AIMC Topic: Radiography

Clear Filters Showing 451 to 460 of 1087 articles

Artificial intelligence in emergency radiology: A review of applications and possibilities.

Diagnostic and interventional imaging
Artificial intelligence (AI) applications in radiology have been rising exponentially in the last decade. Although AI has found usage in various areas of healthcare, its utilization in the emergency department (ED) as a tool for emergency radiologist...

Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?

Radiology
As the role of artificial intelligence (AI) in clinical practice evolves, governance structures oversee the implementation, maintenance, and monitoring of clinical AI algorithms to enhance quality, manage resources, and ensure patient safety. In this...

ImageGCN: Multi-Relational Image Graph Convolutional Networks for Disease Identification With Chest X-Rays.

IEEE transactions on medical imaging
Image representation is a fundamental task in computer vision. However, most of the existing approaches for image representation ignore the relations between images and consider each input image independently. Intuitively, relations between images ca...

Deep learning-based tool affects reproducibility of pes planus radiographic assessment.

Scientific reports
Angle measurement methods for measuring pes planus may lose consistency by errors between observers. If the feature points for angle measurement can be provided in advance with the algorithm developed through the deep learning method, it is thought t...

Explainable emphysema detection on chest radiographs with deep learning.

PloS one
We propose a deep learning system to automatically detect four explainable emphysema signs on frontal and lateral chest radiographs. Frontal and lateral chest radiographs from 3000 studies were retrospectively collected. Two radiologists annotated th...

Application of deep machine learning for the radiographic diagnosis of periodontitis.

Clinical oral investigations
OBJECTIVE: Successful application of deep machine learning could reduce time-consuming and labor-intensive clinical work of calculating the amount of radiographic bone loss (RBL) in diagnosing and treatment planning for periodontitis. This study aime...

Successful real-world application of an osteoarthritis classification deep-learning model using 9210 knees-An orthopedic surgeon's view.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society
This study aimed to evaluate the performance of a deep-learning model to evaluate knee osteoarthritis using Kellgren-Lawrence grading in real-life knee radiographs. A deep convolutional neural network model was trained using 8964 knee radiographs fro...

Applying Deep Learning to Establish a Total Hip Arthroplasty Radiography Registry: A Stepwise Approach.

The Journal of bone and joint surgery. American volume
BACKGROUND: Establishing imaging registries for large patient cohorts is challenging because manual labeling is tedious and relying solely on DICOM (digital imaging and communications in medicine) metadata can result in errors. We endeavored to estab...

AXEAP: a software package for X-ray emission data analysis using unsupervised machine learning.

Journal of synchrotron radiation
The Argonne X-ray Emission Analysis Package (AXEAP) has been developed to calibrate and process X-ray emission spectroscopy (XES) data collected with a two-dimensional (2D) position-sensitive detector. AXEAP is designed to convert a 2D XES image into...

Simplified Transfer Learning for Chest Radiography Models Using Less Data.

Radiology
Background Developing deep learning models for radiology requires large data sets and substantial computational resources. Data set size limitations can be further exacerbated by distribution shifts, such as rapid changes in patient populations and s...