AIMC Topic: Radiography

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Application of deep learning algorithm to detect and visualize vertebral fractures on plain frontal radiographs.

PloS one Jan 28, 2021
BACKGROUND: Identification of vertebral fractures (VFs) is critical for effective secondary fracture prevention owing to their association with the increasing risks of future fractures. Plain abdominal frontal radiographs (PARs) are a common investig...
Deep Learning Radiography Image Interpretation, Computer-Assisted Algorithms Spinal Fractures Humans Databases, Factual Neural Networks, Computer Sensitivity and Specificity
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How does the radiology community discuss the benefits and limitations of artificial intelligence for their work? A systematic discourse analysis.

European journal of radiology Jan 26, 2021
PURPOSE: We aimed to systematically analyse how the radiology community discusses the concept of artificial intelligence (AI), perceives its benefits, and reflects on its limitations.
Forecasting Radiography Radiology Humans Artificial Intelligence
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A semantic database for integrated management of image and dosimetric data in low radiation dose research in medical imaging.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
Medical ionizing radiation procedures and especially medical imaging are a non negligible source of exposure to patients. Whereas the biological effects of high absorbed doses are relatively well known, the effects of low absorbed doses are still deb...
Database Management Systems Radiation Dosage Radiometry Radiography Humans Algorithms Documentation Diagnostic Imaging Databases, Factual Machine Learning Semantics Data Mining Natural Language Processing Terminology as Topic
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Artificial intelligence for detection of periapical lesions on intraoral radiographs: Comparison between convolutional neural networks and human observers.

Oral surgery, oral medicine, oral pathology and oral radiology Jan 22, 2021
OBJECTIVE: The aim of this study was to compare the diagnostic performance of convolutional neural networks (CNNs) with the performance of human observers for the detection of simulated periapical lesions on periapical radiographs.
Cattle ROC Curve Radiography Neural Networks, Computer Artificial Intelligence Animals Radiologists Humans
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Review of deep learning algorithms for the automatic detection of intracranial hemorrhages on computed tomography head imaging.

Journal of neurointerventional surgery Jan 21, 2021
Artificial intelligence is a rapidly evolving field, with modern technological advances and the growth of electronic health data opening new possibilities in diagnostic radiology. In recent years, the performance of deep learning (DL) algorithms on v...
Humans Radiography Head Artificial Intelligence Neuroimaging Algorithms Intracranial Hemorrhages Tomography, X-Ray Computed Deep Learning
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Five principles for the intelligent use of AI in medical imaging.

Intensive care medicine Jan 15, 2021
Radiography Diagnostic Imaging Intelligence Humans Artificial Intelligence
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Artificial Intelligence of COVID-19 Imaging: A Hammer in Search of a Nail.

Radiology Dec 22, 2020
Humans Artificial Intelligence COVID-19 SARS-CoV-2 Deep Learning Radiography
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A review on medical imaging synthesis using deep learning and its clinical applications.

Journal of applied clinical medical physics Dec 11, 2020
This paper reviewed the deep learning-based studies for medical imaging synthesis and its clinical application. Specifically, we summarized the recent developments of deep learning-based methods in inter- and intra-modality image synthesis by listing...
Diagnostic Imaging Deep Learning Image Processing, Computer-Assisted Radiography Humans Research Design
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Proposals for the use of artificial intelligence in emergency radiology.

Diagnostic and interventional imaging Dec 2, 2020
Humans Artificial Intelligence Radiography Radiology
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Training confounder-free deep learning models for medical applications.

Nature communications Nov 26, 2020
The presence of confounding effects (or biases) is one of the most critical challenges in using deep learning to advance discovery in medical imaging studies. Confounders affect the relationship between input data (e.g., brain MRIs) and output variab...
Cohort Studies Humans Brain Child Age Factors Young Adult Female Magnetic Resonance Imaging Adult Deep Learning Image Interpretation, Computer-Assisted Middle Aged Hand Aged Predictive Value of Tests Male Datasets as Topic Adolescent Aged, 80 and over Sex Characteristics Case-Control Studies Age Determination by Skeleton HIV Infections Radiography Sex Factors
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