AIMC Topic: Radiography, Thoracic

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Accounting for data variability in multi-institutional distributed deep learning for medical imaging.

Journal of the American Medical Informatics Association : JAMIA May 1, 2020
OBJECTIVES: Sharing patient data across institutions to train generalizable deep learning models is challenging due to regulatory and technical hurdles. Distributed learning, where model weights are shared instead of patient data, presents an attract...
Deep Learning Diagnostic Imaging Datasets as Topic Humans Diabetic Retinopathy Computer Communication Networks Pattern Recognition, Automated Radiography, Thoracic Health Information Exchange
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Utilizing Artificial Intelligence to Determine Bone Mineral Density Via Chest Computed Tomography.

Journal of thoracic imaging May 1, 2020
PURPOSE: The purpose of this study was to validate the accuracy of an artificial intelligence (AI) prototype application in determining bone mineral density (BMD) from chest computed tomography (CT), as compared with dual-energy x-ray absorptiometry ...
Humans Artificial Intelligence Bone Density Adult Radiography, Thoracic Radiographic Image Interpretation, Computer-Assisted Retrospective Studies Male Reproducibility of Results Absorptiometry, Photon Lumbar Vertebrae Middle Aged Tomography, X-Ray Computed Aged Female Aged, 80 and over
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Prediction of Pulmonary to Systemic Flow Ratio in Patients With Congenital Heart Disease Using Deep Learning-Based Analysis of Chest Radiographs.

JAMA cardiology Apr 1, 2020
IMPORTANCE: Chest radiography is a useful noninvasive modality to evaluate pulmonary blood flow status in patients with congenital heart disease. However, the predictive value of chest radiography is limited by the subjective and qualitive nature of ...
Male Infant Deep Learning Humans Radiography, Thoracic Retrospective Studies Heart Defects, Congenital Child, Preschool Female Blood Circulation Child Radiographic Image Interpretation, Computer-Assisted Pulmonary Circulation
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[Research and application of orthotopic DR chest radiograph quality control system based on artificial intelligence].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Feb 25, 2020
With the change of medical diagnosis and treatment mode, the quality of medical image directly affects the diagnosis and treatment of the disease for doctors. Therefore, realization of intelligent image quality control by computer will have a greater...
Radiography, Thoracic Humans Artificial Intelligence Algorithms Image Processing, Computer-Assisted Quality Control
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Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches.

Journal of X-ray science and technology Jan 1, 2020
BACKGROUND: The novel coronavirus disease 2019 (COVID-19) constitutes a public health emergency globally. The number of infected people and deaths are proliferating every day, which is putting tremendous pressure on our social and healthcare system. ...
Pneumonia, Viral Coronavirus Infections Radiography, Thoracic Betacoronavirus Deep Learning Pandemics Diagnosis, Differential COVID-19 Pneumonia Reproducibility of Results Algorithms Databases, Factual Humans SARS-CoV-2 Tomography, X-Ray Computed Neural Networks, Computer
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AI Is Bringing USB Back: Implementing a Beta Chest X-ray Neural Network.

Journal of digital imaging Dec 1, 2019
In a day and age of rapid technological growth and advancement in digital technology, quantum computing, and decentralized cloud computing, it is difficult to get excited about USB sticks, those little dongles that store only a few gigabytes and comm...
Radiography, Thoracic Artificial Intelligence Canada Radiology Information Systems Humans Neural Networks, Computer
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Deep Learning Method for Automated Classification of Anteroposterior and Posteroanterior Chest Radiographs.

Journal of digital imaging Dec 1, 2019
Ensuring correct radiograph view labeling is important for machine learning algorithm development and quality control of studies obtained from multiple facilities. The purpose of this study was to develop and test the performance of a deep convolutio...
Humans Databases, Factual Adult Sensitivity and Specificity Retrospective Studies Deep Learning Radiography, Thoracic Reproducibility of Results Child Radiographic Image Interpretation, Computer-Assisted
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Generalizable Inter-Institutional Classification of Abnormal Chest Radiographs Using Efficient Convolutional Neural Networks.

Journal of digital imaging Oct 1, 2019
Our objective is to evaluate the effectiveness of efficient convolutional neural networks (CNNs) for abnormality detection in chest radiographs and investigate the generalizability of our models on data from independent sources. We used the National ...
Radiography, Thoracic Humans Neural Networks, Computer Deep Learning Radiographic Image Interpretation, Computer-Assisted Female Lung Diseases Male Datasets as Topic Middle Aged
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Development of an Artificially Intelligent Mobile Phone Application to Identify Cardiac Devices on Chest Radiography.

JACC. Clinical electrophysiology Sep 1, 2019
Pacemaker, Artificial Radiographic Image Interpretation, Computer-Assisted Radiography, Thoracic Defibrillators, Implantable Humans Artificial Intelligence Mobile Applications
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Assessment of Critical Feeding Tube Malpositions on Radiographs Using Deep Learning.

Journal of digital imaging Aug 1, 2019
Assess the efficacy of deep convolutional neural networks (DCNNs) in detection of critical enteric feeding tube malpositions on radiographs. 5475 de-identified HIPAA compliant frontal view chest and abdominal radiographs were obtained, consisting of ...
Enteral Nutrition Image Processing, Computer-Assisted Humans Radiography, Abdominal Radiography Medical Errors Neural Networks, Computer Radiography, Thoracic Deep Learning
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