Radiology

Aging beyond diagnosis: the MRI brain age gap across disorders.

The brain age gap (BAG), the difference between magnetic resonance imaging-predicted brain age and chronological age, is a proposed marker of neurobiological aging, yet its transdiagnostic significance remains uncertain. This meta-analysis evaluated BAG in Alzheimer's disease (AD), mild cognitive impairment (MCI), multiple sclerosis (MS), Parkinson's disease (PD), schizophrenia (SCZ), stroke, and ...

Coronary stenosis assessment: AI-based CT quantification, visual analysis of invasive angiography, and quantitative coronary angiography.

OBJECTIVE: To evaluate the diagnostic performance for coronary stenosis of artificial intelligence (AI)-based CT quantification, manual CT quantification, and visual invasive coronary angiography (ICA) assessment against quantitative coronary angiography (QCA). MATERIALS AND METHODS: This retrospective study included patients who underwent both coronary CT angiography (CCTA) and ICA within 1 month...

An interpretable PET/CT-based radiomic-clinical model for predicting bone marrow involvement in follicular lymphoma: comparison of pelvic and spine-pelvis VOI frameworks.

PURPOSE: To investigate the feasibility of non-invasively identifying bone marrow involvement (BMI) in follicular lymphoma (FL) using baseline 18F-FDG...

Use of Artificial Intelligence in prostate MRI: A rapid scoping review highlighting limited evidence in screening context.

BACKGROUND AND OBJECTIVE: Artificial Intelligence (AI) is seen as a potential solution to alleviate workforce demands arising from growing use of magn...

Aberration-aware 3D localization microscopy via self-supervised neural-physics learning.

Single-molecule localization microscopy (SMLM) enables volumetric nanoscopy by retrieving 3D molecular positions from engineered 2D fluorescence patte...

Multi-task deep learning assists detection and diagnosis of gliomas and brain metastases.

Gliomas and brain metastases (BMs) on MRI pose significant diagnostic challenges for radiologists. This study aims to develop a multi-task model and a...

Enhancing Drug Response Prediction in Epilepsy with Emerging Multimodal Models: Focus on Clinical, Pharmacologic, and Genomic Factors.

Epilepsy is a common neurological disorder, with approximately one-third of the affected population developing drug-resistant epilepsy despite the exp...

Diagnostic accuracy of AI-assisted point-of-care ultrasound for abdominal free fluid detection in FAST trauma assessment: a systematic review and meta-analysis.

STUDY OBJECTIVE: Point-of-care ultrasound (PoCUS) is widely used in trauma care through the Focused Assessment with Sonography for Trauma (FAST) proto...

MDVM-UNet: lumbar MRI segmentation and lordosis angle measurement via a dual-driven mechanism.

With the rising incidence of degenerative lumbar spine disorders, accurate segmentation of spinal structures based on magnetic resonance imaging (MRI)...

A hybrid machine learning approach for automated malaria diagnosis from thin blood smear images.

BACKGROUND: Malaria remains a significant global health challenge, requiring diagnostic approaches that are rapid, cost-effective, and accurate. The p...

MedNext-Insight Model for Automated Metabolic Tumor Volume Delineation on Computed Tomography and Prognostic Value in Nasopharyngeal Carcinoma.

PURPOSES: To develop a deep learning model for automated metabolic tumor volume (MTV) delineation on routine computed tomography (CT) without positron...

Leveraging natural language processing artificial intelligence for automated data extraction of ejection fraction and strain from cardiovascular imaging reports.

STUDY OBJECTIVE: To determine the feasibility of using natural language processing (NLP) to extract ejection fraction (EF) and related cardiac imaging...

Quantification of edge-enhancing effects using accelerated deep learning reconstructed orbital MRI sequences.

RATIONALE AND OBJECTIVES: To qualitatively and quantitatively compare image quality, edge sharpness, and internal-structure delineation of accelerated...

Integrated Diagnosis of Submassive Pulmonary Embolism Using Computed Tomography Angiography, Echocardiography, and Artificial Intelligence.

OBJECTIVES: Early intervention in submassive pulmonary embolism (SMPE) has been shown to improve long-term cardiopulmonary outcomes compared to antico...

Interpretable machine learning using accumulated local effects to characterise predictors of subclinical leaflet thrombosis after self-expanding transcatheter aortic valve implantation.

OBJECTIVES: Subclinical leaflet thrombosis is an early form of bioprosthetic valve dysfunction after transcatheter aortic valve implantation. Predicti...

Artificial Intelligence-Enhanced Identification of Incidental Findings in Prostate MRI.

OBJECTIVES: The use of prostate magnetic resonance imaging (MRI) is increasing, and coverage often captures substantial portions of the pelvis, visual...

Smartphone-Based Pupillometry for Noninvasive Detection of Elevated Intracranial Pressure in Nepali Acute Brain Injury Patients: A Pilot Study.

BACKGROUND: Intracranial hypertension is a life-threatening complication of acute brain injuries such as traumatic brain injury (TBI), subarachnoid he...

Evaluating multi-task network architectures for simultaneous breast lesion segmentation and classification in ultrasound images.

Breast lesion segmentation and classification in ultrasound (US) images are two essential tasks for computer-aided diagnosis of breast cancer. However...

EchoAgent: guideline-centric reasoning agent for echocardiography measurement and interpretation.

PURPOSE: Echocardiographic interpretation requires video-level reasoning and guideline-based measurement analysis, which current deep learning models ...

MRI Habitat Analysis for Preoperative Prediction of Perineural Invasion and Prognostic Stratification in Rectal Cancer.

BACKGROUND: Accurate preoperative assessment of perineural invasion (PNI) remains challenging in rectal cancer. PURPOSE: To develop assessment models ...