Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1007/s00264-020-04609-7 http://scihub22266oqcxt.onion/10.1007/s00264-020-04609-7 32462314!7251557!32462314     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+Orthop 2020 ; 44 (8): 1539-1542 Nephropedia Template TP {{tp|p=32462314|t=2020. Deep learning COVID-19 detection bias: accuracy through artificial intelligence |pdf=|usr=}}{{32462314}} gab.com Text Deep learning COVID-19 detection bias: accuracy through artificial intelligence JO: Int Orthop http://www.kidney.de/pget.php?&tw=1&pmid=32462314 #FOAvip BACKGROUND: Detection of COVID-19 cases' accuracy is posing a conundrum for scientists, physicians, and policy-makers. As of April 23, 2020, 2.7 million cases have been confirmed, over 190,000 people are dead, and about 750,000 people are reported recovered. Yet, there is no publicly available data on tests that could be missing infections. Complicating matters and furthering anxiety are specific instances of false-negative tests. METHODS: We developed a deep learning model to improve accuracy of reported cases and to precisely predict the disease from chest X-ray scans. Our model relied on convolutional neural networks (CNNs) to detect structural abnormalities and disease categorization that were keys to uncovering hidden patterns. To do so, a transfer learning approach was deployed to perform detections from the chest anterior-posterior radiographs of patients. We used publicly available datasets to achieve this. RESULTS: Our results offer very high accuracy (96.3%) and loss (0.151 binary cross-entropy) using the public dataset consisting of patients from different countries worldwide. As the confusion matrix indicates, our model is able to accurately identify true negatives (74) and true positives (32); this deep learning model identified three cases of false-positive and one false-negative finding from the healthy patient scans. CONCLUSIONS: Our COVID-19 detection model minimizes manual interaction dependent on radiologists as it automates identification of structural abnormalities in patient's CXRs, and our deep learning model is likely to detect true positives and true negatives and weed out false positive and false negatives with > 96.3% accuracy. Twit Text FOAVip Deep learning COVID-19 detection bias: accuracy through artificial intelligence ????Int Orthop http://www.kidney.de/pget.php?&tw=1&pmid=32462314 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32462314 #FOAvip English Wikipedia <ref>{{Cite pmid|32462314}}</ref> Deep learning COVID-19 detection bias: accuracy through artificial intelligence #MMPMID32462314 Vaid S; Kalantar R; Bhandari M Int Orthop 2020[Aug]; 44 (8): 1539-1542 PMID32462314show ga BACKGROUND: Detection of COVID-19 cases' accuracy is posing a conundrum for scientists, physicians, and policy-makers. As of April 23, 2020, 2.7 million cases have been confirmed, over 190,000 people are dead, and about 750,000 people are reported recovered. Yet, there is no publicly available data on tests that could be missing infections. Complicating matters and furthering anxiety are specific instances of false-negative tests. METHODS: We developed a deep learning model to improve accuracy of reported cases and to precisely predict the disease from chest X-ray scans. Our model relied on convolutional neural networks (CNNs) to detect structural abnormalities and disease categorization that were keys to uncovering hidden patterns. To do so, a transfer learning approach was deployed to perform detections from the chest anterior-posterior radiographs of patients. We used publicly available datasets to achieve this. RESULTS: Our results offer very high accuracy (96.3%) and loss (0.151 binary cross-entropy) using the public dataset consisting of patients from different countries worldwide. As the confusion matrix indicates, our model is able to accurately identify true negatives (74) and true positives (32); this deep learning model identified three cases of false-positive and one false-negative finding from the healthy patient scans. CONCLUSIONS: Our COVID-19 detection model minimizes manual interaction dependent on radiologists as it automates identification of structural abnormalities in patient's CXRs, and our deep learning model is likely to detect true positives and true negatives and weed out false positive and false negatives with > 96.3% accuracy. |*Betacoronavirus[MESH] |*Coronavirus Infections[MESH] |*Deep Learning[MESH] |*Pandemics[MESH] |*Pneumonia, Viral[MESH] |Adolescent[MESH] |Adult[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |Bias[MESH] |COVID-19[MESH] |Child[MESH] |Female[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH] |Neural Networks, Computer[MESH] |SARS-CoV-2[MESH]Deep learning COVID-19 detection bias: accuracy through artificial int(epmc).pdf DeepDyve Pubget Overpricing