Quantitative Assessment of Chest CT Patterns in COVID-19 and Bacterial Pneumonia Patients: a Deep Learning Perspective
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SciScore for 10.1101/2020.11.13.20231118: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: This study was conducted in accordance with the tenets of the Declaration of Helsinki and was reviewed and approved by the Institutional Review Board of Yeungnam University Hospital (YUH IRB 2020-05-030).
Consent: The requirement for informed consent was waived due to the retrospective study design.Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
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: We …SciScore for 10.1101/2020.11.13.20231118: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: This study was conducted in accordance with the tenets of the Declaration of Helsinki and was reviewed and approved by the Institutional Review Board of Yeungnam University Hospital (YUH IRB 2020-05-030).
Consent: The requirement for informed consent was waived due to the retrospective study design.Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
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: We detected the following sentences addressing limitations in the study:However, several limitations exist and features alone are often insufficient to distinguish between diseases when subtle radiological differences are observed in the image. Discriminating COVID-19 from bacterial pneumonia is regarded as one of the exemplars of such challenges. Here, our method shows the benefit of using deep learning to obtain more robust representations that are more clinically relevant to key imaging characteristics for COVID-19 diagnosis. We quantitatively show that the constructed histogram better captures the overall statistics of the lesion features. Moreover, the SVM classifier can diagnose diseases or patient’s severity more accurately than the radiomics features alone. Our method has two notable advantages compared to common deep learning algorithms; interpretability and generalized representation. Common deep learning methods are limited in interpretability even though they can visualize the important regions using heatmaps [27], [28]. Our method can explain the reasons of diagnosis by checking the presence of specific patterns represented in the patient’s histogram. We verified the key patterns with mean histograms for each disease and severity group and found the important key diagnostic imaging patterns in accordance with published literature. This indicates that our method is safer and more transparent for medical assistance. Moreover, although the proposed feature learning model was not trained to classify severity among patients, the obtained ...
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: Please consider improving the rainbow (“jet”) colormap(s) used on page 4. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.
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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