Background: deeplearning-based radiological image analysis could facilitate use of chest x-rays as a triaging tool for COVID-19 diagnosis in resource-limited settings. This study sought to determine whether a modifie...
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Background: deeplearning-based radiological image analysis could facilitate use of chest x-rays as a triaging tool for COVID-19 diagnosis in resource-limited settings. This study sought to determine whether a modified commercially available deep learning algorithm (M-qXR) could risk stratify patients with suspected COVID-19 infections. Methods: A dual track clinical validation study was designed to assess the clinical accuracy of M-qXR. The algorithm evaluated all Chest-X-rays (CXRs) performed during the study period for abnormal findings and assigned a COVID-19 risk score. Four independent radiologists served as radiological ground truth. The M-qXR algorithm output was compared against radiological ground truth and summary statistics for prediction accuracy were calculated. In addition, patients who underwent both PCR testing and CXR for suspected COVID-19 infection were included in a co-occurrence matrix to assess the sensitivity and specificity of the M-qXR algorithm. Results: 625 CXRs were included in the clinical validation study. 98% of total interpretations made by M-qXR agreed with ground truth (p = 0.25). M-qXR correctly identified the presence or absence of pulmonary opacities in 94% of CXR interpretations. M-qXR's sensitivity, specificity, PPV, and NPV for detecting pulmonary opacities were 94%, 95%, 99%, and 88% respectively. M-qXR correctly identified the presence or absence of pulmonary consolidation in 88% of CXR interpretations (p = 0.48). M-qXR's sensitivity, specificity, PPV, and NPV for detecting pulmonary consolidation were 91%, 84%, 89%, and 86% respectively. Furthermore, 113 PCR-confirmed COVID-19 cases were used to create a co-occurrence matrix between M-qXR's COVID-19 risk score and COVID-19 PCR test results. The PPV and NPV of a medium to high COVID-19 risk score assigned by M-qXR yielding a positive COVID-19 PCR test result was estimated to be 89.7% and 80.4% respectively. Conclusion: M-qXR was found to have comparable accuracy to radiolog
The main objective of this paper is to design and implement a novel automatic E-Waste Management System using machine learningalgorithms. A Waste Management System (WMS) is entirely manual, and it was extended by an ...
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Now a days challenges for medical community and public health issue are increases in rare diseases and this diseases were neglected for many years. Rare diseases affect a limited number of individuals but the number o...
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ISBN:
(纸本)9781538628430;9781538628423
Now a days challenges for medical community and public health issue are increases in rare diseases and this diseases were neglected for many years. Rare diseases affect a limited number of individuals but the number of disorders that fit this definition is very large. Most rare diseases are genetic disorders, which are often severely disabling, substantially affect life expectancy, and impair physical and mental abilities. These disabilities result in reduced quality of life. In proposed system, symptoms based disease discovery system which discovers diseases on the basis of deep learning algorithm. In our base paper, sparsely deeplearning is used to infer the diseases as per the specified questions/symptoms. In sparse deep learning algorithm the author used only 3 hidden layers for processing. Our algorithm contains 5 hidden layers, therefore as compared to existing system, our system is more accurate. To implement disease inference system using deep learning algorithm. In case of any query system implement automatic question-answer system in which user will ask any question and system will process that question using data mining algorithms to find out proper answer. And also implement personalized disease prevention techniques recommendation system.
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