Dynamic Prediction of SARS-CoV-2 RT-PCR status on Chest Radiographs using Deep Learning Enabled Radiogenomics
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Abstract
Reverse Transcription-Polymerase Chain Reaction (RT-PCR) is the gold standard for diagnosis of SARS-CoV-2 infection, but requires specialized equipment and reagents and suffers from long turnaround times. While valuable, chest imaging currently only detects COVID-19 pneumonia, but if it can predict actual RT-PCR SARS-CoV-2 status is unknown. Radiogenomics may provide an effective and accurate RT-PCR-based surrogate. We describe a deep learning radiogenomics (DLR) model (RadGen) that predicts a patient's RT-PCR SARS-CoV-2 status solely from their frontal chest radiograph (CXR).
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SciScore for 10.1101/2021.01.10.21249370: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources The RadGen architecture, based on SE-ResNeXt-50-32×4d, was pretrained on ImageNet and ChestX-ray14 and 28,430 CXR from PadChest, and Kaggle before fine-tuned using CXR from a multinational cohort of RT-PCR tested patients from Hong Kong, GITHUB, SIRM and BIMCV (6,326 images)3-5. GITHUBsuggested: (GitHub, RRID:SCR_002630)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in …SciScore for 10.1101/2021.01.10.21249370: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources The RadGen architecture, based on SE-ResNeXt-50-32×4d, was pretrained on ImageNet and ChestX-ray14 and 28,430 CXR from PadChest, and Kaggle before fine-tuned using CXR from a multinational cohort of RT-PCR tested patients from Hong Kong, GITHUB, SIRM and BIMCV (6,326 images)3-5. GITHUBsuggested: (GitHub, RRID:SCR_002630)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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