Radiology

OpenRad: a curated repository of open-access AI models for radiology.

OBJECTIVES: To create and evaluate OpenRad ( https://konstvr.github.io/OpenRad/index.html ), a curated, standardized repository that aggregates open-access radiology artificial intelligence (AI) models enriched with metadata from the corresponding code repositories regarding availability of pretrained weights and interactive applications. MATERIALS AND METHODS: Retrospective analysis of literature...

Artificial intelligence in cancer immunotherapy: current trends in predicting response and personalizing treatment.

Artificial intelligence (AI) can transform cancer immunotherapy by enabling more accurate prediction of treatment responses, the discovery of specific biomarkers, and the development of personalised treatment plans. Traditional single-marker biomarkers (PD-L1, TMB, MSI) lack consistency across tumour types and cannot be used to assess tumour heterogeneity or the dynamic tumour microenvironment (TM...

Artificial intelligence and transforming cancer care.

Artificial Intelligence (AI) is reshaping oncology by addressing key limitations in traditional cancer care and enabling data-driven, personalized app...

Artificial intelligence-enabled plaque characterization from coronary computed tomography establishes basis of angina in women with nonobstructive atherosclerosis.

BACKGROUND: Half of women with ischemic symptoms have non-obstructive coronary artery disease (CAD), while the pathophysiology of their condition has ...

AutoCumulus: an automated mammographic density measure created using artificial intelligence.

BACKGROUND: Mammographic (or breast) density is an established risk factor for breast cancer, previously measured using a variety of quantitative, sem...

A multi-sequence MRI integration framework using SwinUNETR-v2 for multiple sclerosis lesion segmentation.

Multiple Sclerosis (MS) is a chronic brain disease that affects the brain and spinal cord, where Magnetic Resonance Imaging (MRI) plays a key role in ...

Preoperative low-attenuation area on computed tomography is associated with recurrence after curative resection for non-small-cell lung cancer.

BACKGROUND: Recurrence after curative-intent resection remains a major determinant of long-term outcomes in non-small-cell lung cancer (NSCLC). Preope...

Utility of artificial intelligence for the diagnosis, prognosis, and management of central serous chorioretinopathy: a narrative review.

Central serous chorioretinopathy (CSC) represents a significant cause of visual impairment, particularly in working-age individuals. Despite advances ...

Rapid Generation of Subject-Specific Human Models With Detailed Tissue Structures for Timely Individualized SAR Assessment.

PURPOSE: To enable the rapid generation of subject-specific whole-body anatomical models for patient-specific prediction of torso-local specific absor...

Machine Learning Model Using Pre-Cancer Therapy Cardiac Magnetic Resonance Images to Predict Cancer Therapy-Related Cardiac Dysfunction.

BACKGROUND: Predicting risk of cancer therapy-related cardiac dysfunction (CTRCD) remains challenging. OBJECTIVES: The purpose of this study was to as...

Precision Management of Autoimmune Ocular Complications: Th17 Mechanisms and Therapeutic Innovations.

Precision management of ocular complications in systemic autoimmune diseases, such as Sjögren's syndrome (SS), systemic lupus erythematosus (SLE), Beh...

Automated identification of MRI series using a hierarchical modular machine-learning pipeline.

OBJECTIVE: The volume and diversity of large MR imaging datasets require efficient automated labelling tools for cataloguing MR series, as manual anno...

Clinical evaluation of accelerated breath-held and free-breathing cine cardiac MRI using model-based deep learning reconstruction (SonicDL) in children and young adults.

BACKGROUND: Balanced steady-state free-precession (bSSFP) cine imaging is the clinical standard for ventricular function assessment but requires multi...

Super-resolution deep learning reconstruction achieves low-dose high-resolution imaging in pediatric cardiac computed tomography.

BACKGROUND: Cardiac computed tomography (CT) is widely utilized in pediatric cardiology, but minimizing radiation exposure is essential. Recently, sup...

[Legal responsibility in AI-assisted radiological reporting].

BACKGROUND: Artificial intelligence (AI) is increasingly used in radiological diagnostics, particularly for screening, detection, and prioritization o...

Review of systemic therapy in HCC with an approach to response assessment in clinical practice.

Systemic therapy for hepatocellular carcinoma (HCC) has undergone rapid transformation over the past decade, significantly expanding treatment options...

Interpretable machine learning model for predicting hemorrhage following ultrasound-guided percutaneous renal biopsy: a multicenter study.

OBJECTIVE: This study aims to develop and validate an interpretable machine learning (ML) model for predicting post-procedural hemorrhage (PH) after u...

Fully automatic left ventricle segmentation in [Formula: see text]Rb PET/CT Using a semi-supervised nnU-net.

BACKGROUND: Quantification of myocardial blood flow (MBF) with [Formula: see text]Rb PET/CT requires accurate delineation of the left ventricle (LV). ...

Enhancing resolution and image quality in musculoskeletal MRI using deep learning reconstruction.

OBJECTIVE: Deep learning-based noise reduction enhances image quality, overcoming the tradeoff among acquisition time, spatial resolution, and signal-...

Lesion-Specific Prediction of Segmental Fractional Flow Reserve Using Deep Learning and Optical Coherence Tomography-Derived Features.

BACKGROUND: Intracoronary imaging-derived physiologic indices enable vessel-level assessment of coronary flow impairment by integrating obstructive pl...