Pediatric radiology
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Pediatric radiology · Jul 2019
Pediatric optic nerve and optic nerve sheath diameter on magnetic resonance imaging.
The normal values of optic nerve diameter and optic nerve sheath diameter might be beneficial in defining an abnormality such as optic nerve hypoplasia, or enlarged subarachnoid space, reflecting the state of increased intracranial pressure. ⋯ Seventy-four of the 77 measurements (96%) were of the measurements were above the threshold of 2 mm for optic nerve diameter. Seventy-seven of the 79 measurements (97%) were of the measurements were below the threshold of 6 mm for optic nerve sheath diameter.
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Pediatric radiology · Jul 2019
Automated semantic labeling of pediatric musculoskeletal radiographs using deep learning.
An automated method for identifying the anatomical region of an image independent of metadata labels could improve radiologist workflow (e.g., automated hanging protocols) and help facilitate the automated curation of large medical imaging data sets for machine learning purposes. Deep learning is a potential tool for this purpose. ⋯ DCNNs trained on a small set of images with 30 times augmentation through standard processing techniques are able to automatically classify pediatric musculoskeletal radiographs into anatomical region with near-perfect to perfect accuracy at superhuman speeds. This concept may apply to other body parts and radiographic views with the potential to create an all-encompassing semantic-labeling DCNN.
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Pediatric radiology · Apr 2019
Microwave ablation of osteoid osteoma: initial experience and efficacy.
Image-guided percutaneous microwave ablation has been used to treat adult osteoid osteomas but has not been thoroughly evaluated in the pediatric population. ⋯ Microwave ablation is a technically feasible and clinically effective treatment for pediatric osteoid osteomas.
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Pediatric radiology · Apr 2019
ReviewMachine learning concepts, concerns and opportunities for a pediatric radiologist.
Machine learning, a subfield of artificial intelligence, is a rapidly evolving technology that offers great potential for expanding the quality and value of pediatric radiology. We describe specific types of learning, including supervised, unsupervised and semisupervised. Subsequently, we illustrate two core concepts for the reader: data partitioning and under/overfitting. ⋯ These include the requirement for very large data sets, the need to accurately label these images with a relatively small number of pediatric imagers, technical and regulatory hurdles, as well as the opaque character of convolution neural networks. We review machine learning cases in radiology including detection, classification and segmentation. Last, three pediatric radiologists from the Society for Pediatric Radiology Quality and Safety Committee share perspectives for potential areas of development.
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Pediatric radiology · Feb 2019
Comparative StudyContrast-enhanced MRI compared to direct joint visualization at arthroscopy in pediatric patients with suspected temporomandibular joint synovitis.
Contrast-enhanced magnetic resonance imaging (MRI) has become the gold standard when assessing the temporomandibular joint (TMJ) in children. To our knowledge, no previous pediatric study has compared findings of TMJ MRI with direct visualization of the joint using arthroscopy. ⋯ Joint space width and subjective synovitis on TMJ MRI correlate with arthroscopic findings of chronic synovitis. Increased joint space width may be useful when evaluating the TMJ with less time-intensive modalities, such as ultrasound. However, MRI findings did not correlate well with findings of acute inflammation on arthroscopy.