AIMC Topic: Radiography

Clear Filters Showing 271 to 280 of 1087 articles

An AI-Based Image Quality Control Framework for Knee Radiographs.

Journal of digital imaging
Image quality control (QC) is crucial for the accurate diagnosis of knee diseases using radiographs. However, the manual QC process is subjective, labor intensive, and time-consuming. In this study, we aimed to develop an artificial intelligence (AI)...

Enhancement of Non-Linear Deep Learning Model by Adjusting Confounding Variables for Bone Age Estimation in Pediatric Hand X-rays.

Journal of digital imaging
In medicine, confounding variables in a generalized linear model are often adjusted; however, these variables have not yet been exploited in a non-linear deep learning model. Sex plays important role in bone age estimation, and non-linear deep learni...

ChatGPT in medical imaging higher education.

Radiography (London, England : 1995)
INTRODUCTION: Academic integrity among radiographers and nuclear medicine technologists/scientists in both higher education and scientific writing has been challenged by advances in artificial intelligence (AI). The recent release of ChatGPT, a chatb...

On the importance of patient acceptance for medical robotic imaging.

International journal of computer assisted radiology and surgery
PURPOSE: Mutual acceptance is required for any human-to-human interaction. Therefore, one would assume that this also holds for robot-patient interactions. However, the medical robotic imaging field lacks research in the area of acceptance. This work...

Large language models for structured reporting in radiology: performance of GPT-4, ChatGPT-3.5, Perplexity and Bing.

La Radiologia medica
Structured reporting may improve the radiological workflow and communication among physicians. Artificial intelligence applications in medicine are growing fast. Large language models (LLMs) are recently gaining importance as valuable tools in radiol...

A deep learning model enables accurate prediction and quantification of pulmonary edema from chest X-rays.

Critical care (London, England)
BACKGROUND: A quantitative assessment of pulmonary edema is important because the clinical severity can range from mild impairment to life threatening. A quantitative surrogate measure, although invasive, for pulmonary edema is the extravascular lung...

Deep learning-based screening tool for rotator cuff tears on shoulder radiography.

Journal of orthopaedic science : official journal of the Japanese Orthopaedic Association
BACKGROUND: Early diagnosis of rotator cuff tears is essential for appropriate and timely treatment. Although radiography is the most used technique in clinical practice, it is difficult to accurately rule out rotator cuff tears as an initial imaging...

A Deep Learning Tool for Automated Landmark Annotation on Hip and Pelvis Radiographs.

The Journal of arthroplasty
BACKGROUND: Automatic methods for labeling and segmenting pelvis structures can improve the efficiency of clinical and research workflows and reduce the variability introduced with manual labeling. The purpose of this study was to develop a single de...

Using artificial intelligence models to evaluate envisaged points initially: A pilot study.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
The morphology of the finger bones in hand-wrist radiographs (HWRs) can be considered as a radiological skeletal maturity indicator, along with the other indicators. This study aims to validate the anatomical landmarks envisaged to be used for classi...