AIMC Topic: Sensitivity and Specificity

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Present Limitations of Artificial Intelligence in the Emergency Setting - Performance Study of a Commercial, Computer-Aided Detection Algorithm for Pulmonary Embolism.

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin Aug 5, 2021
PURPOSE:  Since artificial intelligence is transitioning from an experimental stage to clinical implementation, the aim of our study was to evaluate the performance of a commercial, computer-aided detection algorithm of computed tomography pulmonary ...
Radiographic Image Interpretation, Computer-Assisted Pulmonary Embolism Retrospective Studies Female Sensitivity and Specificity Male Humans Artificial Intelligence Algorithms Computers
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Computer-aided diagnosis with a convolutional neural network algorithm for automated detection of urinary tract stones on plain X-ray.

BMC urology Aug 5, 2021
BACKGROUND: Recent increased use of medical images induces further burden of their interpretation for physicians. A plain X-ray is a low-cost examination that has low-dose radiation exposure and high availability, although diagnosing urolithiasis usi...
Humans Deep Learning Adolescent Radiography Radiographic Image Interpretation, Computer-Assisted Datasets as Topic Adult Neural Networks, Computer Sensitivity and Specificity Female Urinary Calculi Male Aged Aged, 80 and over Middle Aged Diagnosis, Computer-Assisted
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Deep graph neural network-based prediction of acute suicidal ideation in young adults.

Scientific reports Aug 4, 2021
Precise remote evaluation of both suicide risk and psychiatric disorders is critical for suicide prevention as well as for psychiatric well-being. Using questionnaires is an alternative to labor-intensive diagnostic interviews in a large general popu...
Neural Networks, Computer Mental Disorders Prognosis Republic of Korea Female Humans Anxiety Suicide, Attempted Psychiatric Status Rating Scales Depression Resilience, Psychological Risk Factors Young Adult Adult Sensitivity and Specificity Male Self Concept Surveys and Questionnaires Adolescent Suicidal Ideation Area Under Curve
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A Novel Deep Learning System for Diagnosing Early Esophageal Squamous Cell Carcinoma: A Multicenter Diagnostic Study.

Clinical and translational gastroenterology Aug 4, 2021
INTRODUCTION: This study aims to construct a real-time deep convolutional neural networks (DCNNs) system to diagnose early esophageal squamous cell carcinoma (ESCC) with white light imaging endoscopy.
Esophageal Neoplasms Early Diagnosis Esophageal Squamous Cell Carcinoma Esophagoscopy Humans Sensitivity and Specificity Diagnosis, Computer-Assisted Deep Learning
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Deep Learning to Determine the Activity of Pulmonary Tuberculosis on Chest Radiographs.

Radiology Aug 3, 2021
Background Determining the activity of pulmonary tuberculosis on chest radiographs is difficult. Purpose To develop a deep learning model to identify active pulmonary tuberculosis on chest radiographs. Materials and Methods Chest radiographs were ret...
Radiography, Thoracic Lung Deep Learning Radiographic Image Interpretation, Computer-Assisted Tuberculosis, Pulmonary Humans Retrospective Studies Female Sensitivity and Specificity Male Middle Aged
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Simultaneous Recognition of Atrophic Gastritis and Intestinal Metaplasia on White Light Endoscopic Images Based on Convolutional Neural Networks: A Multicenter Study.

Clinical and translational gastroenterology Aug 3, 2021
INTRODUCTION: Patients with atrophic gastritis (AG) or gastric intestinal metaplasia (GIM) have elevated risk of gastric adenocarcinoma. Endoscopic screening and surveillance have been implemented in high incidence countries. The study aimed to evalu...
Humans Neural Networks, Computer Female Risk Factors Adenocarcinoma Endoscopy, Gastrointestinal Precancerous Conditions Gastritis, Atrophic Metaplasia Stomach Neoplasms Sensitivity and Specificity Male Middle Aged Intestines
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Differentiation of Active Corneal Infections from Healed Scars Using Deep Learning.

Ophthalmology Aug 2, 2021
PURPOSE: To develop and evaluate an automated, portable algorithm to differentiate active corneal ulcers from healed scars using only external photographs.
Slit Lamp Microscopy Corneal Ulcer Eye Infections, Fungal Sensitivity and Specificity Humans Deep Learning Algorithms ROC Curve Retrospective Studies Wound Healing Area Under Curve Predictive Value of Tests Cicatrix False Positive Reactions Eye Infections, Bacterial Photography
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Machine learning for detection of stenoses and aneurysms: application in a physiologically realistic virtual patient database.

Biomechanics and modeling in mechanobiology Jul 31, 2021
This study presents an application of machine learning (ML) methods for detecting the presence of stenoses and aneurysms in the human arterial system. Four major forms of arterial disease-carotid artery stenosis (CAS), subclavian artery stenosis (SAS...
Humans Neural Networks, Computer Machine Learning Algorithms Aortic Aneurysm, Abdominal Models, Biological Regional Blood Flow Carotid Stenosis Subclavian Artery Databases, Factual Sensitivity and Specificity Severity of Illness Index User-Computer Interface Peripheral Arterial Disease
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Early detection of COVID-19 in the UK using self-reported symptoms: a large-scale, prospective, epidemiological surveillance study.

The Lancet. Digital health Jul 29, 2021
BACKGROUND: Self-reported symptoms during the COVID-19 pandemic have been used to train artificial intelligence models to identify possible infection foci. To date, these models have only considered the culmination or peak of symptoms, which is not s...
Anosmia Dyspnea United Kingdom Young Adult Early Diagnosis Adult Prospective Studies Epidemiologic Studies Sensitivity and Specificity Adolescent Male Female Mobile Applications Humans Middle Aged SARS-CoV-2 Models, Biological Artificial Intelligence Aged Self Report Pandemics Aged, 80 and over Chest Pain COVID-19
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Machine learning to predict distal caries in mandibular second molars associated with impacted third molars.

Scientific reports Jul 29, 2021
Impacted mandibular third molars (M3M) are associated with the occurrence of distal caries on the adjacent mandibular second molars (DCM2M). In this study, we aimed to develop and validate five machine learning (ML) models designed to predict the occ...
Cross-Sectional Studies Male Molar, Third Mandible Dental Caries Dental Caries Susceptibility Data Accuracy Tooth, Impacted Tooth Cervix Female Risk Factors Young Adult Humans Adult Sensitivity and Specificity Machine Learning Retrospective Studies Clinical Decision-Making
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