AIDCOV: An Interpretable Artificial Intelligence Model for Detection of COVID-19 from Chest Radiography Images
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Abstract
As the Coronavirus Disease 2019 (COVID-19) pandemic continues to grow globally, testing to detect COVID-19 and isolating individuals who test positive remains the primary strategy for preventing community spread of the disease. Therefore, automatic and accurate detection of COVID-19 using medical imaging modalities, which are more widely available and accessible, can be beneficial as an alternative diagnostic tool. In this study, an Artificial Intelligence model for Detection of COVID-19 (AIDCOV) is developed to classify chest radiography images as belonging to a person with either COVID-19, other infections, or no pneumonia (i.e., normal). The hierarchical structure in AIDCOV captures the dependencies among features and improves model performance while an attention mechanism makes the model interpretable and transparent. We used several publicly available datasets of both computed tomography (CT) and X-ray modalities. The main public dataset for chest X-ray images contains 475 COVID-19 samples, 3949 samples from other viral/bacterial infections, and 1583 normal samples. Our model achieves a mean cross-validation accuracy of 98.4%. AIDCOV has a sensitivity of 99.8%, a specificity of 100%, and an F1-score of 99.8% in detecting COVID-19 from X-ray images on that dataset. Using a large dataset of CT images, our model obtained mean cross-validation accuracy and sensitivity of 98.8% and 99.4%, respectively. Additionally, our interpretable model can distinguish subtle signs of infection within each radiography image. Assuming these results hold up in larger datasets obtained from a variety of patients over the world, AIDCOV can be used in conjunction with or instead of RT-PCR testing (where RT-PCR testing is unavailable) to detect and isolate individuals with COVID-19, prevent onward transmission to the general population and healthcare workers, and highlight the areas in the lungs that show signs of COVID-related damage.
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SciScore for 10.1101/2020.05.24.20111922: (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: We detected the following sentences addressing limitations in the study:Our study has a number of limitations and, therefore, our results should be interpreted with caution. First, as was the case with the other related studies, our dataset was limited in size and had only 269 cases with COVID-19. Further validation on datasets with a larger number of chest radiography images from patients with COVID-19 would be valuable. Second, chest Xray images may not show signs of SARS-CoV-2 infections in the early …
SciScore for 10.1101/2020.05.24.20111922: (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: We detected the following sentences addressing limitations in the study:Our study has a number of limitations and, therefore, our results should be interpreted with caution. First, as was the case with the other related studies, our dataset was limited in size and had only 269 cases with COVID-19. Further validation on datasets with a larger number of chest radiography images from patients with COVID-19 would be valuable. Second, chest Xray images may not show signs of SARS-CoV-2 infections in the early stages of illness. Abnormalities are more likely to develop over the course of the disease (Wang et al. 2020b; Simpson et al. 2020). However, some preliminary data suggest that abnormalities may show in CT images in the presymptomatic stage and prior to the detection of viral RNA from upper respiratory specimens (Sutton et al. 2020; Zhao et al. 2020). Our dataset does not include information on time since symptom onset or the disease stage at the time the image was taken; thus, we could not assess our model’s accuracy based on these factors. In conclusion, AIDCOV demonstrated high sensitivity, specificity, and positive predictive value in detecting COVID-19 from chest Xray and CT images. Given that radiography is widely available in many countries around the world, AIDCOV can be used in conjunction with or instead of RT-PCR testing (e.g., where RT-PCR testing is unavailable) to find individuals infected with the SARS-CoV-2 virus, isolate them, and prevent the spread of 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.
- 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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