Radiology

Achieving accurate prostate auto-segmentation on CT in the absence of MR imaging.

BACKGROUND: Magnetic resonance imaging (MRI) is considered the gold standard for prostate segmentation. Computed tomography (CT)-based segmentation is prone to observer bias, potentially overestimating the prostate volume by ∼ 30 % compared to MRI. However, MRI accessibility is challenging for patients with contraindications or in rural areas globally with limited clinical resources.

Deep plug-and-play MRI reconstruction based on multiple complementary priors.

Magnetic resonance imaging (MRI) is widely used in clinical diagnosis as a safe, non-invasive, high-resolution medical imaging technology, but long scanning time has been a major challenge for this technology. The undersampling reconstruction method has become an important technical means to accelerate MRI by reducing the data sampling rate while maintaining high-quality imaging. However, traditio...

TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs.

Characterization of breast parenchyma in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity ...

A Machine Learning Model Based on Global Mammographic Radiomic Features Can Predict Which Normal Mammographic Cases Radiology Trainees Find Most Difficult.

This study aims to investigate whether global mammographic radiomic features (GMRFs) can distinguish hardest- from easiest-to-interpret normal cases f...

Deep convolutional neural network for automatic segmentation and classification of jaw tumors in contrast-enhanced computed tomography images.

The purpose of this study was to evaluate the performance of convolutional neural network (CNN)-based image segmentation models for segmentation and c...

A Multicenter Cohort Study on Ultrasound-based Deep Learning Nomogram for Predicting Post-Neoadjuvant Chemotherapy Axillary Lymph Node Status in Breast Cancer Patients.

RATIONALE AND OBJECTIVES: The aim of this study was to evaluate the capability of an ultrasound (US)-based deep learning (DL) nomogram for predicting ...

Large Language Models can Help with Biostatistics and Coding Needed in Radiology Research.

INTRODUCTION: Original research in radiology often involves handling large datasets, data manipulation, statistical tests, and coding. Recent studies ...

Diagnostic accuracy of artificial intelligence for identifying systolic and diastolic cardiac dysfunction in the emergency department.

INTRODUCTION: Cardiac point-of-care ultrasound (POCUS) can evaluate for systolic and diastolic dysfunction to inform care in the Emergency Department ...

Assessment of a fully-automated diagnostic AI software in prostate MRI: Clinical evaluation and histopathological correlation.

OBJECTIVE: This study aims to evaluate the diagnostic performance of a commercial, fully-automated, artificial intelligence (AI) driven software tool ...

Assessment of the Diagnostic Performance of a Commercially Available Artificial Intelligence Algorithm for Risk Stratification of Thyroid Nodules on Ultrasound.

Thyroid nodules are challenging to accurately characterize on ultrasound (US), though the emergence of risk stratification systems and more recently ...

A genetic programming Rician noise reduction and explainable deep learning model for Alzheimer's diseases severity prediction.

BACKGROUND: Degradation of magnetic resonance imaging (MRI) remains a challenging issue, with noise being a key damaging component introduced due to a...

A support vector machine-based approach to guide the selection of a pseudo-reference region for brain PET quantification.

A Support Vector Machine (SVM) based approach was developed to identify a pseudo-reference region for brain PET scans with the aim of reducing intersc...

Paranoid About Androids: A Review of Robotics in Radiology.

In tandem with the ever-increasing global population, the demand for diagnostic radiology service provision is on the rise and at a disproportionate r...

Avoidable biopsies? Validating artificial intelligence-based decision support software in indeterminate thyroid nodules.

BACKGROUND: Multiple artificial intelligence (AI) systems have been approved to risk-stratify thyroid nodules through sonographic characterization. We...

Segmentation of four-chamber view images in fetal ultrasound exams using a novel deep learning model ensemble method.

Fetal echocardiography, a specialized ultrasound application commonly utilized for fetal heart assessment, can greatly benefit from automated segmenta...

Using Machine Learning on MRI Radiomics to Diagnose Parotid Tumours Before Comparing Performance with Radiologists: A Pilot Study.

The parotid glands are the largest of the major salivary glands. They can harbour both benign and malignant tumours. Preoperative work-up relies on MR...

Application of a Deep Learning-Based Contrast-Boosting Algorithm to Low-Dose Computed Tomography Pulmonary Angiography With Reduced Iodine Load.

OBJECTIVE: The aim of this study was to assess the effectiveness of a deep learning-based image contrast-boosting algorithm by enhancing the image qua...

Evaluating retinal blood vessels for predicting white matter hyperintensities in ischemic stroke: A deep learning approach.

OBJECTIVE: This study aims to investigate whether a deep learning approach incorporating retinal blood vessels can effectively identify ischemic strok...

Assessing the impact of ultrasound image standardization in deep learning-based segmentation of carotid plaque types.

BACKGROUND AND OBJECTIVE: Carotid B-mode ultrasound (CBUS) imaging is often used to detect and assess atherosclerotic plaques. Doctors often need to s...

The utilization of artificial intelligence in enhancing 3D/4D ultrasound analysis of fetal facial profiles.

Artificial intelligence (AI) has emerged as a transformative technology in the field of healthcare, offering significant advancements in various medic...