Automated Assessment and Tracking of COVID-19 Pulmonary Disease Severity on Chest Radiographs Using Convolutional Siamese Neural Networks
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SciScore for 10.1101/2020.05.20.20108159: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: This Health Insurance Portability and Accountability Act-compliant retrospective study was reviewed and exempted by the Institutional Review Board of Massachusetts General Hospital, with waiver of informed consent.
Consent: This Health Insurance Portability and Accountability Act-compliant retrospective study was reviewed and exempted by the Institutional Review Board of Massachusetts General Hospital, with waiver of informed consent.Randomization The COVID-19 training set contained 314 admission CXRs from unique patients hospitalized at least in part from April 1-10, 2020, randomly partitioned 9:1 for training and validation (282:32 images). Blinding Intubati… SciScore for 10.1101/2020.05.20.20108159: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: This Health Insurance Portability and Accountability Act-compliant retrospective study was reviewed and exempted by the Institutional Review Board of Massachusetts General Hospital, with waiver of informed consent.
Consent: This Health Insurance Portability and Accountability Act-compliant retrospective study was reviewed and exempted by the Institutional Review Board of Massachusetts General Hospital, with waiver of informed consent.Randomization The COVID-19 training set contained 314 admission CXRs from unique patients hospitalized at least in part from April 1-10, 2020, randomly partitioned 9:1 for training and validation (282:32 images). Blinding Intubation and mortality data were collected from the medical record by two investigators blinded to CXR findings. Power Analysis not detected. Sex as a biological variable We empirically set N=12, using all cases labeled with “No Finding” from the CheXpert validation set as the normal pool (ages 19-68 years, 7 women). Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are several limitations to this study. First, the COVID-19 patients in this study were from urban areas of the United States, which may limit the external generalizability of this algorithm to other locations. However, given that the model was able to generalize to a second hospital (community hospital vs quaternary care center) with only a small decrease in performance, we believe the model should be robust and share the code for further research. Second, abnormal patient positioning and respiratory phase may affect the score. Variability resulting from these differences may impact the algorithm performance in evaluating subtle changes between CXRs. However, since the algorithm explicitly learns to assess radiographic disease severity, quality control by a radiologist is relatively simple as the radiologist can compare the PXS score to what is expected on sample studies. Third, our algorithm was trained for use on AP chest radiographs, as AP positioning is more common than posterior-anterior images among patients with COVID-19. This limits the generalizability of the algorithm model for posterior-anterior radiographs. We developed an automated Siamese neural network-based pulmonary disease severity score for patients with COVID-19, with the potential to help with clinical triage and workflow optimization. With further validation, the score could be incorporated into clinical treatment guidelines to be used together with other clinical and lab data. Beyond the COVID-19 ...
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.
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