AIMC Topic: Sensitivity and Specificity

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Identification of patients with carotid stenosis using natural language processing.

European radiology Feb 26, 2020
PURPOSE: The highly structured nature of medical reports makes them feasible for automated large-scale patient identification. This study aimed to develop a natural language processing (NLP) model to retrospectively retrieve patients with presence an...
Retrospective Studies Sensitivity and Specificity Ultrasonography Natural Language Processing Carotid Stenosis Humans Neural Networks, Computer
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A proof of concept machine learning analysis using multimodal neuroimaging and neurocognitive measures as predictive biomarker in bipolar disorder.

Asian journal of psychiatry Feb 26, 2020
BACKGROUND: Concomitant use of complementary, multimodal imaging measures and neurocognitive measures is reported to have higher accuracy as a biomarker in Alzheimer's dementia. However, such an approach has not been examined to differentiate healthy...
Humans Reproducibility of Results Proof of Concept Study Young Adult Executive Function Sensitivity and Specificity Bipolar Disorder Male Support Vector Machine Psychiatric Status Rating Scales Magnetic Resonance Imaging Neuroimaging Machine Learning Biomarkers Brain Female
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Noninvasive Diagnosis of Nonalcoholic Fatty Liver Disease and Quantification of Liver Fat with Radiofrequency Ultrasound Data Using One-dimensional Convolutional Neural Networks.

Radiology Feb 25, 2020
Background Radiofrequency ultrasound data from the liver contain rich information about liver microstructure and composition. Deep learning might exploit such information to assess nonalcoholic fatty liver disease (NAFLD). Purpose To develop and eval...
Neural Networks, Computer Prospective Studies Radio Waves Female Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging Predictive Value of Tests Sensitivity and Specificity Non-alcoholic Fatty Liver Disease Male Random Allocation Middle Aged Ultrasonography
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Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model.

Scientific reports Feb 24, 2020
We developed a hybrid deep learning model (HDLM) algorithm that quantitatively predicts macular ganglion cell-inner plexiform layer (mGCIPL) thickness from red-free retinal nerve fiber layer photographs (RNFLPs). A total of 789 pairs of RNFLPs and sp...
Humans Glaucoma Algorithms Diagnostic Techniques, Ophthalmological Refractometry Tomography, Optical Coherence Fundus Oculi Male Translational Research, Biomedical Middle Aged Retinal Ganglion Cells Young Adult Case-Control Studies Adult Diagnosis, Computer-Assisted Sensitivity and Specificity Support Vector Machine Intraocular Pressure Regression Analysis Retrospective Studies Optic Nerve Female Deep Learning Aged ROC Curve Aged, 80 and over
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Prediction of perioperative transfusions using an artificial neural network.

PloS one Feb 24, 2020
BACKGROUND: Accurate prediction of operative transfusions is essential for resource allocation and identifying patients at risk of postoperative adverse events. This research examines the efficacy of using artificial neural networks (ANNs) to predict...
Postoperative Hemorrhage Sex Factors Perioperative Care Sensitivity and Specificity Male Age Factors Blood Transfusion Humans Neural Networks, Computer Prognosis Machine Learning Databases, Factual Female Risk Factors Computer Simulation
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A combination of 3-D discrete wavelet transform and 3-D local binary pattern for classification of mild cognitive impairment.

BMC medical informatics and decision making Feb 21, 2020
BACKGROUND: The detection of Alzheimer's Disease (AD) in its formative stages, especially in Mild Cognitive Impairments (MCI), has the potential of helping the clinicians in understanding the condition. The literature review shows that the classifica...
Alzheimer Disease Wavelet Analysis Male Cognitive Dysfunction Support Vector Machine Magnetic Resonance Imaging Humans Machine Learning Algorithms Aged Aged, 80 and over Sensitivity and Specificity Decision Support Techniques Female
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Automatic Recognition of Laryngoscopic Images Using a Deep-Learning Technique.

The Laryngoscope Feb 18, 2020
OBJECTIVES/HYPOTHESIS: To develop a deep-learning-based computer-aided diagnosis system for distinguishing laryngeal neoplasms (benign, precancerous lesions, and cancer) and improve the clinician-based accuracy of diagnostic assessments of laryngosco...
Humans Retrospective Studies Female Deep Learning Image Interpretation, Computer-Assisted Reproducibility of Results Laryngoscopy Adult Laryngeal Neoplasms Sensitivity and Specificity Otolaryngologists Male
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Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques.

BioMed research international Feb 18, 2020
The adaptability of heart to external and internal stimuli is reflected by the heart rate variability (HRV). Reduced HRV can be a predictor of negative cardiovascular outcomes. Based on the nonlinear, nonstationary, and highly complex dynamics of the...
Heart Rate ROC Curve Humans Support Vector Machine Machine Learning Models, Statistical Algorithms Heart Failure Sensitivity and Specificity
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Radiogenomic Models Using Machine Learning Techniques to Predict EGFR Mutations in Non-Small Cell Lung Cancer.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes Feb 17, 2020
BACKGROUND: The purpose of this study was to build radiogenomics models from texture signatures derived from computed tomography (CT) and F-FDG PET-CT (FDG PET-CT) images of non-small cell lung cancer (NSCLC) with and without epidermal growth factor ...
Lung Lung Neoplasms Carcinoma, Non-Small-Cell Lung Predictive Value of Tests Positron Emission Tomography Computed Tomography Reproducibility of Results Aged Sensitivity and Specificity Male ErbB Receptors Imaging Genomics Mutation Radiopharmaceuticals Image Interpretation, Computer-Assisted Fluorodeoxyglucose F18 Machine Learning Retrospective Studies Female Humans
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Deep learning for screening of interstitial lung disease patterns in high-resolution CT images.

Clinical radiology Feb 16, 2020
AIM: To develop a screening tool for the detection of interstitial lung disease (ILD) patterns using a deep-learning method.
Sensitivity and Specificity Deep Learning Radiographic Image Interpretation, Computer-Assisted Lung Diseases, Interstitial Datasets as Topic Humans Tomography, X-Ray Computed
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