DICOM images of all frontal CXRs were imported into a commercial AI algorithm (qure.ai, Mumbai, India) and processed by two study coauthors (SE and FH with 1-year post-doctoral research experience in thoracic imaging). The algorithm provides the percentage of projected area with COVID-19 related findings which we deemed as the AI score. The processing time per CXR was < 5 s.

The AI algorithm is a deep learning-based model trained with two sets of data. With the first set of 2.5 million CXRs, the algorithm was trained and validated for detection and distribution of pulmonary opacities along with presence of other radiographic findings such as hilar enlargement, pleural effusions, cavities, nodules, and calcifications. The second set of 600 CXRs (300 CXRs from RT-PCR assay positive COVID-19 positive patients and 300 CXRs without COVID-19 pneumonia) were used to train the algorithm to output COVID-19 prediction scores. None of the two datasets belonged to any of the participating institutions or countries included in our study.

The abnormality detection AI algorithm in our study is composed of two parts. First, the abnormality-specific region of interest (ROI) generator comprises multiple segmentation networks using U-Net architecture10. It creates a mask for different anatomies such as lungs, diaphragm, and mediastinum and then generates a set of ROIs with a specific abnormality. Second, a hybrid convolutional neural network generates outputs of a low-resolution probability map and a prediction score of findings. The predictions from each of the multiple ROIs are pooled with the Log-Sum-Exp function (a convex approximation of the maximum function) to obtain the overall prediction score and pixel map1113. The hybrid network was trained end-to-end using both Natural Language Processing-inferred labels from radiology reports and pixel-level annotations from radiologists where available.

Upon completion of processing, the AI algorithm outputs a secondary capture DICOM with the following components: pixel-level border (the affected lung regions with COVID-19 related findings), percentage of projected area with COVID-19 related findings, the risk of the CXR being from COVID-19 positive patient (COVID-19 risk as high, medium, low and none) and a COVID-19 score for each lung, separately. The total AI score was estimated by adding scores for each lung.

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