AIMC Topic: Retrospective Studies

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Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI.

Journal of magnetic resonance imaging : JMRI May 2, 2019
BACKGROUND: Detecting and segmenting brain metastases is a tedious and time-consuming task for many radiologists, particularly with the growing use of multisequence 3D imaging.
Middle Aged Deep Learning Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Humans Brain Retrospective Studies Female Aged Adult Sensitivity and Specificity Brain Neoplasms Male
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Use of machine learning techniques in the development and refinement of a predictive model for early diagnosis of ankylosing spondylitis.

Clinical rheumatology May 1, 2019
OBJECTIVE: To develop a predictive mathematical model for the early identification of ankylosing spondylitis (AS) based on the medical and pharmacy claims history of patients with and without AS.
Machine Learning Databases, Factual Retrospective Studies Female Aged Humans Young Adult Adolescent Adult Models, Theoretical Child Male Early Diagnosis United States Middle Aged Spondylitis, Ankylosing Risk Assessment
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A deep learning radiomics model for preoperative grading in meningioma.

European journal of radiology May 1, 2019
OBJECTIVES: To noninvasively differentiate meningioma grades by deep learning radiomics (DLR) model based on routine post-contrast MRI.
Humans Aged Young Adult Adult Sensitivity and Specificity Male Algorithms Prognosis Retrospective Studies Female Reproducibility of Results Meninges Area Under Curve Neoplasm Grading Preoperative Care Middle Aged Meningioma Meningeal Neoplasms Deep Learning Adolescent Cohort Studies Magnetic Resonance Imaging
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Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network.

European radiology Apr 30, 2019
OBJECTIVES: To evaluate the performance of a novel three-dimensional (3D) joint convolutional and recurrent neural network (CNN-RNN) for the detection of intracranial hemorrhage (ICH) and its five subtypes (cerebral parenchymal, intraventricular, sub...
Male Humans Middle Aged Algorithms Infant Child, Preschool Retrospective Studies Deep Learning Reproducibility of Results Child Adolescent Tomography, X-Ray Computed Aged Imaging, Three-Dimensional Female Aged, 80 and over Intracranial Hemorrhages Young Adult Adult
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Effect of Tube Voltage on Diagnostic Performance of Fractional Flow Reserve Derived From Coronary CT Angiography With Machine Learning: Results From the MACHINE Registry.

AJR. American journal of roentgenology Apr 30, 2019
Coronary CT angiography (CCTA)-based methods allow noninvasive estimation of fractional flow reserve (cFFR), recently through use of a machine learning (ML) algorithm (cFFR). However, attenuation values vary according to the tube voltage used, and i...
Humans Machine Learning Retrospective Studies Female Coronary Disease Male Aged Computed Tomography Angiography Sensitivity and Specificity Middle Aged Fractional Flow Reserve, Myocardial Predictive Value of Tests Coronary Angiography Registries
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Automated semantic labeling of pediatric musculoskeletal radiographs using deep learning.

Pediatric radiology Apr 30, 2019
BACKGROUND: 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 imagin...
Workflow Musculoskeletal Diseases Automation Semantics Neural Networks, Computer Child Machine Learning Male Databases, Factual ROC Curve Deep Learning Area Under Curve Retrospective Studies Radiography Clinical Competence Humans Female Radiologists Adolescent Child, Preschool
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Deep Learning for Diagnosis of Chronic Myocardial Infarction on Nonenhanced Cardiac Cine MRI.

Radiology Apr 30, 2019
Background Renal impairment is common in patients with coronary artery disease and, if severe, late gadolinium enhancement (LGE) imaging for myocardial infarction (MI) evaluation cannot be performed. Purpose To develop a fully automatic framework for...
Image Interpretation, Computer-Assisted Magnetic Resonance Imaging, Cine Retrospective Studies Female Aged Adult Sensitivity and Specificity Male Middle Aged Deep Learning Chronic Disease Myocardial Infarction Humans Databases, Factual
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Classification of degenerative parkinsonism subtypes by support-vector-machine analysis and striatal I-FP-CIT indices.

Journal of neurology Apr 29, 2019
OBJECTIVES: To provide an automated classification method for degenerative parkinsonian syndromes (PS) based on semiquantitative I-FP-CIT SPECT striatal indices and support-vector-machine (SVM) analysis.
Support Vector Machine Iodine Radioisotopes Parkinsonian Disorders Corpus Striatum Tomography, Emission-Computed, Single-Photon Aged Aged, 80 and over Adult Male Middle Aged Humans Retrospective Studies Female
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Extensive phenotype data and machine learning in prediction of mortality in acute coronary syndrome - the MADDEC study.

Annals of medicine Apr 27, 2019
Investigation of the clinical potential of extensive phenotype data and machine learning (ML) in the prediction of mortality in acute coronary syndrome (ACS). The value of ML and extensive clinical data was analyzed in a retrospective registry stud...
Comorbidity Female Logistic Models Aged Coronary Angiography Humans Male Registries Middle Aged Machine Learning Acute Coronary Syndrome Risk Assessment Phenotype Retrospective Studies Electronic Health Records
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Directional Relationship Between Vitamin D Status and Prediabetes: A New Approach from Artificial Neural Network in a Cohort of Workers with Overweight-Obesity.

Journal of the American College of Nutrition Apr 25, 2019
Despite the increasing literature on the association of diabetes with inflammation, cardiovascular risk, and vitamin D (25(OH)D) concentrations, strong evidence on the direction of causality among these factors is still lacking. This gap could be ad...
Vitamin D Vitamin D Deficiency Data Mining Prediabetic State Adult Male Middle Aged Cohort Studies Overweight Humans Neural Networks, Computer Retrospective Studies Female Aged
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