Radiology

A myocardial extraction method using deep learning for 99mTc myocardial perfusion SPECT images: A basic study to reduce the effects of extra-myocardial activity.

AIM: The purpose of this study was to automatically extract myocardial regions from transaxial singl...

The role of artificial intelligence in paediatric cardiovascular magnetic resonance imaging.

Artificial intelligence (AI) offers the potential to change many aspects of paediatric cardiac imagi...

Editorial for "Deep Learning Assisted Diagnosis of Musculoskeletal Tumors Based Upon Contrast-Enhanced Magnetic Resonance Imaging".

Quantitative computed tomography imaging-based classification of cement dust-exposed subjects with an artificial neural network technique.

BACKGROUND AND OBJECTIVE: Cement dust exposure is likely to affect the structural and functional alt...

Weakly-supervised deep learning for ultrasound diagnosis of breast cancer.

Conventional deep learning (DL) algorithm requires full supervision of annotating the region of inte...

Image-Based Artificial Intelligence in Wound Assessment: A Systematic Review.

Accurately predicting wound healing trajectories is difficult for wound care clinicians due to the ...

Deep learning for Alzheimer's disease: Mapping large-scale histological tau protein for neuroimaging biomarker validation.

Abnormal tau inclusions are hallmarks of Alzheimer's disease and predictors of clinical decline. Sev...

Brain tissue segmentation via non-local fuzzy c-means clustering combined with Markov random field.

The segmentation and extraction of brain tissue in magnetic resonance imaging (MRI) is a meaningful ...

Accuracy of Robot-Assisted Percutaneous Pedicle Screw Placement under Regional Anesthesia: A Retrospective Cohort Study.

BACKGROUND: Robot-assisted pedicle screw placement is usually performed under general anesthesia to ...

Imaging Manifestations and Evaluation of Postoperative Complications of Bone and Joint Infections under Deep Learning.

To explore and evaluate the imaging manifestations of postoperative complications of bone and joint ...

TypeSeg: A type-aware encoder-decoder network for multi-type ultrasound images co-segmentation.

PURPOSE: As a portable and radiation-free imaging modality, ultrasound can be easily used to image v...

Deep learning model to quantify left atrium volume on routine non-contrast chest CT and predict adverse outcomes.

BACKGROUND: Low-dose computed tomography (LDCT) are performed routinely for lung cancer screening. H...

Radiogenomics and Artificial Intelligence Approaches Applied to Cardiac Computed Tomography Angiography and Cardiac Magnetic Resonance for Precision Medicine in Coronary Heart Disease: A Systematic Review.

The risk of coronary heart disease (CHD) clinical manifestations and patient management is estimated...

Radiation Protection and Occupational Exposure on Ga-PSMA-11-Based Cerenkov Luminescence Imaging Procedures in Robot-Assisted Prostatectomy.

Cerenkov luminescence imaging (CLI) was successfully implemented in the intraoperative context as a ...

Image quality and radiologists' subjective acceptance using model-based iterative and deep learning reconstructions as adjuncts to ultrahigh-resolution CT in low-dose contrast-enhanced abdominopelvic CT: phantom and clinical pilot studies.

PURPOSE: In contrast-enhanced abdominopelvic CT (CE-APCT) for oncologic follow-up, ultrahigh-resolut...

TBNet: a context-aware graph network for tuberculosis diagnosis.

Tuberculosis (TB) is an infectious bacterial disease. It can affect the human lungs, brain, bones, a...

Diagnostic Value of SonoVue Contrast-Enhanced Ultrasonography in Nipple Discharge Based on Artificial Intelligence.

This paper aims to explore the application value of SonoVue contrast-enhanced ultrasonography based ...

Optimizing the transfer-learning with pretrained deep convolutional neural networks for first stage breast tumor diagnosis using breast ultrasound visual images.

Female accounts for approximately 50% of the total population worldwide and many of them had breast ...

Spine Medical Image Segmentation Based on Deep Learning.

The aim was to further explore the clinical value of deep learning algorithm in the field of spinal ...

Magnetic Resonance Imaging Images under Deep Learning in the Identification of Tuberculosis and Pneumonia.

This work aimed to explore the application value of deep learning-based magnetic resonance imaging (...

Joint MRI T1 Unenhancing and Contrast-enhancing Multiple Sclerosis Lesion Segmentation with Deep Learning in OPERA Trials.

Background Deep learning-based segmentation could facilitate rapid and reproducible T1 lesion load a...