AIMC Topic: Sensitivity and Specificity

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Liver vessel segmentation based on extreme learning machine.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) May 4, 2016
Liver-vessel segmentation plays an important role in vessel structure analysis for liver surgical planning. This paper presents a liver-vessel segmentation method based on extreme learning machine (ELM). Firstly, an anisotropic filter is used to remo...
Algorithms Tomography, X-Ray Computed Reproducibility of Results Sensitivity and Specificity Humans Machine Learning Radiotherapy Planning, Computer-Assisted False Positive Reactions Radiography, Abdominal Anisotropy Imaging, Three-Dimensional Image Processing, Computer-Assisted Liver Diffusion
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Deep Learning Representation from Electroencephalography of Early-Stage Creutzfeldt-Jakob Disease and Features for Differentiation from Rapidly Progressive Dementia.

International journal of neural systems May 3, 2016
A novel technique of quantitative EEG for differentiating patients with early-stage Creutzfeldt-Jakob disease (CJD) from other forms of rapidly progressive dementia (RPD) is proposed. The discrimination is based on the extraction of suitable features...
Creutzfeldt-Jakob Syndrome Electroencephalography Alzheimer Disease Wavelet Analysis Aged Sensitivity and Specificity Male Middle Aged Disease Progression Support Vector Machine Diagnosis, Differential Humans Brain Retrospective Studies Female
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Cascaded Adaptation Framework for Fast Calibration of Myoelectric Control.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society May 3, 2016
In spite of several decades of intensive research and development, the existing algorithms of myoelectric pattern recognition (MPR) are yet to make significant clinical and commercial impact. This study focuses on the one of the limiting factors of c...
Amputation Stumps Male Pattern Recognition, Automated Algorithms Discriminant Analysis Humans Calibration Electromyography Reproducibility of Results Machine Learning Muscle Contraction Young Adult Games, Experimental Sensitivity and Specificity
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A framework for the automatic detection and characterization of brain malformations: Validation on the corpus callosum.

Medical image analysis May 3, 2016
In this paper, we extend the one-class Support Vector Machine (SVM) and the regularized discriminative direction analysis to the Multiple Kernel (MK) framework, providing an effective analysis pipeline for the detection and characterization of brain ...
Humans Female Reproducibility of Results Support Vector Machine Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Pattern Recognition, Automated Sensitivity and Specificity Male Child, Preschool Child Corpus Callosum
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Network-based classification of ADHD patients using discriminative subnetwork selection and graph kernel PCA.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Apr 23, 2016
BACKGROUND: Attention Deficit Hyperactivity Disorder (ADHD) is one of the most prevalent behavioral disorders in childhood and adolescence. Recently, network-based diagnosis of ADHD has attracted great attentions due to the fact that ADHD disease is ...
Support Vector Machine Sensitivity and Specificity Pattern Recognition, Automated Principal Component Analysis Male Attention Deficit Disorder with Hyperactivity Datasets as Topic Case-Control Studies Humans Child Brain Female
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Metastatic liver tumour segmentation with a neural network-guided 3D deformable model.

Medical & biological engineering & computing Apr 22, 2016
The segmentation of liver tumours in CT images is useful for the diagnosis and treatment of liver cancer. Furthermore, an accurate assessment of tumour volume aids in the diagnosis and evaluation of treatment response. Currently, segmentation is perf...
Models, Biological Databases as Topic Sensitivity and Specificity Colorectal Neoplasms Liver Neoplasms Imaging, Three-Dimensional Neural Networks, Computer Tomography, X-Ray Computed Reproducibility of Results Humans
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A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials.

Medical & biological engineering & computing Apr 22, 2016
Falls are the leading cause of injury-related morbidity and mortality among older adults. Over 90 % of hip and wrist fractures and 60 % of traumatic brain injuries in older adults are due to falls. Another serious consequence of falls among older adu...
Humans Machine Learning Algorithms Young Adult Adult Activities of Daily Living Accelerometry Sensitivity and Specificity Accidental Falls Signal Processing, Computer-Assisted
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Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks.

Medical image analysis Apr 21, 2016
The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular events. CAC is clinically quantified in cardiac calcium scoring CT (CSCT), but it has been shown that cardiac CT angiography (CCTA) may also be ...
Sensitivity and Specificity Coronary Artery Disease Calcium Humans Computed Tomography Angiography Neural Networks, Computer Algorithms Reproducibility of Results Risk Factors
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A self-taught artificial agent for multi-physics computational model personalization.

Medical image analysis Apr 21, 2016
Personalization is the process of fitting a model to patient data, a critical step towards application of multi-physics computational models in clinical practice. Designing robust personalization algorithms is often a tedious, time-consuming, model- ...
Reproducibility of Results Sensitivity and Specificity Physics Humans Artificial Intelligence Computer Simulation Precision Medicine
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Folded concave penalized learning in identifying multimodal MRI marker for Parkinson's disease.

Journal of neuroscience methods Apr 19, 2016
BACKGROUND: Brain MRI holds promise to gauge different aspects of Parkinson's disease (PD)-related pathological changes. Its analysis, however, is hindered by the high-dimensional nature of the data.
Linear Models Pattern Recognition, Automated Diffusion Tensor Imaging Algorithms Brain Magnetic Resonance Imaging Sensitivity and Specificity Logistic Models Humans Parkinson Disease Machine Learning Multimodal Imaging Image Interpretation, Computer-Assisted
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