AIMC Topic: Sensitivity and Specificity

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Endoscopic three-categorical diagnosis of Helicobacter pylori infection using linked color imaging and deep learning: a single-center prospective study (with video).

Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association May 7, 2020
BACKGROUND: Helicobacter pylori (H. pylori) eradication is required to reduce incidence related to gastric cancer. Recently, it was found that even after the successful eradication of H. pylori, an increased, i.e., moderate, risk of gastric cancer pe...
Endoscopy, Digestive System Middle Aged Diagnosis, Computer-Assisted Deep Learning Predictive Value of Tests Area Under Curve Color Helicobacter Infections Helicobacter pylori Feasibility Studies Image Enhancement Gastric Mucosa Atrophy Humans Prospective Studies Female Aged Sensitivity and Specificity Male
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Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging.

Advances in rheumatology (London, England) May 7, 2020
BACKGROUND: Currently, magnetic resonance imaging (MRI) is used to evaluate active inflammatory sacroiliitis related to axial spondyloarthritis (axSpA). The qualitative and semiquantitative diagnosis performed by expert radiologists and rheumatologis...
Diagnosis, Computer-Assisted Magnetic Resonance Imaging Sacroiliac Joint Sacroiliitis Spondylarthritis Humans Machine Learning Retrospective Studies Sensitivity and Specificity
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Diagnostic accuracy of diabetic retinopathy grading by an artificial intelligence-enabled algorithm compared with a human standard for wide-field true-colour confocal scanning and standard digital retinal images.

The British journal of ophthalmology May 6, 2020
BACKGROUND: Photographic diabetic retinopathy screening requires labour-intensive grading of retinal images by humans. Automated retinal image analysis software (ARIAS) could provide an alternative to human grading. We compare the performance of an A...
Humans Adult Sensitivity and Specificity Cross-Sectional Studies Male Middle Aged Image Processing, Computer-Assisted Diabetic Retinopathy Artificial Intelligence Algorithms Female Reproducibility of Results Aged False Positive Reactions Reference Standards Slit Lamp Microscopy Microscopy, Confocal Predictive Value of Tests Diagnostic Imaging Retina Diagnostic Tests, Routine
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Predicting the Glaucomatous Central 10-Degree Visual Field From Optical Coherence Tomography Using Deep Learning and Tensor Regression.

American journal of ophthalmology May 6, 2020
PURPOSE: To predict the visual field (VF) of glaucoma patients within the central 10° from optical coherence tomography (OCT) measurements using deep learning and tensor regression.
Optic Nerve Diseases Glaucoma, Open-Angle Gonioscopy Slit Lamp Microscopy Axial Length, Eye Adult Sensitivity and Specificity Cross-Sectional Studies Intraocular Pressure Male Middle Aged Deep Learning Visual Acuity Vision Disorders Tomography, Optical Coherence Visual Fields Visual Field Tests Humans Retrospective Studies Female Aged
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A Diagnostic Algorithm using Multi-parametric MRI to Differentiate Benign from Malignant Myometrial Tumors: Machine-Learning Method.

Scientific reports May 4, 2020
This study aimed to develop a diagnostic algorithm for preoperative differentiating uterine sarcoma from leiomyoma through a supervised machine-learning method using multi-parametric MRI. A total of 65 participants with 105 myometrial tumors were inc...
Leiomyoma Female Premenopause Reproducibility of Results Myometrium Adult Sensitivity and Specificity Humans Middle Aged Image Processing, Computer-Assisted Machine Learning Diagnosis, Computer-Assisted Magnetic Resonance Imaging Algorithms Decision Trees Predictive Value of Tests Uterine Neoplasms Magnetic Resonance Spectroscopy
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Applying machine learning methods to develop a successful aging maintenance prediction model based on physical fitness tests.

Geriatrics & gerontology international May 1, 2020
AIM: The purpose of this study was to develop a machine learning prediction model for successful aging (SA) based on physical fitness tests.
Aged Aged, 80 and over Sensitivity and Specificity Male Aging Middle Aged Humans Machine Learning Female Decision Trees Predictive Value of Tests Geriatric Assessment Physical Fitness Independent Living Healthy Aging Exercise China Surveys and Questionnaires ROC Curve Logistic Models Follow-Up Studies
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Computer-Aided Pathologic Diagnosis of Nasopharyngeal Carcinoma Based on Deep Learning.

The American journal of pathology Apr 29, 2020
The pathologic diagnosis of nasopharyngeal carcinoma (NPC) by different pathologists is often inefficient and inconsistent. We have therefore introduced a deep learning algorithm into this process and compared the performance of the model with that o...
Humans Diagnosis, Computer-Assisted Neural Networks, Computer Databases, Factual Deep Learning Reproducibility of Results Nasopharyngeal Carcinoma Sensitivity and Specificity Nasopharyngeal Neoplasms
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Convolutional Neural Networks in Predicting Nodal and Distant Metastatic Potential of Newly Diagnosed Non-Small Cell Lung Cancer on FDG PET Images.

AJR. American journal of roentgenology Apr 29, 2020
The purpose of this study was to assess, by analyzing features of the primary tumor with F-FDG PET, the utility of deep machine learning with a convolutional neural network (CNN) in predicting the potential of newly diagnosed non-small cell lung can...
Retrospective Studies Male Female Humans Neural Networks, Computer Predictive Value of Tests Middle Aged Aged Imaging, Three-Dimensional Aged, 80 and over Lung Neoplasms Positron Emission Tomography Computed Tomography Adult Carcinoma, Non-Small-Cell Lung Radiopharmaceuticals Sensitivity and Specificity Lymphatic Metastasis Neoplasm Metastasis Fluorodeoxyglucose F18
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Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT.

Radiology Apr 27, 2020
Background Coronavirus disease 2019 (COVID-19) and pneumonia of other diseases share similar CT characteristics, which contributes to the challenges in differentiating them with high accuracy. Purpose To establish and evaluate an artificial intellige...
Betacoronavirus Pneumonia, Viral Pneumonia Radiologists Coronavirus Infections Humans Radiography, Thoracic Artificial Intelligence Philadelphia Adult Pandemics Rhode Island Sensitivity and Specificity COVID-19 Female China Male Retrospective Studies Adolescent Infant, Newborn Middle Aged Tomography, X-Ray Computed Child, Preschool SARS-CoV-2 Infant Child Aged Lung Diagnosis, Differential Aged, 80 and over Young Adult
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Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks.

European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology Apr 27, 2020
Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more tr...
Humans Neural Networks, Computer Pandemics COVID-19 Tomography, X-Ray Computed SARS-CoV-2 Sensitivity and Specificity Lung Deep Learning Reverse Transcriptase Polymerase Chain Reaction Coronavirus Infections Betacoronavirus COVID-19 Testing Pneumonia, Viral Image Interpretation, Computer-Assisted False Negative Reactions False Positive Reactions Clinical Laboratory Techniques
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