AIMC Topic: Sensitivity and Specificity

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Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: The STARD-AI Steering Group.

Nature medicine Jun 1, 2020
Artificial Intelligence Sensitivity and Specificity ROC Curve Predictive Value of Tests Area Under Curve Research Report Guidelines as Topic Diagnostic Techniques and Procedures Humans
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Thyroid Nodule Malignancy Risk Stratification Using a Convolutional Neural Network.

Ultrasound quarterly Jun 1, 2020
This study evaluates the performance of convolutional neural networks (CNNs) in risk stratifying the malignant potential of thyroid nodules alongside traditional methods such as American College of Radiology Thyroid Imaging Reporting and Data System ...
Sensitivity and Specificity Male Middle Aged Risk Assessment Humans Neural Networks, Computer Retrospective Studies Female Thyroid Gland Thyroid Nodule Image Interpretation, Computer-Assisted Biopsy, Fine-Needle
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Performance of deep neural network-based artificial intelligence method in diabetic retinopathy screening: a systematic review and meta-analysis of diagnostic test accuracy.

European journal of endocrinology Jun 1, 2020
OBJECTIVE: Automatic diabetic retinopathy screening system based on neural networks has been used to detect diabetic retinopathy (DR). However, there is no quantitative synthesis of performance of these methods. We aimed to estimate the sensitivity a...
Humans Diabetic Retinopathy Neural Networks, Computer Artificial Intelligence Macular Edema Sensitivity and Specificity Mass Screening Fundus Oculi
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Automated MRI-Based Deep Learning Model for Detection of Alzheimer's Disease Process.

International journal of neural systems Jun 1, 2020
In the context of neuro-pathological disorders, neuroimaging has been widely accepted as a clinical tool for diagnosing patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). The advanced deep learning method, a novel brain imagi...
Alzheimer Disease Datasets as Topic Neuroimaging Aged, 80 and over Sensitivity and Specificity Humans Cognitive Dysfunction Male Female Support Vector Machine Deep Learning Aged Magnetic Resonance Imaging Models, Neurological
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Automated Segmentation of Left Ventricular Myocardium on Cardiac Computed Tomography Using Deep Learning.

Korean journal of radiology Jun 1, 2020
OBJECTIVE: To evaluate the accuracy of a deep learning-based automated segmentation of the left ventricle (LV) myocardium using cardiac CT.
Algorithms Retrospective Studies Tomography, X-Ray Computed Female Humans Deep Learning Coronary Artery Disease Heart Ventricles Automation Aged Sensitivity and Specificity Male Middle Aged
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Incorporating natural language processing to improve classification of axial spondyloarthritis using electronic health records.

Rheumatology (Oxford, England) May 1, 2020
OBJECTIVES: To develop classification algorithms that accurately identify axial SpA (axSpA) patients in electronic health records, and compare the performance of algorithms incorporating free-text data against approaches using only International Clas...
International Classification of Diseases Algorithms Spondylitis, Ankylosing Natural Language Processing Electronic Health Records Spondylarthritis Area Under Curve Female Quality Improvement Humans Aged Sensitivity and Specificity Male Middle Aged Cohort Studies
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Diagnosing Glaucoma With Spectral-Domain Optical Coherence Tomography Using Deep Learning Classifier.

Journal of glaucoma Apr 1, 2020
UNLABELLED: PRéCIS:: A spectral-domain optical coherence tomography (SD-OCT) based deep learning system detected glaucomatous structural change with high sensitivity and specificity. It outperformed the clinical diagnostic parameters in discriminatin...
Middle Aged Visual Fields Sensitivity and Specificity Tomography, Optical Coherence Deep Learning Humans Intraocular Pressure ROC Curve Nerve Fibers Glaucoma Male Area Under Curve Optic Disk Female Retinal Ganglion Cells Aged Adult
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Depression screening using mobile phone usage metadata: a machine learning approach.

Journal of the American Medical Informatics Association : JAMIA Apr 1, 2020
OBJECTIVE: Depression is currently the second most significant contributor to non-fatal disease burdens globally. While it is treatable, depression remains undiagnosed in many cases. As mobile phones have now become an integral part of daily life, th...
Logistic Models Area Under Curve Telemedicine Depressive Disorder Cell Phone Use Severity of Illness Index Depression Surveys and Questionnaires Mobile Applications Algorithms Adult Sensitivity and Specificity Humans Neural Networks, Computer Machine Learning
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Diagnostic accuracy of 3D deep-learning-based fully automated estimation of patient-level minimum fractional flow reserve from coronary computed tomography angiography.

European heart journal. Cardiovascular Imaging Apr 1, 2020
AIMS: Although deep-learning algorithms have been used to compute fractional flow reserve (FFR) from coronary computed tomography angiography (CCTA), no study has achieved 'fully automated' (i.e. free from human input) FFR calculation using deep-lear...
Humans Retrospective Studies Tomography, X-Ray Computed Sensitivity and Specificity Computed Tomography Angiography Deep Learning Predictive Value of Tests Coronary Stenosis Coronary Angiography Fractional Flow Reserve, Myocardial
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A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas.

Neuro-oncology Mar 5, 2020
BACKGROUND: Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. Currently, reliable IDH mutation determination requires invasive surgical procedures. The purpose of this study was to develop a high...
Glioma Magnetic Resonance Imaging Brain Neoplasms Isocitrate Dehydrogenase Male Humans Middle Aged Female Deep Learning Sensitivity and Specificity
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