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10.4103/ijri.IJRI_777_20 http://scihub22266oqcxt.onion/10.4103/ijri.IJRI_777_20 33814766!7996692!33814766     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Indian+J+Radiol+Imaging 2021 ; 31 (Suppl 1): S87-S93 Nephropedia Template TP {{tp|p=33814766|t=2021. Radiographic findings in COVID-19: Comparison between AI and radiologist |pdf=|usr=}}{{33814766}} gab.com Text Radiographic findings in COVID-19: Comparison between AI and radiologist JO: Indian J Radiol Imaging http://www.kidney.de/pget.php?&tw=1&pmid=33814766 #FOAvip CONTEXT: As the burden of COVID-19 enhances, the need of a fast and reliable screening method is imperative. Chest radiographs plays a pivotal role in rapidly triaging the patients. Unfortunately, in low-resource settings, there is a scarcity of trained radiologists. AIM: This study evaluates and compares the performance of an artificial intelligence (AI) system with a radiologist in detecting chest radiograph findings due to COVID-19. SUBJECTS AND METHODS: The test set consisted of 457 CXR images of patients with suspected COVID-19 pneumonia over a period of three months. The radiographs were evaluated by a radiologist with experience of more than 13 years and by the AI system (NeuraCovid, a web application that pairs with the AI model COVID-NET). Performance of AI system and the radiologist were compared by calculating the sensitivity, specificity and generating a receiver operating characteristic curve. RT-PCR test results were used as the gold standard. RESULTS: The radiologist obtained a sensitivity and specificity of 44.1% and 92.5%, respectively, whereas the AI had a sensitivity and specificity of 41.6% and 60%, respectively. The area under curve for correctly classifying CXR images as COVID-19 pneumonia was 0.48 for the AI system and 0.68 for the radiologist. The radiologist's prediction was found to be superior to that of the AI with a P VALUE of 0.005. CONCLUSION: The specificity and sensitivity of detecting lung involvement in COVID-19, by the radiologist, was found to be superior to that by the AI system. Twit Text FOAVip Radiographic findings in COVID-19: Comparison between AI and radiologist ????Indian J Radiol Imaging http://www.kidney.de/pget.php?&tw=1&pmid=33814766 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33814766 #FOAvip English Wikipedia <ref>{{Cite pmid|33814766}}</ref> Radiographic findings in COVID-19: Comparison between AI and radiologist #MMPMID33814766 Sukhija A; Mahajan M; Joshi PC; Dsouza J; Seth NDN; Patil KH Indian J Radiol Imaging 2021[Jan]; 31 (Suppl 1): S87-S93 PMID33814766show ga CONTEXT: As the burden of COVID-19 enhances, the need of a fast and reliable screening method is imperative. Chest radiographs plays a pivotal role in rapidly triaging the patients. Unfortunately, in low-resource settings, there is a scarcity of trained radiologists. AIM: This study evaluates and compares the performance of an artificial intelligence (AI) system with a radiologist in detecting chest radiograph findings due to COVID-19. SUBJECTS AND METHODS: The test set consisted of 457 CXR images of patients with suspected COVID-19 pneumonia over a period of three months. The radiographs were evaluated by a radiologist with experience of more than 13 years and by the AI system (NeuraCovid, a web application that pairs with the AI model COVID-NET). Performance of AI system and the radiologist were compared by calculating the sensitivity, specificity and generating a receiver operating characteristic curve. RT-PCR test results were used as the gold standard. RESULTS: The radiologist obtained a sensitivity and specificity of 44.1% and 92.5%, respectively, whereas the AI had a sensitivity and specificity of 41.6% and 60%, respectively. The area under curve for correctly classifying CXR images as COVID-19 pneumonia was 0.48 for the AI system and 0.68 for the radiologist. The radiologist's prediction was found to be superior to that of the AI with a P VALUE of 0.005. CONCLUSION: The specificity and sensitivity of detecting lung involvement in COVID-19, by the radiologist, was found to be superior to that by the AI system. äRadiographic findings in COVID-19: Comparison between AI and radiologi(epmc).pdf DeepDyve Pubget Overpricing