AIMC Topic: Sensitivity and Specificity

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Novel Breast Imaging and Machine Learning: Predicting Breast Lesion Malignancy at Cone-Beam CT Using Machine Learning Techniques.

AJR. American journal of roentgenology May 24, 2018
OBJECTIVE: The purpose of this study is to evaluate the diagnostic performance of machine learning techniques for malignancy prediction at breast cone-beam CT (CBCT) and to compare them to human readers.
Middle Aged Cone-Beam Computed Tomography Contrast Media Predictive Value of Tests Radiographic Image Interpretation, Computer-Assisted Iohexol Prospective Studies Female Sensitivity and Specificity Breast Neoplasms Humans Machine Learning
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Analysis of hepatitis B virus infection in blood sera using Raman spectroscopy and machine learning.

Photodiagnosis and photodynamic therapy May 19, 2018
This study presents the analysis of hepatitis B virus (HBV) infection in human blood serum using Raman spectroscopy combined with pattern recognition technique. In total, 119 confirmed samples of HBV infected sera, collected from Pakistan Atomic Ener...
Humans Algorithms Support Vector Machine Sensitivity and Specificity Pattern Recognition, Automated Diagnostic Errors Serum Hepatitis B Image Processing, Computer-Assisted Spectrum Analysis, Raman
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Machine Learning for Outcome Prediction in Electroencephalograph (EEG)-Monitored Children in the Intensive Care Unit.

Journal of child neurology May 13, 2018
The aim of this study was to evaluate the performance of models predicting in-hospital mortality in critically ill children undergoing continuous electroencephalography (cEEG) in the intensive care unit (ICU). We evaluated the performance of machine ...
Critical Care Neurophysiological Monitoring Diagnosis, Computer-Assisted Adolescent Child, Preschool Child Hospital Mortality Area Under Curve Electroencephalography Epilepsy Young Adult Seizures Humans Sensitivity and Specificity Male Machine Learning Infant Prognosis Female Infant, Newborn
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A Deep Learning Approach for Targeted Contrast-Enhanced Ultrasound Based Prostate Cancer Detection.

IEEE/ACM transactions on computational biology and bioinformatics May 11, 2018
The important role of angiogenesis in cancer development has driven many researchers to investigate the prospects of noninvasive cancer diagnosis based on the technology of contrast-enhanced ultrasound (CEUS) imaging. This paper presents a deep learn...
Ultrasonography Image Processing, Computer-Assisted Humans Reproducibility of Results Computational Biology Area Under Curve Neural Networks, Computer Sensitivity and Specificity Contrast Media Prostatic Neoplasms Algorithms Male Models, Statistical Video Recording Deep Learning Neoplasm Transplantation
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Accuracy of ultra-wide-field fundus ophthalmoscopy-assisted deep learning, a machine-learning technology, for detecting age-related macular degeneration.

International ophthalmology May 9, 2018
PURPOSE: To predict exudative age-related macular degeneration (AMD), we combined a deep convolutional neural network (DCNN), a machine-learning algorithm, with Optos, an ultra-wide-field fundus imaging system.
Middle Aged Diagnosis, Computer-Assisted Deep Learning Ophthalmoscopy Wet Macular Degeneration Humans Aged Neural Networks, Computer Aged, 80 and over Algorithms Sensitivity and Specificity Female Male
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Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram.

Neural networks : the official journal of the International Neural Network Society May 7, 2018
Seizure prediction has attracted growing attention as one of the most challenging predictive data analysis efforts to improve the life of patients with drug-resistant epilepsy and tonic seizures. Many outstanding studies have reported great results i...
Seizures Fourier Analysis Humans Neural Networks, Computer Sensitivity and Specificity Child Electroencephalography
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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...
Sensitivity and Specificity Support Vector Machine Humans Linear Models Spectroscopy, Fourier Transform Infrared Glycation End Products, Advanced Reproducibility of Results Least-Squares Analysis 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...
Aged Aged, 80 and over Comorbidity Humans Young Adult Retrospective Studies Male Machine Learning Adolescent Adult Electronic Health Records Middle Aged Child, Preschool Algorithms Sensitivity and Specificity Female Infant Child Autism Spectrum Disorder Infant, Newborn Prevalence Chronic Disease
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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...
Aged Sensitivity and Specificity Biomechanical Phenomena Male Middle Aged Humans Female Video Recording Image Interpretation, Computer-Assisted Antiparkinson Agents Levodopa Dyskinesia, Drug-Induced Severity of Illness Index Deep Learning Parkinson Disease Patient Reported Outcome Measures
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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 Spatial Memory Neural Networks, Computer Male Carotid Artery, Common Taxis Response Machine Learning Mice Maze Learning Sensitivity and Specificity Mice, Inbred C57BL Carotid Stenosis Animals Reaction Time
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