AIMC Topic: Radiography

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Machine learning for detecting moyamoya disease in plain skull radiography using a convolutional neural network.

EBioMedicine Dec 29, 2018
BACKGROUND: Recently, innovative attempts have been made to identify moyamoya disease (MMD) by focusing on the morphological differences in the head of MMD patients. Following the recent revolution in the development of deep learning (DL) algorithms,...
Humans Skull Moyamoya Disease Data Interpretation, Statistical ROC Curve Adult Radiography Image Processing, Computer-Assisted Male Neural Networks, Computer Middle Aged Machine Learning Algorithms Female Reproducibility of Results Young Adult
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Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI.

Journal of magnetic resonance imaging : JMRI Dec 21, 2018
Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep-learning algorithms have shown groundbreaking performance in a variety ...
Neural Networks, Computer Artificial Intelligence Machine Learning Humans Magnetic Resonance Imaging Radiography Radiology Diagnostic Tests, Routine Algorithms Deep Learning Image Processing, Computer-Assisted
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The RSNA Pediatric Bone Age Machine Learning Challenge.

Radiology Nov 27, 2018
Purpose The Radiological Society of North America (RSNA) Pediatric Bone Age Machine Learning Challenge was created to show an application of machine learning (ML) and artificial intelligence (AI) in medical imaging, promote collaboration to catalyze ...
Hand Bones Radiography Humans Image Interpretation, Computer-Assisted Machine Learning Age Determination by Skeleton Female Algorithms Male Databases, Factual Child
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Refining Convolutional Neural Network Detection of Small-Bowel Obstruction in Conventional Radiography.

AJR. American journal of roentgenology Nov 26, 2018
OBJECTIVE: The purpose of this study was to evaluate improvement of convolutional neural network detection of high-grade small-bowel obstruction on conventional radiographs with increased training set size.
Humans Neural Networks, Computer Retrospective Studies Female Aged Radiography Intestinal Obstruction Male Intestine, Small Middle Aged Aged, 80 and over Adolescent Young Adult Adult
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Artificial intelligence in sports medicine radiology: what's coming?

British journal of sports medicine Nov 22, 2018
Radiography Sports Medicine Humans Artificial Intelligence
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A dataset of clinically generated visual questions and answers about radiology images.

Scientific data Nov 20, 2018
Radiology images are an essential part of clinical decision making and population screening, e.g., for cancer. Automated systems could help clinicians cope with large amounts of images by answering questions about the image contents. An emerging area...
Data Mining Radiology Information Systems Algorithms Image Processing, Computer-Assisted Radiography Humans Machine Learning Data Analysis
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Ossification area localization in pediatric hand radiographs using deep neural networks for object detection.

PloS one Nov 16, 2018
BACKGROUND: Detection of ossification areas of hand bones in X-ray images is an important task, e.g. as a preprocessing step in automated bone age estimation. Deep neural networks have emerged recently as de facto standard detection methods, but thei...
Osteogenesis Data Curation Adolescent Infant, Newborn Child, Preschool Humans Image Processing, Computer-Assisted Male Neural Networks, Computer Infant Radiography Female Hand Child
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Deep Learning for Triage of Chest Radiographs: Should Every Institution Train Its Own System?

Radiology Nov 13, 2018
Neural Networks, Computer Deep Learning Triage Radiography
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Annotation of enhanced radiographs for medical image retrieval with deep convolutional neural networks.

PloS one Nov 12, 2018
The number of images taken per patient scan has rapidly increased due to advances in software, hardware and digital imaging in the medical domain. There is the need for medical image annotation systems that are accurate as manual annotation is imprac...
Neural Networks, Computer Machine Learning Algorithms Diagnosis, Computer-Assisted Humans Image Processing, Computer-Assisted Radiography
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Forensic age estimation for pelvic X-ray images using deep learning.

European radiology Nov 6, 2018
PURPOSE: To develop a deep learning bone age assessment model based on pelvic radiographs for forensic age estimation and compare its performance to that of the existing cubic regression model.
Young Adult Adult Male Humans Retrospective Studies Female Pelvic Bones Radiography Age Determination by Skeleton Deep Learning Adolescent Child
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