A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
No abstract available
Article activity feed
-
-
SciScore for 10.1101/2020.03.12.20027185: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: Patients: This retrospective study was approved by Huazhong University of Science and Technology ethics committee, patient consent was waived due to the retrospective nature of this study.
Consent: Patients: This retrospective study was approved by Huazhong University of Science and Technology ethics committee, patient consent was waived due to the retrospective nature of this study.Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable Finally, 540 patients (mean age, 42.5 ± 16.1 years; range, 3-81 years, male 226, female 314) were enrolled into this study, including 313 patients (mean age, 50.7 ± 14.7 years; … SciScore for 10.1101/2020.03.12.20027185: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: Patients: This retrospective study was approved by Huazhong University of Science and Technology ethics committee, patient consent was waived due to the retrospective nature of this study.
Consent: Patients: This retrospective study was approved by Huazhong University of Science and Technology ethics committee, patient consent was waived due to the retrospective nature of this study.Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable Finally, 540 patients (mean age, 42.5 ± 16.1 years; range, 3-81 years, male 226, female 314) were enrolled into this study, including 313 patients (mean age, 50.7 ± 14.7 years; range, 8-81 years; male 138, female 175) with clinical diagnosed COVID-19 (COVID-positive group) and 229 patients (mean age, 31.2±10.0 years; range, 3-69 years; male 88, female 141) without COVID-19 (COVID-negative group). Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations of this study: There are still several limitations in this study. First, network design and training may be further improved. For example, the UNet model for lung segmentation did not utilize temporal information and it was trained using imperfect ground-truth masks, which could be improved by using 3D segmentation networks and adopting precise ground-truth annotated by experts. Second, the data used in this study came from a single hospital and cross-center validations were not performed. Third, when diagnosing COVID-19, the algorithm worked in a black-box manner, since the algorithm was based on deep learning and its explainability was still at an early stage. Related work of all limitations mentioned above will be addressed in our further studies.
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.
-
-