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10.2174/1573405617999210112195450 http://scihub22266oqcxt.onion/10.2174/1573405617999210112195450 33438548!ä!33438548 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Curr+Med+Imaging 2021 ; 17 (9): 1094-1102 Nephropedia Template TP {{tp|p=33438548|t=2021. Texture Analysis in the Evaluation of COVID-19 Pneumonia in Chest X-Ray Images: A Proof of Concept Study |pdf=|usr=}}{{33438548}} gab.com Text Texture Analysis in the Evaluation of COVID-19 Pneumonia in Chest X-Ray Images: A Proof of Concept Study JO: Curr Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=33438548 #FOAvip BACKGROUND: One of the most challenging aspects related to Covid-19 is to establish the presence of infection in an early phase of the disease. Texture analysis might be an additional tool for the evaluation of Chest X-ray in patients with clinical suspicion of Covid-19 related pneumonia. OBJECTIVE: To evaluate the diagnostic performance of texture analysis and machine learning models for the diagnosis of Covid-19 interstitial pneumonia in Chest X-ray images. METHODS: Chest X-ray images were accessed from a publicly available repository(https://www.kaggle. com/tawsifurrahman/covid19-radiography-database). Lung areas were manually segmented using a polygonal region of interest covering both lung areas, using MaZda, a freely available software for texture analysis. A total of 308 features per ROI was extracted. One hundred-ten Covid-19 Chest X-ray images were selected for the final analysis. RESULTS: Six models, namely NB, GLM, DL, GBT, ANN, and PLS-DA were selected and ensembled. According to Youden's index, the Covid-19 Ensemble Machine Learning Score showing the highest area under the curve (0.971+/-0.015) was 132.57. Assuming this cut-off the Ensemble model performance was estimated by evaluating both true and false positive/negative, resulting in 91.8% accuracy with 93% sensitivity and 90% specificity. Moving the cut-off value to -100, although the accuracy resulted lower (90.6%), the Ensemble Machine Learning showed 100% sensitivity, with 80% specificity. CONCLUSION: Texture analysis of Chest X-ray images and machine learning algorithms may help in differentiating patients with Covid-19 pneumonia. Despite several limitations, this study can lay the ground for future research works in this field and help to develop more rapid and accurate screening tools for these patients. Twit Text FOAVip Texture Analysis in the Evaluation of COVID-19 Pneumonia in Chest X-Ray Images: A Proof of Concept Study ????Curr Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=33438548 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33438548 #FOAvip English Wikipedia <ref>{{Cite pmid|33438548}}</ref> Texture Analysis in the Evaluation of COVID-19 Pneumonia in Chest X-Ray Images: A Proof of Concept Study #MMPMID33438548 Cavallo AU; Troisi J; Forcina M; Mari PV; Forte V; Sperandio M; Pagano S; Cavallo P; Floris R; Garaci F Curr Med Imaging 2021[]; 17 (9): 1094-1102 PMID33438548show ga BACKGROUND: One of the most challenging aspects related to Covid-19 is to establish the presence of infection in an early phase of the disease. Texture analysis might be an additional tool for the evaluation of Chest X-ray in patients with clinical suspicion of Covid-19 related pneumonia. OBJECTIVE: To evaluate the diagnostic performance of texture analysis and machine learning models for the diagnosis of Covid-19 interstitial pneumonia in Chest X-ray images. METHODS: Chest X-ray images were accessed from a publicly available repository(https://www.kaggle. com/tawsifurrahman/covid19-radiography-database). Lung areas were manually segmented using a polygonal region of interest covering both lung areas, using MaZda, a freely available software for texture analysis. A total of 308 features per ROI was extracted. One hundred-ten Covid-19 Chest X-ray images were selected for the final analysis. RESULTS: Six models, namely NB, GLM, DL, GBT, ANN, and PLS-DA were selected and ensembled. According to Youden's index, the Covid-19 Ensemble Machine Learning Score showing the highest area under the curve (0.971+/-0.015) was 132.57. Assuming this cut-off the Ensemble model performance was estimated by evaluating both true and false positive/negative, resulting in 91.8% accuracy with 93% sensitivity and 90% specificity. Moving the cut-off value to -100, although the accuracy resulted lower (90.6%), the Ensemble Machine Learning showed 100% sensitivity, with 80% specificity. CONCLUSION: Texture analysis of Chest X-ray images and machine learning algorithms may help in differentiating patients with Covid-19 pneumonia. Despite several limitations, this study can lay the ground for future research works in this field and help to develop more rapid and accurate screening tools for these patients. |*COVID-19[MESH] |Humans[MESH] |Proof of Concept Study[MESH] |SARS-CoV-2[MESH] |Tomography, X-Ray Computed[MESH]Texture Analysis in the Evaluation of COVID-19 Pneumonia in Chest X-Ra(epmc).pdf DeepDyve Pubget Overpricing