AIMC Topic: Sensitivity and Specificity

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Quantitative analysis of glycated albumin in serum based on ATR-FTIR spectrum combined with SiPLS and SVM.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy May 7, 2018
A rapid quantitative analysis model for determining the glycated albumin (GA) content based on Attenuated total reflectance (ATR)-Fourier transform infrared spectroscopy (FTIR) combining with linear SiPLS and nonlinear SVM has been developed. Firstly...
Support Vector Machine Spectroscopy, Fourier Transform Infrared Least-Squares Analysis Linear Models Glycation End Products, Advanced Humans Reproducibility of Results Sensitivity and Specificity Serum Albumin Glycated Serum Albumin
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Using machine learning to identify patterns of lifetime health problems in decedents with autism spectrum disorder.

Autism research : official journal of the International Society for Autism Research May 7, 2018
Very little is known about the health problems experienced by individuals with autism spectrum disorder (ASD) throughout their life course. We retrospectively analyzed diagnostic codes associated with de-identified electronic health records using a m...
Infant, Newborn Aged Aged, 80 and over Young Adult Comorbidity Humans Adult Adolescent Machine Learning Child, Preschool Algorithms Sensitivity and Specificity Child Autism Spectrum Disorder Male Retrospective Studies Middle Aged Electronic Health Records Chronic Disease Infant Female Prevalence
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Automated assessment of levodopa-induced dyskinesia: Evaluating the responsiveness of video-based features.

Parkinsonism & related disorders May 5, 2018
INTRODUCTION: Technological solutions for quantifying Parkinson's disease (PD) symptoms may provide an objective means to track response to treatment, including side effects such as levodopa-induced dyskinesia. Vision-based systems are advantageous a...
Deep Learning Parkinson Disease Patient Reported Outcome Measures Video Recording Image Interpretation, Computer-Assisted Sensitivity and Specificity Antiparkinson Agents Humans Biomechanical Phenomena Levodopa Male Female Dyskinesia, Drug-Induced Middle Aged Aged Severity of Illness Index
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Recognition of early stage thigmotaxis in Morris water maze test with convolutional neural network.

PloS one May 3, 2018
The Morris water maze test (MWM) is a useful tool to evaluate rodents' spatial learning and memory, but the outcome is susceptible to various experimental conditions. Thigmotaxis is a commonly observed behavioral pattern which is thought to be relate...
Swimming Carotid Artery, Common Maze Learning Carotid Stenosis Spatial Memory Animals Male Mice Mice, Inbred C57BL Reaction Time Taxis Response Neural Networks, Computer Machine Learning Sensitivity and Specificity
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Small lung nodules detection based on local variance analysis and probabilistic neural network.

Computer methods and programs in biomedicine Apr 28, 2018
BACKGROUND AND OBJECTIVE: In medical examinations doctors use various techniques in order to provide to the patients an accurate analysis of their actual state of health. One of the commonly used methodologies is the x-ray screening. This examination...
Pattern Recognition, Automated False Positive Reactions X-Rays Radiographic Image Enhancement Medical Errors Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Carcinoma Probability Humans Reproducibility of Results Neural Networks, Computer Sensitivity and Specificity Algorithms Lung Databases, Factual Image Processing, Computer-Assisted Lung Neoplasms
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Building predictive in vitro pulmonary toxicity assays using high-throughput imaging and artificial intelligence.

Archives of toxicology Apr 28, 2018
Human lungs are susceptible to the toxicity induced by soluble xenobiotics. However, the direct cellular effects of many pulmonotoxic chemicals are not always clear, and thus, a general in vitro assay for testing pulmonotoxicity applicable to a wide ...
Cell Survival Humans Predictive Value of Tests Artificial Intelligence A549 Cells Bronchi Sensitivity and Specificity High-Throughput Screening Assays Lung Toxicity Tests Xenobiotics Cell Line
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An improved deep learning approach for detection of thyroid papillary cancer in ultrasound images.

Scientific reports Apr 26, 2018
Unlike daily routine images, ultrasound images are usually monochrome and low-resolution. In ultrasound images, the cancer regions are usually blurred, vague margin and irregular in shape. Moreover, the features of cancer region are very similar to n...
Adolescent Child Ultrasonography ROC Curve Image Processing, Computer-Assisted Thyroid Cancer, Papillary Humans Adult Software Sensitivity and Specificity Female Algorithms Male Aged Middle Aged Aged, 80 and over Deep Learning Young Adult
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Detection of white matter lesion regions in MRI using SLIC0 and convolutional neural network.

Computer methods and programs in biomedicine Apr 19, 2018
BACKGROUND AND OBJECTIVE: White matter lesions are non-static brain lesions that have a prevalence rate up to 98% in the elderly population. Because they may be associated with several brain diseases, it is important that they are detected as soon as...
Humans Neural Networks, Computer Algorithms Databases, Factual Image Processing, Computer-Assisted Reproducibility of Results Brain Magnetic Resonance Imaging Sensitivity and Specificity Pattern Recognition, Automated Prevalence False Positive Reactions Diagnosis, Computer-Assisted White Matter Deep Learning
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Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI.

Journal of magnetic resonance imaging : JMRI Apr 16, 2018
BACKGROUND: Deep learning is the most promising methodology for automatic computer-aided diagnosis of prostate cancer (PCa) with multiparametric MRI (mp-MRI).
Deep Learning ROC Curve Predictive Value of Tests Area Under Curve Prostatic Neoplasms Retrospective Studies Reproducibility of Results Sensitivity and Specificity Male Diagnosis, Computer-Assisted Pattern Recognition, Automated Diffusion Magnetic Resonance Imaging Humans Neural Networks, Computer Software Databases, Factual
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Classifying Acute Ischemic Stroke Onset Time using Deep Imaging Features.

AMIA ... Annual Symposium proceedings. AMIA Symposium Apr 16, 2018
Models have been developed to predict stroke outcomes (e.g., mortality) in attempt to provide better guidance for stroke treatment. However, there is little work in developing classification models for the problem of unknown time-since-stroke (TSS), ...
ROC Curve Support Vector Machine Magnetic Resonance Imaging Regression Analysis Area Under Curve Aged Stroke Humans Brain Ischemia Sensitivity and Specificity Machine Learning Male Middle Aged Deep Learning Prognosis Female
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