AIMC Topic: Sensitivity and Specificity

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Assessment of Electrocardiogram Rhythms by GoogLeNet Deep Neural Network Architecture.

Journal of healthcare engineering Apr 28, 2019
The aim of this study is to design GoogLeNet deep neural network architecture by expanding the kernel size of the inception layer and combining the convolution layers to classify the electrocardiogram (ECG) beats into a normal sinus rhythm, premature...
Electrocardiography Humans Signal Processing, Computer-Assisted Databases, Factual Internet Sensitivity and Specificity Arrhythmias, Cardiac Deep Learning
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Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI.

European radiology Apr 23, 2019
OBJECTIVES: To develop and validate a proof-of-concept convolutional neural network (CNN)-based deep learning system (DLS) that classifies common hepatic lesions on multi-phasic MRI.
Humans Neural Networks, Computer Female Reproducibility of Results Cholangiocarcinoma Aged Carcinoma, Hepatocellular Bile Ducts, Intrahepatic Adult United States Sensitivity and Specificity Magnetic Resonance Imaging Male Image Interpretation, Computer-Assisted Bile Duct Neoplasms Middle Aged Deep Learning ROC Curve Liver Neoplasms
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Accuracy of American College of Surgeons National Surgical Quality Improvement Program Universal Surgical Risk Calculator in Predicting Complications Following Robot-Assisted Radical Cystectomy at a National Comprehensive Cancer Center.

Journal of endourology Apr 22, 2019
There is paucity of literature about the validation of the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) surgical risk calculator for prediction of outcomes after robot-assisted radical cystectomy (RARC). We ...
Decision Support Techniques Retrospective Studies Female Aged Aged, 80 and over Humans United States Robotics Area Under Curve Quality Improvement Cystectomy Sensitivity and Specificity Male Risk Assessment Postoperative Complications ROC Curve
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Simulating Dual-Energy X-Ray Absorptiometry in CT Using Deep-Learning Segmentation Cascade.

Journal of the American College of Radiology : JACR Apr 12, 2019
PURPOSE: Osteoporosis is an underdiagnosed condition despite effective screening modalities. Dual-energy x-ray absorptiometry (DEXA) screening, although recommended in clinical guidelines, remains markedly underutilized. In contrast to DEXA, CT utili...
Lumbar Vertebrae Absorptiometry, Photon Sensitivity and Specificity Male Humans Middle Aged Osteoporosis Computer Simulation Deep Learning Tomography, X-Ray Computed Bone Density Female Aged Aged, 80 and over
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Lungs nodule detection framework from computed tomography images using support vector machine.

Microscopy research and technique Apr 11, 2019
The emergence of cloud infrastructure has the potential to provide significant benefits in a variety of areas in the medical imaging field. The driving force behind the extensive use of cloud infrastructure for medical image processing is the exponen...
Lung Image Processing, Computer-Assisted Lung Neoplasms Support Vector Machine False Positive Reactions Humans Tomography, X-Ray Computed Sensitivity and Specificity
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Predicting daily outcomes in acetaminophen-induced acute liver failure patients with machine learning techniques.

Computer methods and programs in biomedicine Apr 11, 2019
BACKGROUND/OBJECTIVE: Assessing prognosis for acetaminophen-induced acute liver failure (APAP-ALF) patients during the first week of hospitalization often presents significant challenges. Current models such as the King's College Criteria (KCC) and t...
Liver Failure, Acute Area Under Curve Bayes Theorem Registries Linear Models Severity of Illness Index Acetaminophen ROC Curve Female Humans Treatment Outcome Machine Learning Adult Predictive Value of Tests Databases, Factual Sensitivity and Specificity Prognosis Male Retrospective Studies Middle Aged
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Application of Artificial Neural Networks to Identify Alzheimer's Disease Using Cerebral Perfusion SPECT Data.

International journal of environmental research and public health Apr 11, 2019
The aim of this study was to demonstrate the usefulness of artificial neural networks in Alzheimer disease diagnosis (AD) using data of brain single photon emission computed tomography (SPECT). The results were compared with discriminant analysis. Th...
Male Middle Aged Case-Control Studies ROC Curve Discriminant Analysis Female Aged Aged, 80 and over Sensitivity and Specificity Alzheimer Disease Tomography, Emission-Computed, Single-Photon Humans Neural Networks, Computer
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Statistical characterization and classification of colon microarray gene expression data using multiple machine learning paradigms.

Computer methods and programs in biomedicine Apr 10, 2019
OBJECTIVE: A colon microarray data is a repository of thousands of gene expressions with different strengths for each cancer cell. It is necessary to detect which genes are responsible for cancer growth. This study presents an exhaustive comparative ...
Colonic Neoplasms Risk Normal Distribution Oncogenes Neural Networks, Computer Tissue Array Analysis Machine Learning Gene Expression Profiling Humans Decision Trees Sensitivity and Specificity Logistic Models Colon Area Under Curve Support Vector Machine Bayes Theorem Models, Statistical Discriminant Analysis Regression Analysis
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Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task.

European journal of cancer (Oxford, England : 1990) Apr 10, 2019
BACKGROUND: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For ...
Dermoscopy Sensitivity and Specificity Deep Learning Dermatologists Hospitals, University Germany Nevus Humans Melanoma Skin Neoplasms
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Plaque components segmentation in carotid artery on simultaneous non-contrast angiography and intraplaque hemorrhage imaging using machine learning.

Magnetic resonance imaging Apr 5, 2019
PURPOSE: This study sought to determine the feasibility of using Simultaneous Non-contrast Angiography and intraPlaque Hemorrhage (SNAP) to detect the lipid-rich/necrotic core (LRNC), and develop a machine learning based algorithm to segment plaque c...
Necrosis Carotid Stenosis Plaque, Amyloid Contrast Media Calcinosis Humans Reproducibility of Results Lipids Machine Learning Aged Cerebral Hemorrhage Bayes Theorem Algorithms Sensitivity and Specificity Carotid Arteries Male Female Carotid Artery, Common Middle Aged Plaque, Atherosclerotic Angiography
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