AIMC Topic: Radiography

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Automated detection of enteric tubes misplaced in the respiratory tract on chest radiographs using deep learning with two centre validation.

Clinical radiology Jul 15, 2022
AIM: To develop and test a model based on a convolutional neural network that can identify enteric tube position accurately on chest radiography.
Deep Learning Radiography Respiratory System Radiography, Thoracic Retrospective Studies Adult Humans Neural Networks, Computer
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Mortality Prediction Analysis among COVID-19 Inpatients Using Clinical Variables and Deep Learning Chest Radiography Imaging Features.

Tomography (Ann Arbor, Mich.) Jul 13, 2022
The emergence of the COVID-19 pandemic over a relatively brief interval illustrates the need for rapid data-driven approaches to facilitate clinical decision making. We examined a machine learning process to predict inpatient mortality among COVID-19...
Inpatients Humans Radiography COVID-19 Pandemics Deep Learning
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Non-iterative learning machine for identifying CoViD19 using chest X-ray images.

Scientific reports Jul 13, 2022
CoViD19 is a novel disease which has created panic worldwide by infecting millions of people around the world. The last significant variant of this virus, called as omicron, contributed to majority of cases in the third wave across globe. Though less...
X-Rays Radiography Thorax Humans Neural Networks, Computer SARS-CoV-2 Deep Learning COVID-19
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Radiographers' knowledge, attitudes and expectations of artificial intelligence in medical imaging.

Radiography (London, England : 1995) Jul 12, 2022
INTRODUCTION: Artificial intelligence (AI) is increasingly utilised in medical imaging systems and processes, and radiographers must embrace this advancement. This study aimed to investigate perceptions, knowledge, and expectations towards integratin...
Humans Health Knowledge, Attitudes, Practice Radiography Artificial Intelligence Motivation Cross-Sectional Studies
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Automated evaluation of rheumatoid arthritis from hand radiographs using Machine Learning and deep learning techniques.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine Jul 12, 2022
The aim and objectives of the study are as follows: (i) to implement automated patch-based classification of hand X-ray images using modified pre-trained convolutional neural network (CNN) models; (ii) to develop a customized CNN model for automated ...
Neural Networks, Computer Machine Learning Arthritis, Rheumatoid Radiography Deep Learning Humans
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A Pipeline for the Implementation and Visualization of Explainable Machine Learning for Medical Imaging Using Radiomics Features.

Sensors (Basel, Switzerland) Jul 12, 2022
Machine learning (ML) models have been shown to predict the presence of clinical factors from medical imaging with remarkable accuracy. However, these complex models can be difficult to interpret and are often criticized as "black boxes". Prediction ...
Radiography Machine Learning Humans Glioma Algorithms Magnetic Resonance Imaging
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Deep learning accurately classifies elbow joint effusion in adult and pediatric radiographs.

Scientific reports Jul 12, 2022
Joint effusion due to elbow fractures are common among adults and children. Radiography is the most commonly used imaging procedure to diagnose elbow injuries. The purpose of the study was to investigate the diagnostic accuracy of deep convolutional ...
Retrospective Studies Female Humans Neural Networks, Computer Young Adult Adult Male Aged Aged, 80 and over Child, Preschool Child Elbow Joint Radiography Deep Learning Middle Aged Adolescent Infant
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Analyzing Transfer Learning of Vision Transformers for Interpreting Chest Radiography.

Journal of digital imaging Jul 11, 2022
Limited availability of medical imaging datasets is a vital limitation when using "data hungry" deep learning to gain performance improvements. Dealing with the issue, transfer learning has become a de facto standard, where a pre-trained convolution ...
Child Radiography Neural Networks, Computer Machine Learning Humans
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Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data.

Sensors (Basel, Switzerland) Jul 11, 2022
Privacy regulations and the physical distribution of heterogeneous data are often primary concerns for the development of deep learning models in a medical context. This paper evaluates the feasibility of differentially private federated learning for...
Humans Radiography Privacy X-Rays Neural Networks, Computer ROC Curve
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Artificial Intelligence System for Automatic Quantitative Analysis and Radiology Reporting of Leg Length Radiographs.

Journal of digital imaging Jul 6, 2022
Leg length discrepancies are common orthopedic problems with the potential for poor functional outcomes. These are frequently assessed using bilateral leg length radiographs. The objective was to determine whether an artificial intelligence (AI)-base...
Humans Radiography Artificial Intelligence Radiology Retrospective Studies Leg
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