Radiology

Validation of an artificial intelligence program in the characterization of breast nodules by ultrasound.

INTRODUCTION: Breast ultrasound is a widely accessible imaging method but highly operator-dependent. Artificial intelligence (AI) may improve breast lesion characterization, aiding in diagnostic decisions. OBJECTIVE: To validate an AI system (Koios DS v3.1) in the BI-RADS classification of breast lesions on ultrasound. METHODS: This cross-sectional diagnostic study included 100 women with breast l...

Attention-Guided Deep Learning of Chemical Exchange Saturation Transfer Magnetic Resonance Imaging to Differentiate Between Tumor Progression and Radiation Necrosis in Brain Metastasis.

PURPOSE: Stereotactic radiosurgery (SRS) is a standard treatment for brain metastases; however, it may lead to radiation necrosis (RN). RN can be virtually indistinguishable from tumor progression (TP), which can have significant clinical implications on appropriate, time-sensitive treatment. This study investigated the effectiveness of multimodal chemical exchange saturation transfer magnetic res...

Deep Learning for Differentiating Benign From Malignant Bile Duct Dilation on MRCP: Development and Prospective Evaluation of an Xception-Logistic Regression Ensemble Model.

BACKGROUND: Accurate identification of benign and malignant bile duct dilatation (BDD) is needed to determine its management plan. Conventional imagin...

Low-dose CBCT image reconstruction: a review.

Cone-beam computed tomography (CBCT) is a critical imaging modality in various medical fields, yet its repeated use poses radiation risks to patients....

Artificial Intelligence in Prostate MRI: Addressing Current Limitations Through Emerging Technologies.

Prostate MRI has transformed lesion detection and risk stratification in prostate cancer, but its impact is constrained by the high cost of the exam, ...

Statin-Induced Decade-Long Stabilization of Noncalcified Coronary Plaque Despite a Zero Calcium Score.

BACKGROUND: A 48-year-old man with a coronary artery calcium (CAC) score of 0 underwent serial artificial intelligence (AI)-assisted coronary computed...

Precision medicine in Moyamoya vasculopathy.

PURPOSE OF REVIEW: Moyamoya vasculopathy is a progressive cerebrovascular steno-occlusive disease with variable presentation. As revascularization tec...

First Clinical Use of a Novel AI-Based Imaging Tool to Enhance CTO PCI Planning.

BACKGROUND: Chronic total occlusion (CTO) interventions are frequently limited by incomplete angiographic information. We report the use of a novel ar...

Artificial intelligence in the detection of multiple sclerosis plaques: Can it influence the treatment decision?

BACKGROUND AND PURPOSE: To monitor multiple sclerosis (MS) progression, follow-up MRIs are used to detect new or enlarging lesions (ELs), typically th...

Utilizing Optimized Mixed-Order Relation-Aware Recurrent Neural Network for Metacarpophalangeal Rheumatoid Arthritis Grading via Ultrasound Images.

The diagnostic problem of grading evaluation of ultrasonic images of Metacarpophalangeal rheumatoid arthritis (RA) is mostly dependent on the skills o...

Low-Field Magnetic Resonance Imaging of the Late Gestation Cervix and Birth Outcome Correlation: A Prospective Cohort Study.

OBJECTIVE: To use low-field MRI to produce reconstructions and 3D models of the cervix and to automate measurements for correlation with demographics ...

An efficient method for determining the mass attenuation coefficient of contrast agent-water mixtures and analysis of influencing factors in medical imaging systems.

The mass attenuation coefficient (MAC) plays a key parameter in computed tomography (CT) imaging and the development of novel contrast agents. In prac...

Uncertainty estimates in pharmacokinetic modelling of DCE-MRI.

Dynamic contrast-enhanced (DCE) MRI is a powerful technique for detecting and characterising various diseases by quantifying tissue perfusion. However...

ProtoRadNet: Prototypical patches of Convolutional Features for Radiology Image Classification Network.

Convolutional Neural Networks (CNNs) have achieved significant success in classifying radiology images; however, their implementation often resembles ...

Prediction of IDH-mutation and 1p19q-codeletion status in adult-type diffuse gliomas using three-class radiomics models.

PURPOSE: To predict the genetic subtypes of adult-type diffuse gliomas with three-class MRI radiomics. MATERIAL AND METHODS: Four hundred and eighty p...

A deep learning model to reduce agent dose for contrast-enhanced MRI of the cerebellopontine angle cistern.

OBJECTIVES: To evaluate a deep learning (DL) model for reducing the agent dose of contrast-enhanced T1-weighted MRI (T1ce) of the cerebellopontine ang...

3D Automated Segmentation of Bronchial Abnormalities on Ultrashort Echo Time MRI: A Quantitative MR Outcome in Cystic Fibrosis.

BACKGROUND: Cystic fibrosis (CF) monitoring relies on computed tomography (CT), but ultra-short echo time MRI (UTE-MRI) offers a radiation-free altern...

An artificial intelligence model for electrocardiogram detection of occlusion myocardial infarction: a retrospective study to reduce false-positive cath lab activations.

AIMS: Existing ST-segment elevation myocardial infarction (STEMI) alert pathways that rely on traditional STEMI criteria perform suboptimally. We aime...

Enhancing vertebral fracture prediction using multitask deep learning computed tomography imaging of bone and muscle.

OBJECTIVES: To develop and externally validate a computed tomography (CT)-based multitask learning model to predict fracture risk. MATERIALS AND METHO...

Automated Segmentation of Liver and Liver Tumors with SwinUNETR and UNET Neural Networks on 18F-FDG PET/CT.

OBJECTIVES: To develop and evaluate automated segmentation models for the liver and hepatic tumors on 18F-fluorodeoxyglucose positron emission tomogra...