AI Medical Compendium Journal:
The British journal of radiology

Showing 31 to 40 of 137 articles

Feasibility study of deep-learning-based bone suppression incorporated with single-energy material decomposition technique in chest X-rays.

The British journal of radiology
OBJECTIVE: To improve the detection of lung abnormalities in chest X-rays by accurately suppressing overlapping bone structures in the lung area. According to literature on missed lung cancer in chest X-rays, such structures are a significant cause o...

Clinical application of deep learning and radiomics in hepatic disease imaging: a systematic scoping review.

The British journal of radiology
OBJECTIVE: Artificial intelligence (AI) has begun to play a pivotal role in hepatic imaging. This systematic scoping review summarizes the latest progress of AI in evaluating hepatic diseases based on computed tomography (CT) and magnetic resonance (...

Dose length product and outcome of CT fluoroscopy-guided interventions using a new 320-detector row CT scanner with deep-learning reconstruction and new bow-tie filter.

The British journal of radiology
OBJECTIVES: To investigate the dose length product (DLP) and outcomes of CT fluoroscopy (CTF)-guided interventions using a novel 320-detector row CT scanner with deep-learning reconstruction (DLR) and a new bow-tie filter ( Aquilion ONE Prism Edition...

A prediction model for pathological findings after neoadjuvant chemoradiotherapy for resectable locally advanced esophageal squamous cell carcinoma based on endoscopic images using deep learning.

The British journal of radiology
OBJECTIVES: To propose deep-learning (DL)-based predictive model for pathological complete response rate for resectable locally advanced esophageal squamous cell carcinoma (SCC) after neoadjuvant chemoradiotherapy (NCRT) with endoscopic images.

A deep learning-based approach for the diagnosis of adrenal adenoma: a new trial using CT.

The British journal of radiology
OBJECTIVE: To develop and validate deep convolutional neural network (DCNN) models for the diagnosis of adrenal adenoma (AA) using CT.

Incidentalomas in chest CT.

The British journal of radiology
Advances in imaging technology have dramatically increased the resolution of CT and improved detection of disease; these advances also have led to an increase in incidentalomas or incidental findings that often do not represent significant disease. I...

The efficacy of F-FDG-PET-based radiomic and deep-learning features using a machine-learning approach to predict the pathological risk subtypes of thymic epithelial tumors.

The British journal of radiology
OBJECTIVE: To examine whether the machine-learning approach using 18-fludeoxyglucose positron emission tomography (F-FDG-PET)-based radiomic and deep-learning features is useful for predicting the pathological risk subtypes of thymic epithelial tumor...

Diagnostic accuracy of a commercially available deep-learning algorithm in supine chest radiographs following trauma.

The British journal of radiology
OBJECTIVES: Trauma chest radiographs may contain subtle and time-critical pathology. Artificial intelligence (AI) may aid in accurate reporting, timely identification and worklist prioritisation. However, few AI programs have been externally validate...

Starting CT-guided robotic interventional oncology at a UK centre.

The British journal of radiology
OBJECTIVE: A commercially available CT-guided robot offers enhanced abilities in planning, targeting, and confirming accurate needle placement. In this short communication, we describe our first UK experience of robotic interventional oncology proced...

Artificial intelligence applied to fetal MRI: A scoping review of current research.

The British journal of radiology
Artificial intelligence (AI) is defined as the development of computer systems to perform tasks normally requiring human intelligence. A subset of AI, known as machine learning (ML), takes this further by drawing inferences from patterns in data to '...