AIMC Topic: Sensitivity and Specificity

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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.
Visual Fields Visual Field Tests Transfer, Psychology Glaucoma, Open-Angle Gonioscopy Area Under Curve Tomography, Optical Coherence Datasets as Topic Nerve Fibers Retinal Ganglion Cells Glaucoma Female Reproducibility of Results Aged Adult Sensitivity and Specificity Intraocular Pressure Male Middle Aged Deep Learning ROC Curve Humans
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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...
Humans Neural Networks, Computer Algorithms Decision Trees Bayes Theorem Male Patient Discharge Risk Assessment Patient Readmission Decision Support Systems, Clinical Logistic Models Support Vector Machine Female Reproducibility of Results Risk Factors Sensitivity and Specificity
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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 ...
Cartilage, Articular Anterior Cruciate Ligament Injuries Anterior Cruciate Ligament Anterior Cruciate Ligament Reconstruction Meniscus ROC Curve Imaging, Three-Dimensional Magnetic Resonance Imaging False Positive Reactions Osteoarthritis, Knee Aged Adult Sensitivity and Specificity Male Middle Aged Severity of Illness Index Humans Neural Networks, Computer Retrospective Studies Female Reproducibility of Results
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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...
Young Adult Adult Sensitivity and Specificity Male Middle Aged Deep Learning Humans Neural Networks, Computer Tomography, X-Ray Computed Female Aged Adolescent Organs at Risk Nasopharyngeal Carcinoma Organ Sparing Treatments Patient Care Planning
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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.
Humans Neural Networks, Computer Retrospective Studies Female Young Adult Sensitivity and Specificity Male Infant Diagnosis, Computer-Assisted Adolescent Child, Preschool Child Radiography Elbow Joint Observer Variation Elbow Injuries
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Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks.

NeuroImage Oct 6, 2018
The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and long...
Magnetic Resonance Imaging Humans Multiple Sclerosis Neural Networks, Computer Spinal Cord Reproducibility of Results Observer Variation Sensitivity and Specificity Pattern Recognition, Automated Image Processing, Computer-Assisted
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Comparison of 2 Natural Language Processing Methods for Identification of Bleeding Among Critically Ill Patients.

JAMA network open Oct 5, 2018
IMPORTANCE: To improve patient safety, health care systems need reliable methods to detect adverse events in large patient populations. Events are often described in clinical notes, rather than structured data, which make them difficult to identify o...
Aged ROC Curve Humans Support Vector Machine Aged, 80 and over Machine Learning Natural Language Processing Sensitivity and Specificity Critical Illness Hemorrhage Male Electronic Health Records Middle Aged Female
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Detection of gastritis by a deep convolutional neural network from double-contrast upper gastrointestinal barium X-ray radiography.

Journal of gastroenterology Oct 3, 2018
BACKGROUND: Deep learning has become a new trend of image recognition tasks in the field of medicine. We developed an automated gastritis detection system using double-contrast upper gastrointestinal barium X-ray radiography.
Humans Middle Aged Deep Learning Radiography Gastritis False Negative Reactions Neural Networks, Computer Barium Retrospective Studies Female Sensitivity and Specificity Male
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Diagnosis of urinary tract infection based on artificial intelligence methods.

Computer methods and programs in biomedicine Oct 2, 2018
BACKGROUND AND OBJECTIVE: Urinary tract infection (UTI) is a common disease affecting the vast majority of people. UTI involves a simple infection caused by urinary tract inflammation as well as a complicated infection that may be caused by an inflam...
Decision Trees Urinary Tract Infections False Positive Reactions Cystitis Adolescent Child ROC Curve Decision Support Systems, Clinical Support Vector Machine Adult Sensitivity and Specificity Male Middle Aged Diagnosis, Computer-Assisted Artificial Intelligence Machine Learning Algorithms Female Aged Young Adult Humans Neural Networks, Computer
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Machine learning based framework to predict cardiac arrests in a paediatric intensive care unit : Prediction of cardiac arrests.

Journal of clinical monitoring and computing Sep 27, 2018
A cardiac arrest is a life-threatening event, often fatal. Whilst clinicians classify some of the cardiac arrests as potentially predictable, the majority are difficult to identify even in a post-incident analysis. Changes in some patients' physiolog...
Blood Pressure Oxygen Humans Signal Processing, Computer-Assisted Machine Learning Heart Rate Infant Critical Care Software Child, Preschool Systole Critical Illness Respiratory Rate Child Infant, Newborn Heart Arrest Intensive Care Units, Pediatric Reproducibility of Results Monitoring, Physiologic Sensitivity and Specificity
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