Radiology

Neural network combining with clinical ultrasonography: A new approach for classification of salivary gland tumors.

OBJECTIVE: Little information is available about deep learning methods used in ultrasound images of salivary gland tumors. We aimed to compare the accuracy of the ultrasound-trained model to computed tomography or magnetic resonance imaging trained model.

Development of a deep-learning model for classification of LI-RADS major features by using subtraction images of MRI: a preliminary study.

PURPOSE: Liver Imaging Reporting and Data System (LI-RADS) is limited by interreader variability. Thus, our study aimed to develop a deep-learning model for classifying LI-RADS major features using subtraction images using magnetic resonance imaging (MRI).

A comparison of point-tracking algorithms in ultrasound videos from the upper limb.

Tracking points in ultrasound (US) videos can be especially useful to characterize tissues in motion. Tracking algorithms that analyze successive vide...

Deep Learning-Based Quantitative Assessment of Melamine and Cyanuric Acid in Pet Food Using Fourier Transform Infrared Spectroscopy.

Melamine and its derivative, cyanuric acid, are occasionally added to pet meals because of their nitrogen-rich qualities, leading to the development o...

Deep Learning Detection and Segmentation of Brain Arteriovenous Malformation on Magnetic Resonance Angiography.

BACKGROUND: The delineation of brain arteriovenous malformations (bAVMs) is crucial for subsequent treatment planning. Manual segmentation is time-con...

Smart IoT in Breast Cancer Detection Using Optimal Deep Learning.

IoT in healthcare systems is currently a viable option for providing higher-quality medical care for contemporary e-healthcare. Using an Internet of T...

Automatic brain MRI motion artifact detection based on end-to-end deep learning is similarly effective as traditional machine learning trained on image quality metrics.

Head motion artifacts in magnetic resonance imaging (MRI) are an important confounding factor concerning brain research as well as clinical practice. ...

Standalone AI for Breast Cancer Detection at Screening Digital Mammography and Digital Breast Tomosynthesis: A Systematic Review and Meta-Analysis.

Background There is considerable interest in the potential use of artificial intelligence (AI) systems in mammographic screening. However, it is essen...

Deep Learning Aided Neuroimaging and Brain Regulation.

Currently, deep learning aided medical imaging is becoming the hot spot of AI frontier application and the future development trend of precision neuro...

Prediction of Bone Marrow Biopsy Results From MRI in Multiple Myeloma Patients Using Deep Learning and Radiomics.

OBJECTIVES: In multiple myeloma and its precursor stages, plasma cell infiltration (PCI) and cytogenetic aberrations are important for staging, risk s...

Academic integrity and artificial intelligence: is ChatGPT hype, hero or heresy?

Academic integrity in both higher education and scientific writing has been challenged by developments in artificial intelligence. The limitations ass...

Deep learning-based classification of breast lesions using dynamic ultrasound video.

PURPOSE: We intended to develop a deep-learning-based classification model based on breast ultrasound dynamic video, then evaluate its diagnostic perf...

Multimodality MRI synchronous construction based deep learning framework for MRI-guided radiotherapy synthetic CT generation.

Synthesizing computed tomography (CT) images from magnetic resonance imaging (MRI) data can provide the necessary electron density information for acc...

Inter-crystal scattering event identification using a novel silicon photomultiplier signal multiplexing method.

Identifying the inter-crystal scatter (ICS) events and recovering the first interaction position enables the accurate determination of the line-of-res...

Uncertainty estimation for deep learning-based pectoral muscle segmentation via Monte Carlo dropout.

. Deep Learning models are often susceptible to failures after deployment. Knowing when your model is producing inadequate predictions is crucial. In ...

The Emerging Role of Automation, Measurement Standardization, and Artificial Intelligence in Foot and Ankle Imaging: An Update.

In the past few years, advances in clinical imaging in the realm of foot and ankle have been consequential and game changing. Improvements in the hard...

Diagnosis of Liver Fibrosis Using Artificial Intelligence: A Systematic Review.

The development of liver fibrosis as a consequence of continuous inflammation represents a turning point in the evolution of chronic liver diseases. ...

Deep learning for detection of iso-dense, obscure masses in mammographically dense breasts.

OBJECTIVES: To analyze the performance of deep learning in isodense/obscure masses in dense breasts. To build and validate a deep learning (DL) model ...

Feasibility of accelerated non-contrast-enhanced whole-heart bSSFP coronary MR angiography by deep learning-constrained compressed sensing.

OBJECTIVES: To examine a compressed sensing artificial intelligence (CSAI) framework to accelerate image acquisition in non-contrast-enhanced whole-he...

Deep learning regressor model based on nigrosome MRI in Parkinson syndrome effectively predicts striatal dopamine transporter-SPECT uptake.

PURPOSE: Nigrosome imaging using susceptibility-weighted imaging (SWI) and dopamine transporter imaging using I-2β-carbomethoxy-3β-(4-iodophenyl)-N-(3...