing PCI in left main stem or complex lesions may result in a significant uptake as the technology is embraced...

Artificial Intelligence-assisted Risk Stratification of Thyroid Nodules with Atypia of Undetermined Significance.

OBJECTIVE: Given the limitations of conventional approaches in managing indeterminate thyroid nodules, there remains an unmet need for non-invasive assistant tools to improve risk stratification. This study aimed to evaluate the clinical applicability of an artificial intelligence (AI) model for thyroid nodules with atypia of undetermined significance (AUS) cytology. METHODS: We retrospectively an...

Deep-learning pipeline for automated skeletal muscle segmentation and sarcopenia detection.

BACKGROUND: Sarcopenia, characterized by progressive skeletal muscle loss, is associated with poor outcomes in various diseases. Traditional methods f...

Attenuation-based ultra-low-dose lung computed tomography at 0.1 mSv to 0.3 mSv effective dose in children.

BACKGROUND: Radiation dose reduction is essential in paediatric lung computed tomography (CT). Advances in energy-integrating detector CT and deep-lea...

Hybrid imaging-clinical model for predicting microvascular invasion in hepatocellular carcinoma using deep learning-derived features from CT.

The role of adipose tissue in predicting microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) remains unclear. This study prop...

Model uncertainty estimates for deep learning mammographic density prediction using ordinal and classification approaches.

Mammographic density is associated with the risk of developing breast cancer and can be predicted using deep learning methods. Model uncertainty estim...

OCT-Derived Virtual Fractional Flow Reserve Associated With 1-Year Outcomes After PCI in ACS Patients.

BACKGROUND: Despite PCI, many acute coronary syndrome (ACS) patients experience major adverse cardiovascular events (MACE). Angiography is limited, an...

Deep visual detection system for oral squamous cell carcinoma.

BACKGROUND: Oral Squamous Cell Carcinoma (OSCC) is a widespread and aggressive malignancy where early and accurate detection is essential for improvin...

Multiparametric MRI radiomics nomogram predicts synchronous distant metastasis in rectal cancer.

This study aimed to evaluate the utility of a multiparametric MRI-based radiomics nomogram for identifying patients with rectal cancer (RC) at high ri...

Unlocking the silent signals: Motor kinematics as a new frontier in early detection of mild cognitive impairment.

The increasing global prevalence of mild cognitive impairment (MCI) necessitates a paradigm shift in early detection strategies. Conventional neuropsy...

Connectome-based predictive modelling of problematic gaming in youth from the ABCD study.

BACKGROUND: Despite the rapid growth in gaming consumption and associated harms in adolescents, data-driven research to identify brain networks underl...

SERPINA1 as a Shared Biomarker in Periodontitis and Oral Squamous Cell Carcinoma.

OBJECTIVE: Periodontitis (PD) and oral squamous cell carcinoma (OSCC) frequently co‑occur in clinical populations. However, shared molecular determina...

GIN-transformer based pairwise graph contrastive learning framework.

Resting-state functional magnetic resonance imaging (rs-fMRI) provides critical biomarkers for diagnosing neuropsychiatric disorders such as autism sp...

Clinical validation of a unified data-driven respiratory motion correction technique in 18F-FDG PET/CT imaging of upper abdominal lesions: a real-world study.

PURPOSE: Respiratory motion (RM)-related artifacts significantly impact image quality and diagnostic accuracy in PET/CT imaging. This study aimed to p...

Machine learning integrating MRI and clinical features predicts early recurrence of hepatocellular carcinoma after resection.

This study aims to construct a robust artificial intelligence (AI) model to predict early recurrence of hepatocellular carcinoma (HCC) following surgi...

Construction of musculoskeletal quantitative model based on deep learning and study of musculoskeletal relationship in patients with osteoporosis.

INTRODUCTION: Lumbar CT and MRI scans are helpful for osteoporosis (OP) screening. Deep learning enhances the efficiency and accuracy of musculoskelet...

Transfer learning from 2D natural images to 4D fMRI brain images via geometric mapping.

Functional magnetic resonance imaging (fMRI) allows real-time observation of brain activity through blood oxygen level-dependent (BOLD) signals and is...

Machine learning and SHAP values for predicting coronary artery disease risk in Xinjiang, China.

BACKGROUND: Accurate individual risk assessment is crucial for guiding and improving the prevention of atherosclerotic cardiovascular disease (ASCVD)....

Biomarker discovery in Lennox-Gastaut syndrome: Advances and challenges in electrophysiological, genetic, neuroimaging, and neuroinflammatory approaches.

In Lennox-Gastaut Syndrome (LGS), a severe developmental and epileptic encephalopathy, the absence of validated biomarkers limits our ability to detec...

Comparative analysis of machine learning algorithms for predicting stereotactic coordinates of the centromedian nucleus.

OBJECTIVE: Deep brain stimulation (DBS) of the centromedian nucleus (CM) of the thalamus is a promising treatment for drug-resistant epilepsy, Tourett...