ProgNet: COVID-19 Prognosis Using Recurrent and Convolutional Neural Networks
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Abstract
Prognosis of lung mathology severity after Covid-19 infection using chest X-ray time series
Background:
We have been inspired by methods analysing time series of images in remote sensing for change detection. During the current Covid-19 pandemic, our motivation is to provide an automatic tool to predict severity of lung pathologies due to Covid-19. This can be done by analysing images of the same patient acquired at different dates. Since no analytical model is available, and also no accurate quantification tools can be used due to many unknowns about the pathology, feature-free methods are good candidates to analyse such temporal images.
Objective:
This contribution helps improving performances of medical structures facing the Covid-19 pandemic. The first impact is medical and social since more lives could be saved with a 92% rate of good prognosis. In addition to that, patients in intensive care units (up to 15%) could a posteriori suffer from less sequels due to an early and accurate prognosis of their PP. Moreover, accurate prognosis can lead to a better planning of patient’s transfer between units and hospitals, which is linked to the second claimed economical impact. Indeed, prognosis is linked to lower treatment costs due to an optimized predictive protocol using ragiological prognosis.
Methods:
Using Convolutional Neural Networks (CNN) in combination with Recurrent Neural Networks (RNN). Spatial and temporal features are combines to analyse image time series. A prognosis score is delivered indicating the severity of the pathology. Learning is made on a publicly available database.
Results:
When applied to radiological time-series, promising results are obtained with an accuracy rates higher than 92%. Sensitivity and specificity rates are also very interesting.
Conclusion:
Our method is segmentation-free, which makes it competitive with respect to other assessment methods relying on time-consuming lung segmentation algorithms. When applied on radiographic data, the proposed ProgNet architecture showed promising results with good classification performances, especially for ambiguous cases. Specifically, the reported low false positive rates are interesting for an accurate and personalised care workflow.
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SciScore for 10.1101/2020.05.06.20092874: (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
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
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 …
SciScore for 10.1101/2020.05.06.20092874: (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
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
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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