Radiology

Clinical evaluation of a deep-learning model for automatic scoring of the Alberta stroke program early CT score on non-contrast CT.

BACKGROUND: Automated measurement of the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) can support clinical decision making. Based on a deep learning algorithm, we developed an automated ASPECTS scoring system (Heuron ASPECTS) and validated its performance in a prespecified clinical trial.

A new convolutional neural network based on combination of circlets and wavelets for macular OCT classification.

Artificial intelligence (AI) algorithms, encompassing machine learning and deep learning, can assist ophthalmologists in early detection of various ocular abnormalities through the analysis of retinal optical coherence tomography (OCT) images. Despite considerable progress in these algorithms, several limitations persist in medical imaging fields, where a lack of data is a common issue. Accordingl...

Enhancing domain generalization in the AI-based analysis of chest radiographs with federated learning.

Developing robust artificial intelligence (AI) models that generalize well to unseen datasets is challenging and usually requires large and variable d...

Antibacterial, Resistance Modulation, Anti-Biofilm Formation, and Efflux Pump Inhibition Properties of (Willd.) N. Halle (Celastraceae) Stem Extract and Its Constituents.

This study investigated the antibacterial, resistance modulation, biofilm inhibition, and efflux pump inhibition potentials of stem extract and its c...

Feasibility and acceptability of ChatGPT generated radiology report summaries for cancer patients.

OBJECTIVE: Patients now have direct access to their radiology reports, which can include complex terminology and be difficult to understand. We assess...

Deep-learning models for differentiation of xanthogranulomatous cholecystitis and gallbladder cancer on ultrasound.

BACKGROUND: The radiological differentiation of xanthogranulomatous cholecystitis (XGC) and gallbladder cancer (GBC) is challenging yet critical. We a...

Semiautomatic Assessment of Facet Tropism From Lumbar Spine MRI Using Deep Learning: A Northern Finland Birth Cohort Study.

STUDY DESIGN: This is a retrospective, cross-sectional, population-based study that automatically measured the facet joint (FJ) angles from T2-weighte...

Quality assessment of colour fundus and fluorescein angiography images using deep learning.

BACKGROUND/AIMS: Image quality assessment (IQA) is crucial for both reading centres in clinical studies and routine practice, as only adequate quality...

Lesion detection in women breast's dynamic contrast-enhanced magnetic resonance imaging using deep learning.

Breast cancer is one of the most common cancers in women and the second foremost cause of cancer death in women after lung cancer. Recent technologica...

Deep learning radiomics on shear wave elastography and b-mode ultrasound videos of diaphragm for weaning outcome prediction.

PURPOSE: We proposed an automatic method based on deep learning radiomics (DLR) on shear wave elastography (SWE) and B-mode ultrasound videos of diaph...

Machine learning and deep learning for brain tumor MRI image segmentation.

Brain tumors are often fatal. Therefore, accurate brain tumor image segmentation is critical for the diagnosis, treatment, and monitoring of patients ...

Deep learning algorithm for predicting preterm birth in the case of threatened preterm labor admissions using transvaginal ultrasound.

PURPOSE: Preterm birth presents a major challenge in perinatal care, and predicting preterm birth remains a major challenge. If preterm birth cases ca...

A 5G-based telerobotic ultrasound system provides qualified abdominal ultrasound services for patients on a rural island: a prospective and comparative study of 401 patients.

PURPOSE: To explore the feasibility of a 5G-based telerobotic ultrasound (US) system for providing qualified abdominal US services on a rural island.

Application of a deep learning algorithm for three-dimensional T1-weighted gradient-echo imaging of gadoxetic acid-enhanced MRI in patients at a high risk of hepatocellular carcinoma.

PURPOSE: To evaluate the efficacy of a vendor-specific deep learning reconstruction algorithm (DLRA) in enhancing image quality and focal lesion detec...

A deep learning approach to the automatic detection of alignment errors in cryo-electron tomographic reconstructions.

Electron tomography is an imaging technique that allows for the elucidation of three-dimensional structural information of biological specimens in a v...

Noninvasive diagnostic imaging for endometriosis part 1: a systematic review of recent developments in ultrasound, combination imaging, and artificial intelligence.

Endometriosis affects 1 in 9 women and those assigned female at birth. However, it takes 6.4 years to diagnose using the conventional standard of lapa...

Feasibility of artificial intelligence its current status, clinical applications, and future direction in cardiovascular disease.

In routine clinical practice, the diagnosis and treatment of cardiovascular disease (CVD) rely on data in a variety of formats. These formats comprise...

Review of Deep Learning Approaches for Interleaved Photoacoustic and Ultrasound (PAUS) Imaging.

Photoacoustic (PA) imaging provides optical contrast at relatively large depths within the human body, compared to other optical methods, at ultrasoun...

Diagnosis of COVID-19 with simultaneous accurate prediction of cardiac abnormalities from chest computed tomographic images.

COVID-19 has potential consequences on the pulmonary and cardiovascular health of millions of infected people worldwide. Chest computed tomographic (C...

Advances in Clinical Imaging of Vascular Inflammation: A State-of-the-Art Review.

Vascular inflammation is a major contributor to cardiovascular disease, particularly atherosclerotic disease, and early detection of vascular inflamma...