AIMC Topic: Sensitivity and Specificity

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A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms.

Physiological measurement Oct 24, 2018
OBJECTIVE: Electrocardiography is the most common tool to diagnose cardiovascular diseases. Annotation, segmentation and rhythm classification of ECGs are challenging tasks, especially in the presence of atrial fibrillation and other arrhythmias. Our...
Electrocardiography Supervised Machine Learning Atrial Fibrillation Heart Rate Artifacts Humans Neural Networks, Computer Sensitivity and Specificity Diagnosis, Computer-Assisted
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Analysis of tuberculosis disease through Raman spectroscopy and machine learning.

Photodiagnosis and photodynamic therapy Oct 22, 2018
We present the effectiveness of Raman spectroscopy (RS) in combination with machine learning for screening and analysis of blood sera collected from tuberculosis patients. Blood samples of 60 patients have confirmed active pulmonary tuberculosis and ...
Middle Aged Adolescent Child Humans Image Processing, Computer-Assisted Support Vector Machine Young Adult Female Spectrum Analysis, Raman Adult Principal Component Analysis Sensitivity and Specificity Tuberculosis Male
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Deep neural network improves fracture detection by clinicians.

Proceedings of the National Academy of Sciences of the United States of America Oct 22, 2018
Suspected fractures are among the most common reasons for patients to visit emergency departments (EDs), and X-ray imaging is the primary diagnostic tool used by clinicians to assess patients for fractures. Missing a fracture in a radiograph often ha...
Hospital Rapid Response Team Radiography Wrist Image Interpretation, Computer-Assisted Neural Networks, Computer Emergency Medicine Sensitivity and Specificity Diagnostic Errors Deep Learning Fractures, Bone Humans
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Automated detection of cancer cells in effusion specimens by DNA karyometry.

Cancer cytopathology Oct 19, 2018
BACKGROUND: The average sensitivity of conventional cytology for the identification of cancer cells in effusion specimens is only approximately 58%. DNA image cytometry (DNA-ICM), which exploits the DNA content of morphologically suspicious nuclei me...
Diagnosis, Computer-Assisted Epithelium Aneuploidy Cell Nucleus DNA, Neoplasm Image Cytometry Humans Karyometry Machine Learning Sensitivity and Specificity Neoplasms
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Deep-RBPPred: Predicting RNA binding proteins in the proteome scale based on deep learning.

Scientific reports Oct 15, 2018
RNA binding protein (RBP) plays an important role in cellular processes. Identifying RBPs by computation and experiment are both essential. Recently, an RBP predictor, RBPPred, is proposed in our group to predict RBPs. However, RBPPred is too slow fo...
Neural Networks, Computer Machine Learning Algorithms Sensitivity and Specificity Deep Learning RNA-Binding Proteins Computational Biology Proteome Databases, Protein Amino Acid Sequence Sequence Analysis, Protein
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Using Deep Learning and Transfer Learning to Accurately Diagnose Early-Onset Glaucoma From Macular Optical Coherence Tomography Images.

American journal of ophthalmology Oct 12, 2018
PURPOSE: We sought to construct and evaluate a deep learning (DL) model to diagnose early glaucoma from spectral-domain optical coherence tomography (OCT) images.
Transfer, Psychology Glaucoma, Open-Angle Gonioscopy Datasets as Topic Humans Nerve Fibers Retinal Ganglion Cells Glaucoma Visual Fields Female Visual Field Tests Reproducibility of Results Deep Learning Aged ROC Curve Adult Area Under Curve Tomography, Optical Coherence Sensitivity and Specificity Intraocular Pressure Male Middle Aged
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An improved support vector machine-based diabetic readmission prediction.

Computer methods and programs in biomedicine Oct 12, 2018
BACKGROUND AND OBJECTIVE: In healthcare systems, the cost of unplanned readmission accounts for a large proportion of total hospital payment. Hospital-specific readmission rate becomes a critical issue around the world. Quantification and early ident...
Decision Support Systems, Clinical Reproducibility of Results Bayes Theorem Humans Logistic Models Patient Discharge Risk Factors Neural Networks, Computer Patient Readmission Support Vector Machine Sensitivity and Specificity Algorithms Decision Trees Male Risk Assessment Female
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3D convolutional neural networks for detection and severity staging of meniscus and PFJ cartilage morphological degenerative changes in osteoarthritis and anterior cruciate ligament subjects.

Journal of magnetic resonance imaging : JMRI Oct 10, 2018
BACKGROUND: Semiquantitative assessment of MRI plays a central role in musculoskeletal research; however, in the clinical setting MRI reports often tend to be subjective and qualitative. Grading schemes utilized in research are not used because they ...
Severity of Illness Index ROC Curve Imaging, Three-Dimensional Magnetic Resonance Imaging False Positive Reactions Adult Sensitivity and Specificity Male Middle Aged Meniscus Osteoarthritis, Knee Cartilage, Articular Anterior Cruciate Ligament Injuries Anterior Cruciate Ligament Anterior Cruciate Ligament Reconstruction Humans Neural Networks, Computer Retrospective Studies Female Reproducibility of Results Aged
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Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning.

European radiology Oct 9, 2018
OBJECTIVE: Accurate detection and segmentation of organs at risks (OARs) in CT image is the key step for efficient planning of radiation therapy for nasopharyngeal carcinoma (NPC) treatment. We develop a fully automated deep-learning-based method (te...
Humans Neural Networks, Computer Tomography, X-Ray Computed Female Nasopharyngeal Carcinoma Organ Sparing Treatments Patient Care Planning Male Middle Aged Aged Deep Learning Young Adult Adolescent Adult Organs at Risk Sensitivity and Specificity
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Detection of Traumatic Pediatric Elbow Joint Effusion Using a Deep Convolutional Neural Network.

AJR. American journal of roentgenology Oct 9, 2018
OBJECTIVE: The purpose of this study is to determine whether a deep convolutional neural network (DCNN) trained on a dataset of limited size can accurately diagnose traumatic pediatric elbow effusion on lateral radiographs.
Child, Preschool Child Radiography Elbow Joint Observer Variation Male Infant Humans Diagnosis, Computer-Assisted Adolescent Neural Networks, Computer Retrospective Studies Female Young Adult Elbow Injuries Sensitivity and Specificity
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