AIMC Topic: Sensitivity and Specificity

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Correlation-Based Ensemble Feature Selection Using Bioinspired Algorithms and Classification Using Backpropagation Neural Network.

Computational and mathematical methods in medicine Sep 23, 2019
A framework for clinical diagnosis which uses bioinspired algorithms for feature selection and gradient descendant backpropagation neural network for classification has been designed and implemented. The clinical data are subjected to data preprocess...
Hepatitis Pattern Recognition, Automated Breast Neoplasms Algorithms Humans Diagnosis, Computer-Assisted Computer Simulation Neural Networks, Computer Computational Biology Reproducibility of Results Machine Learning Sensitivity and Specificity Support Vector Machine Software
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Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction Without Volume Measurements Using a Machine Learning Algorithm Mimicking a Human Expert.

Circulation. Cardiovascular imaging Sep 16, 2019
BACKGROUND: Echocardiographic quantification of left ventricular (LV) ejection fraction (EF) relies on either manual or automated identification of endocardial boundaries followed by model-based calculation of end-systolic and end-diastolic LV volume...
Male Ventricular Function, Left Stroke Volume Echocardiography Automation Female Reproducibility of Results Humans Aged Machine Learning Sensitivity and Specificity Retrospective Studies
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A superpixel-driven deep learning approach for the analysis of dermatological wounds.

Computer methods and programs in biomedicine Sep 13, 2019
BACKGROUND: The image-based identification of distinct tissues within dermatological wounds enhances patients' care since it requires no intrusive evaluations. This manuscript presents an approach, we named QTDU, that combines deep learning models wi...
Deep Learning Image Processing, Computer-Assisted Support Vector Machine Principal Component Analysis Area Under Curve Bayes Theorem Dermatology Pattern Recognition, Automated Skin Ulcer Algorithms Humans Reproducibility of Results Machine Learning Sensitivity and Specificity Diagnosis, Computer-Assisted
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Identifying pulmonary nodules or masses on chest radiography using deep learning: external validation and strategies to improve clinical practice.

Clinical radiology Sep 11, 2019
AIM: To test the diagnostic performance of a deep learning-based system for the detection of clinically significant pulmonary nodules/masses on chest radiographs.
Multiple Pulmonary Nodules Solitary Pulmonary Nodule Taiwan Radiographic Image Interpretation, Computer-Assisted Quality Improvement Radiography, Thoracic Middle Aged Diagnosis, Computer-Assisted Tomography, X-Ray Computed Deep Learning Lung Neoplasms Female Sensitivity and Specificity Male Software Algorithms Humans Retrospective Studies
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LDAPred: A Method Based on Information Flow Propagation and a Convolutional Neural Network for the Prediction of Disease-Associated lncRNAs.

International journal of molecular sciences Sep 10, 2019
Long non-coding RNAs (lncRNAs) play a crucial role in the pathogenesis and development of complex diseases. Predicting potential lncRNA-disease associations can improve our understanding of the molecular mechanisms of human diseases and help identify...
Genetic Predisposition to Disease Computational Biology MicroRNAs RNA, Long Noncoding Algorithms Reproducibility of Results Sensitivity and Specificity Neoplasms Humans Neural Networks, Computer
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Automatic detection of epileptic seizure based on approximate entropy, recurrence quantification analysis and convolutional neural networks.

Artificial intelligence in medicine Sep 7, 2019
Epilepsy is the most common neurological disorder in humans. Electroencephalogram is a prevalent tool for diagnosing the epileptic seizure activity in clinical, which provides valuable information for understanding the physiological mechanisms behind...
Sensitivity and Specificity Electroencephalography Humans Epilepsy Neural Networks, Computer Seizures Recurrence Algorithms Automation Entropy Databases, Factual
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Utilizing supervised machine learning to identify microglia and astrocytes in situ: implications for large-scale image analysis and quantification.

Journal of neuroscience methods Sep 5, 2019
BACKGROUND: The evaluation of histological tissue samples plays a crucial role in deciphering preclinical disease and injury mechanisms. High-resolution images can be obtained quickly however data acquisition are often bottlenecked by manual analysis...
Animals, Genetically Modified Reproducibility of Results Fluorescent Antibody Technique Rats Sensitivity and Specificity Histological Techniques Animals Microglia Neurosciences Image Processing, Computer-Assisted Astrocytes Mice Supervised Machine Learning
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Prevalence and Predictability of Low-Yield Inpatient Laboratory Diagnostic Tests.

JAMA network open Sep 4, 2019
IMPORTANCE: Laboratory testing is an important target for high-value care initiatives, constituting the highest volume of medical procedures. Prior studies have found that up to half of all inpatient laboratory tests may be medically unnecessary, but...
Hospitalization Aged Adult Sensitivity and Specificity Male Humans Machine Learning Retrospective Studies Female Blood Urea Nitrogen Leukocyte Count Clinical Laboratory Techniques Glycated Hemoglobin Hemoglobins Troponin I L-Lactate Dehydrogenase Middle Aged ROC Curve Predictive Value of Tests Area Under Curve
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Predicting lung nodule malignancies by combining deep convolutional neural network and handcrafted features.

Physics in medicine and biology Sep 4, 2019
To predict lung nodule malignancy with a high sensitivity and specificity for low dose CT (LDCT) lung cancer screening, we propose a fusion algorithm that combines handcrafted features (HF) into the features learned at the output layer of a 3D deep c...
Deep Learning Lung Neoplasms Image Processing, Computer-Assisted Early Detection of Cancer Humans Signal-To-Noise Ratio Databases, Factual Tomography, X-Ray Computed Sensitivity and Specificity
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Semi-automatic classification of prostate cancer on multi-parametric MR imaging using a multi-channel 3D convolutional neural network.

European radiology Aug 29, 2019
OBJECTIVE: To present a deep learning-based approach for semi-automatic prostate cancer classification based on multi-parametric magnetic resonance (MR) imaging using a 3D convolutional neural network (CNN).
Prostatic Neoplasms Male Humans Biopsy Deep Learning Sensitivity and Specificity Diffusion Magnetic Resonance Imaging ROC Curve Multiparametric Magnetic Resonance Imaging Area Under Curve
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