AIMC Topic: Radiographic Image Interpretation, Computer-Assisted

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Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3D CT data.

Medical image analysis Aug 3, 2018
This paper aims to address the segmentation and classification of lytic and sclerotic metastatic lesions that are difficult to define by using spinal 3D Computed Tomography (CT) images obtained from highly pathologically affected cases. As the lesion...
Female Aged Aged, 80 and over Male Humans Neural Networks, Computer Tomography, X-Ray Computed Middle Aged Spinal Neoplasms Imaging, Three-Dimensional Radiographic Image Interpretation, Computer-Assisted
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Transfer Learning From Convolutional Neural Networks for Computer-Aided Diagnosis: A Comparison of Digital Breast Tomosynthesis and Full-Field Digital Mammography.

Academic radiology Aug 1, 2018
RATIONALE AND OBJECTIVES: With the growing adoption of digital breast tomosynthesis (DBT) in breast cancer screening, we compare the performance of deep learning computer-aided diagnosis on DBT images to that of conventional full-field digital mammog...
Radiographic Image Interpretation, Computer-Assisted Retrospective Studies Breast Female Breast Neoplasms Humans Middle Aged Neural Networks, Computer ROC Curve Machine Learning Mammography Aged Adult
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Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability.

Skeletal radiology Aug 1, 2018
OBJECTIVE: Radiographic bone age assessment (BAA) is used in the evaluation of pediatric endocrine and metabolic disorders. We previously developed an automated artificial intelligence (AI) deep learning algorithm to perform BAA using convolutional n...
Artificial Intelligence Algorithms Retrospective Studies Female Humans Male Deep Learning Adolescent Child, Preschool Child Radiographic Image Interpretation, Computer-Assisted Bone Diseases, Metabolic Age Determination by Skeleton
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Deep Convolutional Neural Networks for Chest Diseases Detection.

Journal of healthcare engineering Aug 1, 2018
Chest diseases are very serious health problems in the life of people. These diseases include chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, and lung diseases. The timely diagnosis of chest diseases is very important. Many me...
Radiographic Image Interpretation, Computer-Assisted Humans Radiography, Thoracic Respiratory Tract Diseases Algorithms Deep Learning
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Detection of early infarction signs with machine learning-based diagnosis by means of the Alberta Stroke Program Early CT score (ASPECTS) in the clinical routine.

Neuroradiology Jul 31, 2018
PURPOSE: New software solutions emerged to support radiologists in image interpretation in acute ischemic stroke. This study aimed to validate the performance of computer-aided assessment of the Alberta Stroke Program Early CT score (ASPECTS) for det...
Software Tomography, X-Ray Computed Female Aged Aged, 80 and over Adult Sensitivity and Specificity Male Humans Machine Learning Alberta Stroke Infarction, Middle Cerebral Artery Middle Aged Brain Ischemia Predictive Value of Tests Observer Variation Radiographic Image Interpretation, Computer-Assisted
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Diagnostic performance of machine-learning-based computed fractional flow reserve (FFR) derived from coronary computed tomography angiography for the assessment of myocardial ischemia verified by invasive FFR.

The international journal of cardiovascular imaging Jul 30, 2018
To explore the diagnostic performance of a machine-learning-based (ML-based) computed fractional flow reserve (cFFR) derived from coronary computed tomography angiography (CCTA) in identifying ischemia-causing lesions verified by invasive FFR in cath...
Multidetector Computed Tomography Computed Tomography Angiography Coronary Stenosis Fractional Flow Reserve, Myocardial Cardiac Catheters Predictive Value of Tests Coronary Angiography Radiographic Image Interpretation, Computer-Assisted Coronary Vessels Male Middle Aged Humans Severity of Illness Index Machine Learning Retrospective Studies Female Reproducibility of Results Aged Aged, 80 and over
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Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning.

PloS one Jul 27, 2018
We developed a computer-aided diagnosis (CADx) method for classification between benign nodule, primary lung cancer, and metastatic lung cancer and evaluated the following: (i) the usefulness of the deep convolutional neural network (DCNN) for CADx o...
Radiographic Image Interpretation, Computer-Assisted Neural Networks, Computer Machine Learning Middle Aged Retrospective Studies Tomography, X-Ray Computed Lung Humans Lung Neoplasms Female Aged Male
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Transferability of artificial neural networks for clinical document classification across hospitals: A case study on abnormality detection from radiology reports.

Journal of biomedical informatics Jul 17, 2018
OBJECTIVE: Application of machine learning techniques for automatic and reliable classification of clinical documents have shown promising results. However, machine learning models require abundant training data specific to each target hospital and m...
Deep Learning Computational Biology Support Vector Machine Radiography Machine Learning Algorithms Databases, Factual Radiographic Image Interpretation, Computer-Assisted Humans Neural Networks, Computer
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The past, present and future role of artificial intelligence in imaging.

European journal of radiology Jun 22, 2018
Artificial intelligence (AI) is already widely employed in various medical roles, and ongoing technological advances are encouraging more widespread use of AI in imaging. This is partly driven by the recognition of the significant frequency and clini...
Humans Artificial Intelligence Software Reproducibility of Results Radiology Radiographic Image Interpretation, Computer-Assisted Radiology Information Systems
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Deep learning and conditional random fields-based depth estimation and topographical reconstruction from conventional endoscopy.

Medical image analysis Jun 14, 2018
Colorectal cancer is the fourth leading cause of cancer deaths worldwide and the second leading cause in the United States. The risk of colorectal cancer can be mitigated by the identification and removal of premalignant lesions through optical colon...
Radiographic Image Interpretation, Computer-Assisted Phantoms, Imaging Colonoscopy Swine Deep Learning Algorithms Tomography, X-Ray Computed Colorectal Neoplasms Animals
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