ROBUST COVID-19-RELATED CONDITION CLASSIFICATION NETWORK
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Abstract
COVID-19 can exponentially precipitate life-threatening emergencies as witnessed during the recent spreading of a novel coronavirus infection which can rapidly evolve into lung collapse and respiratory distress (among other various severe clinical conditions). Our study evaluates the performance of a tailor-designed deep convolutional network on the tasks of early detection and localization of radiological signs associated to COVID-19 on frontal chest X-rays. We also asses the frameworks capacity in differentiating the above-mentioned signs, which are usually confused with the more usual common bacterial and viral pneumonias. Open-source chest X-ray images categorized as Normal, Non-COVID-19 and COVID-19 pneumonias were downloaded from the NIH (n=2,259), RSNA (n=600) and HM Hospitales (n=2,307). Our algorithmic framework was able to precisely detect the images with COVID19- related radiological findings (mean Accuracy: 90.5%; Sensitivity: 80.6%; Specificity: 98.0%), whilst correctly categorizing images deemed as Non-COVID-19 pneumonias (mean Accuracy: 88.4%; Sensitivity: 93.3%; Specificity: 92.0%) and normal chest X- rays (mean Accuracy 92.1%; Sensitivity: 91.8%; Specificity: 94.3%). The associated results show that our AI framework is able to classify COVID-19 accurately, making of it a potential tool to improve the diagnostic performance across primary-care centres and, to grant priority to a subset of algorithmic selected images for urgent follow-on expert review. This would sensibly accelerate diagnosis in remote locations, reduce the bottleneck on specialized centres, and/or help to alleviate the needs on situations of scarcity in the availability of molecular tests.
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SciScore for 10.1101/2020.05.19.20106336: (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: 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:Another limitation is the fact that although the test dataset was disjunct from the training dataset, all the COVID-19 positive images belonged to the same original database (HM Hospitales), raising concerns about the framework ability to generalize on exogenous test sets coming from different image banks. It is known that the efficacy …
SciScore for 10.1101/2020.05.19.20106336: (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: 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:Another limitation is the fact that although the test dataset was disjunct from the training dataset, all the COVID-19 positive images belonged to the same original database (HM Hospitales), raising concerns about the framework ability to generalize on exogenous test sets coming from different image banks. It is known that the efficacy of DNNs varies based on the set of images with which they are trained. Each model may have different sensitivities and specificities and may be subject to a unique set of biases and shortcomings in prediction introduced by the image training set. Finally, not all COVID-19 cases are associated with chest pathology. In fact, approximately half of patients imaged 0-2 days after symptom onset had a normal chest CT 10. We intend to address these limitations in future editions of this work.
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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