AIMC Topic: Radiography, Abdominal

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Population-Scale CT-based Body Composition Analysis of a Large Outpatient Population Using Deep Learning to Derive Age-, Sex-, and Race-specific Reference Curves.

Radiology Nov 24, 2020
Background Although CT-based body composition (BC) metrics may inform disease risk and outcomes, obtaining these metrics has been too resource intensive for large-scale use. Thus, population-wide distributions of BC remain uncertain. Purpose To demon...
Cross-Sectional Studies Male Middle Aged Deep Learning Humans Tomography, X-Ray Computed Female Reproducibility of Results Racial Groups Outpatients Radiography, Abdominal Age Distribution Sex Distribution Cohort Studies Image Processing, Computer-Assisted Body Composition Reference Values
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Extraction of radiographic findings from unstructured thoracoabdominal computed tomography reports using convolutional neural network based natural language processing.

PloS one Jul 30, 2020
BACKGROUND: Heart failure (HF) is a major cause of morbidity and mortality. However, much of the clinical data is unstructured in the form of radiology reports, while the process of data collection and curation is arduous and time-consuming.
Humans Neural Networks, Computer Tomography, X-Ray Computed Electronic Health Records Female Machine Learning Radiography, Abdominal Image Processing, Computer-Assisted Prognosis Natural Language Processing Heart Failure Aged Radiography, Thoracic Survival Rate Male Cohort Studies
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The effect of deep convolutional neural networks on radiologists' performance in the detection of hip fractures on digital pelvic radiographs.

European journal of radiology Jul 23, 2020
PURPOSE: The purpose of our study is to develop deep convolutional neural network (DCNN) for detecting hip fractures using CT and MRI as a gold standard, and to evaluate the diagnostic performance of 7 readers with and without DCNN.
Deep Learning ROC Curve Image Processing, Computer-Assisted Aged Magnetic Resonance Imaging Humans Hip Fractures Aged, 80 and over Area Under Curve Neural Networks, Computer Radiographic Image Enhancement Male Pelvis Tomography, X-Ray Computed Radiography, Abdominal Middle Aged Female
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4D-CT deformable image registration using multiscale unsupervised deep learning.

Physics in medicine and biology Apr 20, 2020
Deformable image registration (DIR) of 4D-CT images is important in multiple radiation therapy applications including motion tracking of soft tissue or fiducial markers, target definition, image fusion, dose accumulation and treatment response evalua...
Humans Deep Learning Image Processing, Computer-Assisted Radiotherapy Planning, Computer-Assisted Radiography, Abdominal Four-Dimensional Computed Tomography Neural Networks, Computer Algorithms Retrospective Studies Neoplasms
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Image Quality Assessment of Abdominal CT by Use of New Deep Learning Image Reconstruction: Initial Experience.

AJR. American journal of roentgenology Apr 14, 2020
The purpose of this study was to perform quantitative and qualitative evaluation of a deep learning image reconstruction (DLIR) algorithm in contrast-enhanced oncologic CT of the abdomen. Retrospective review (April-May 2019) of the cases of adults...
Radiation Dosage Radiography, Abdominal Digestive System Neoplasms Thoracic Neoplasms Contrast Media Radiographic Image Interpretation, Computer-Assisted Iohexol Tomography, X-Ray Computed Humans Female Retrospective Studies Aged Adult Aged, 80 and over Male Middle Aged Deep Learning
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Image Quality and Lesion Detection on Deep Learning Reconstruction and Iterative Reconstruction of Submillisievert Chest and Abdominal CT.

AJR. American journal of roentgenology Jan 22, 2020
The objective of this study was to compare image quality and clinically significant lesion detection on deep learning reconstruction (DLR) and iterative reconstruction (IR) images of submillisievert chest and abdominopelvic CT. Our prospective mult...
Radiography, Thoracic Radiography, Abdominal Imaging, Three-Dimensional Humans Contrast Media Prospective Studies Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Radiation Dosage Male Female Middle Aged Aged Deep Learning
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Performance of deep learning-based algorithm for detection of ileocolic intussusception on abdominal radiographs of young children.

Scientific reports Dec 19, 2019
The purpose of this study was to develop and test the performance of a deep learning-based algorithm to detect ileocolic intussusception using abdominal radiographs of young children. For the training set, children (≤5 years old) who underwent abdomi...
Intussusception Radiography, Abdominal Infant Deep Learning Image Processing, Computer-Assisted Ileal Diseases Humans Algorithms Retrospective Studies
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Automated Liver Fat Quantification at Nonenhanced Abdominal CT for Population-based Steatosis Assessment.

Radiology Sep 17, 2019
Background Nonalcoholic fatty liver disease and its consequences are a growing public health concern requiring cross-sectional imaging for noninvasive diagnosis and quantification of liver fat. Purpose To investigate a deep learning-based automated l...
Radiography, Abdominal Male Middle Aged Prevalence Deep Learning Humans Non-alcoholic Fatty Liver Disease Retrospective Studies Tomography, X-Ray Computed Female
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MRI-based treatment planning for proton radiotherapy: dosimetric validation of a deep learning-based liver synthetic CT generation method.

Physics in medicine and biology Jul 16, 2019
Magnetic resonance imaging (MRI) has been widely used in combination with computed tomography (CT) radiation therapy because MRI improves the accuracy and reliability of target delineation due to its superior soft tissue contrast over CT. The MRI-onl...
Magnetic Resonance Imaging Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiography, Abdominal Deep Learning Humans Liver Neoplasms Retrospective Studies Carcinoma, Hepatocellular Tomography, X-Ray Computed Image Processing, Computer-Assisted Reproducibility of Results
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Holistic decomposition convolution for effective semantic segmentation of medical volume images.

Medical image analysis Jul 8, 2019
Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in many different 2D medical image analysis tasks. In clinical practice, however, a large part of the medical imaging data available is in 3D, e.g, magnetic resonance ima...
Datasets as Topic Pancreas Radiography, Abdominal Hip Dislocation Imaging, Three-Dimensional Humans Image Processing, Computer-Assisted Neural Networks, Computer Magnetic Resonance Imaging Algorithms Artifacts Tomography, X-Ray Computed
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