Radiology

Consensus Between Radiologists, Specialists in Internal Medicine, and AI Software on Chest X-Rays in a Hospital-at-Home Service: Prospective Observational Study.

BACKGROUND: Home hospitalization is a care modality growing in popularity worldwide. Telemedicine-driven hospital-at-home (HAH) services could replace traditional hospital departments for selected patients. Chest x-rays typically serve as a key diagnostic tool in such cases.

Deep denoising approach to improve shear wave phase velocity map reconstruction in ultrasound elastography.

BACKGROUND: Measurement noise often leads to inaccurate shear wave phase velocity estimation in ultrasound shear wave elastography. Filtering techniques are commonly used for denoising the shear wavefields. However, these filters are often not sufficient, especially in fatty tissues where the signal-to-noise ratio (SNR) can be very low.

Performance of recurrent neural networks with Monte Carlo dropout for predicting pharmacokinetic parameters from dynamic contrast-enhanced magnetic resonance imaging data.

PURPOSE: To quantitatively evaluate the performance of two types of recurrent neural networks (RNNs), long short-term memory (LSTM) and gated recurren...

Machine Learning Driven by Magnetic Resonance Imaging for the Classification of Alzheimer Disease Progression: Systematic Review and Meta-Analysis.

BACKGROUND: To diagnose Alzheimer disease (AD), individuals are classified according to the severity of their cognitive impairment. There are currentl...

Comparison and analysis of deep learning models for discriminating longitudinal and oblique vaginal septa based on ultrasound imaging.

BACKGROUND: The longitudinal vaginal septum and oblique vaginal septum are female müllerian duct anomalies that are relatively less diagnosed but seve...

Multi-branch CNNFormer: a novel framework for predicting prostate cancer response to hormonal therapy.

PURPOSE: This study aims to accurately predict the effects of hormonal therapy on prostate cancer (PC) lesions by integrating multi-modality magnetic ...

Assessment of MGMT promoter methylation status in glioblastoma using deep learning features from multi-sequence MRI of intratumoral and peritumoral regions.

OBJECTIVE: This study aims to evaluate the effectiveness of deep learning features derived from multi-sequence magnetic resonance imaging (MRI) in det...

Machine Learning for Predicting Zearalenone Contamination Levels in Pet Food.

Zearalenone (ZEN) has been detected in both pet food ingredients and final products, causing acute toxicity and chronic health problems in pets. There...

Automated Measurement of Effective Radiation Dose by F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography.

BACKGROUND/OBJECTIVES: Calculating the radiation dose from CT in F-PET/CT examinations poses a significant challenge. The objective of this study is t...

VASARI 2.0: a new updated MRI VASARI lexicon to predict grading and status in brain glioma.

INTRODUCTION: Precision medicine refers to managing brain tumors according to each patient's unique characteristics when it was realized that patients...

Artificial intelligence in respiratory care.

The evolution of artificial intelligence (AI) has revolutionised numerous aspects of our daily lives, with profound implications across various sector...

AI-derived automated quantification of cardiac chambers and myocardium from non-contrast CT: Prediction of major adverse cardiovascular events in asymptomatic subjects.

BACKGROUND AND AIMS: The significance of left ventricular mass and chamber volumes from non-contrast computed tomography (CT) for predicting major adv...

Enhancing diagnostic accuracy in breast cancer: integrating novel machine learning approaches with enhanced image preprocessing for improved mammography analysis.

PURPOSE: This study explored the use of computer-aided diagnosis (CAD) systems to enhance mammography image quality and identify potentially suspiciou...

Comparison of conventional diffusion-weighted imaging and multiplexed sensitivity-encoding combined with deep learning-based reconstruction in breast magnetic resonance imaging.

PURPOSE: To evaluate the feasibility of multiplexed sensitivity-encoding (MUSE) with deep learning-based reconstruction (DLR) for breast imaging in co...

SMDFnet: Saliency multiscale dense fusion network for MRI and CT image fusion.

MRI-CT image fusion technology combines magnetic resonance imaging (MRI) and computed tomography (CT) imaging to provide more comprehensive and accura...

Predicting preterm birth using electronic medical records from multiple prenatal visits.

This study aimed to predict preterm birth in nulliparous women using machine learning and easily accessible variables from prenatal visits. Elastic ne...

AI model using CT-based imaging biomarkers to predict hepatocellular carcinoma in patients with chronic hepatitis B.

BACKGROUND & AIMS: Various hepatocellular carcinoma (HCC) prediction models have been proposed for patients with chronic hepatitis B (CHB) using clini...

Classifying Alzheimer's Disease Using a Finite Basis Physics Neural Network.

The disease amyloid plaques, neurofibrillary tangles, synaptic dysfunction, and neuronal death gradually accumulate throughout Alzheimer's disease (AD...

Programmable ultrasound-mediated swarms manipulation of bacteria-red blood cell microrobots for tumor-specific thrombosis and robust photothermal therapy.

Despite the excellent advantages of biomicrorobots, such as autonomous navigation and targeting actuation, effective penetration and retention to deep...

Combination of deep learning reconstruction and quantification for dynamic contrast-enhanced (DCE) MRI.

Dynamic contrast-enhanced (DCE) MRI is an important imaging tool for evaluating tumor vascularity that can lead to improved characterization of tumor ...