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10.4103/ijri.IJRI_914_20 http://scihub22266oqcxt.onion/10.4103/ijri.IJRI_914_20 33814762!7996677!33814762     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33814762&cmd=llinks): Failed to open stream: HTTP request failed! 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Comparing a deep learning model s diagnostic performance to that of radiologists to detect Covid -19 features on chest radiographs |pdf=|usr=}}{{33814762}} gab.com Text Comparing a deep learning model s diagnostic performance to that of radiologists to detect Covid -19 features on chest radiographs JO: Indian J Radiol Imaging http://www.kidney.de/pget.php?&tw=1&pmid=33814762 #FOAvip BACKGROUND: Whether the sensitivity of Deep Learning (DL) models to screen chest radiographs (CXR) for CoVID-19 can approximate that of radiologists, so that they can be adopted and used if real-time review of CXRs by radiologists is not possible, has not been explored before. OBJECTIVE: To evaluate the diagnostic performance of a doctor-trained DL model (Svita_DL8) to screen for COVID-19 on CXR, and to compare the performance of the DL model with that of expert radiologists. MATERIALS AND METHODS: We used a pre-trained convolutional neural network to develop a publicly available online DL model to evaluate CXR examinations saved in .jpeg or .png format. The initial model was subsequently curated and trained by an internist and a radiologist using 1062 chest radiographs to classify a submitted CXR as either normal, COVID-19, or a non-COVID-19 abnormal. For validation, we collected a separate set of 430 CXR examinations from numerous publicly available datasets from 10 different countries, case presentations, and two hospital repositories. These examinations were assessed for COVID-19 by the DL model and by two independent radiologists. Diagnostic performance was compared between the model and the radiologists and the correlation coefficient calculated. RESULTS: For detecting COVID-19 on CXR, our DL model demonstrated sensitivity of 91.5%, specificity of 55.3%, PPV 60.9%, NPV 77.9%, accuracy 70.1%, and AUC 0.73 (95% CI: 0.86, 0.95). There was a significant correlation (r = 0.617, P = 0.000) between the results of the DL model and the radiologists' interpretations. The sensitivity of the radiologists is 96% and their overall diagnostic accuracy is 90% in this study. CONCLUSIONS: The DL model demonstrated high sensitivity for detecting COVID-19 on CXR. CLINICAL IMPACT: The doctor trained DL tool Svita_DL8 can be used in resource-constrained settings to quickly triage patients with suspected COVID-19 for further in-depth review and testing. Twit Text FOAVip Comparing a deep learning model s diagnostic performance to that of radiologists to detect Covid -19 features on chest radiographs ????Indian J Radiol Imaging http://www.kidney.de/pget.php?&tw=1&pmid=33814762 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33814762 #FOAvip English Wikipedia <ref>{{Cite pmid|33814762}}</ref> Comparing a deep learning model s diagnostic performance to that of radiologists to detect Covid -19 features on chest radiographs #MMPMID33814762 Krishnamoorthy S; Ramakrishnan S; Colaco LB; Dias A; Gopi IK; Gowda GAG; Aishwarya KC; Ramanan V; Chandran M Indian J Radiol Imaging 2021[Jan]; 31 (Suppl 1): S53-S60 PMID33814762show ga BACKGROUND: Whether the sensitivity of Deep Learning (DL) models to screen chest radiographs (CXR) for CoVID-19 can approximate that of radiologists, so that they can be adopted and used if real-time review of CXRs by radiologists is not possible, has not been explored before. OBJECTIVE: To evaluate the diagnostic performance of a doctor-trained DL model (Svita_DL8) to screen for COVID-19 on CXR, and to compare the performance of the DL model with that of expert radiologists. MATERIALS AND METHODS: We used a pre-trained convolutional neural network to develop a publicly available online DL model to evaluate CXR examinations saved in .jpeg or .png format. The initial model was subsequently curated and trained by an internist and a radiologist using 1062 chest radiographs to classify a submitted CXR as either normal, COVID-19, or a non-COVID-19 abnormal. For validation, we collected a separate set of 430 CXR examinations from numerous publicly available datasets from 10 different countries, case presentations, and two hospital repositories. These examinations were assessed for COVID-19 by the DL model and by two independent radiologists. Diagnostic performance was compared between the model and the radiologists and the correlation coefficient calculated. RESULTS: For detecting COVID-19 on CXR, our DL model demonstrated sensitivity of 91.5%, specificity of 55.3%, PPV 60.9%, NPV 77.9%, accuracy 70.1%, and AUC 0.73 (95% CI: 0.86, 0.95). There was a significant correlation (r = 0.617, P = 0.000) between the results of the DL model and the radiologists' interpretations. The sensitivity of the radiologists is 96% and their overall diagnostic accuracy is 90% in this study. CONCLUSIONS: The DL model demonstrated high sensitivity for detecting COVID-19 on CXR. CLINICAL IMPACT: The doctor trained DL tool Svita_DL8 can be used in resource-constrained settings to quickly triage patients with suspected COVID-19 for further in-depth review and testing. äComparing a deep learning model s diagnostic performance to that of ra(epmc).pdf DeepDyve Pubget Overpricing