A Simplified Point-of-Care Lung Ultrasound Protocol to Detect Coronavirus Disease 2019 in Inpatients: A Prospective Observational Study

ObjectivesTo assess the diagnostic performance of lung point-of-care ultrasound (POCUS) compared to either a positive nucleic acid test (NAT) or a COVID-19-typical pattern on computed tomography (CT) and to evaluate opportunities to simplify a POCUS algorithm. MethodsHospital-admitted adult inpatients with (1) either confirmed or suspected COVID-19 and (2) a completed or ordered CT within the preceding 24 hours were recruited. Twelve lung zones were scanned with a handheld POCUS machine. POCUS, CT, and X-ray (CXR) images were reviewed independently by blinded experts. A simplified POCUS algorithm was developed via machine learning. ResultsOf 79 enrolled subjects, 26.6% had a positive NAT and 31.6% had a CT typical for COVID-19. The receiver operator curve (ROC) for a 12-zone POCUS protocol had an area under the curve (AUC) of 0.787 for positive NAT and 0.820 for typical CT. A simplified four-zone protocol had an AUC of 0.862 for typical CT and 0.862 for positive NAT. CT had an AUC of 0.815 for positive NAT; CXR had AUCs of 0.793 for positive NAT and 0.733 for typical CT. Performance of the four-zone protocol was superior to CXR for positive NAT (p=0.0471). Using a two-point cutoff system, the four-zone POCUS protocol had a sensitivity of 0.920 and 0.904 compared to CT and NAT, respectively, at the lower cutoff; it had a specificity of 0.926 and 0.948 at the higher cutoff, respectively. ConclusionPOCUS outperformed CXR to predict positive NAT. POCUS could potentially replace other chest imaging for persons under investigation for COVID-19.

Improvement and Multi-Population Generalizability of a Deep Learning-Based Chest Radiograph Severity Score for COVID-19

PurposeTo improve and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations. Materials and MethodsA published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung disease severity (pulmonary x-ray severity (PXS) score). The model was evaluated on CXRs from four test sets, including 3 from the United States (patients hospitalized at an academic medical center (N=154), patients hospitalized at a community hospital (N=113), and outpatients (N=108)) and 1 from Brazil (patients at an academic medical center emergency department (N=303)). Radiologists from both countries independently assigned reference standard CXR severity scores, which were correlated with the PXS scores as a measure of model performance (Pearson r). The Uniform Manifold Approximation and Projection (UMAP) technique was used to visualize the neural network results. ResultsTuning the deep learning model with outpatient data improved model performance in two United States hospitalized patient datasets (r=0.88 and r=0.90, compared to baseline r=0.86). Model performance was similar, though slightly lower, when tested on the United States outpatient and Brazil emergency department datasets (r=0.86 and r=0.85, respectively). UMAP showed that the model learned disease severity information that generalized across test sets. ConclusionsPerformance of a deep learning-based model that extracts a COVID-19 severity score on CXRs improved using training data from a different patient cohort (outpatient versus hospitalized) and generalized across multiple populations.

Automated assessment of COVID-19 pulmonary disease severity on chest radiographs using convolutional Siamese neural networks

PurposeTo develop an automated measure of COVID-19 pulmonary disease severity on chest radiographs (CXRs), for longitudinal disease evaluation and clinical risk stratification. Materials and MethodsA convolutional Siamese neural network-based algorithm was trained to output a measure of pulmonary disease severity on anterior-posterior CXRs (pulmonary x-ray severity (PXS) score), using weakly-supervised pretraining on ~160,000 images from CheXpert and transfer learning on 314 CXRs from patients with COVID-19. The algorithm was evaluated on internal and external test sets from different hospitals, containing 154 and 113 CXRs respectively. The PXS score was correlated with a radiographic severity score independently assigned by two thoracic radiologists and one in-training radiologist. For 92 internal test set patients with follow-up CXRs, the change in PXS score was compared to radiologist assessments of change. The association between PXS score and subsequent intubation or death was assessed. ResultsThe PXS score correlated with the radiographic pulmonary disease severity score assigned to CXRs in the COVID-19 internal and external test sets ({rho}=0.84 and {rho}=0.78 respectively). The direction of change in PXS score in follow-up CXRs agreed with radiologist assessment ({rho}=0.74). In patients not intubated on the admission CXR, the PXS score predicted subsequent intubation or death within three days of hospital admission (area under the receiver operator characteristic curve=0.80 (95%CI 0.75-0.85)). ConclusionA Siamese neural network-based severity score automatically measures COVID-19 pulmonary disease severity in chest radiographs, which can be scaled and rapidly deployed for clinical triage and workflow optimization. SUMMARYA convolutional Siamese neural network-based algorithm can calculate a continuous radiographic pulmonary disease severity score in COVID-19 patients, which can be used for longitudinal disease evaluation and clinical risk stratification. KEY RESULTSO_LIA Siamese neural network-based severity score correlates with radiologist-annotated pulmonary disease severity on chest radiographs from patients with COVID-19 ({rho}=0.84 and {rho}=0.78 in internal and external test sets respectively). C_LIO_LIThe direction of change in the severity score in follow-up radiographs is concordant with radiologist assessment ({rho}=0.74). C_LIO_LIThe admission chest radiograph severity score can help predict subsequent intubation or death within three days of admission (receiver operator characteristic area under the curve=0.80). C_LI

Artificial intelligence for rapid identification of the coronavirus disease 2019 (COVID-19)

For diagnosis of COVID-19, a SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT-PCR) test is routinely used. However, this test can take up to two days to complete, serial testing may be required to rule out the possibility of false negative results, and there is currently a shortage of RT-PCR test kits, underscoring the urgent need for alternative methods for rapid and accurate diagnosis of COVID-19 patients. Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS-CoV-2 infection. Nevertheless, CT alone may have limited negative predictive value for ruling out SARS-CoV-2 infection, as some patients may have normal radiologic findings at early stages of the disease. In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history, and laboratory testing to rapidly diagnose COVID-19 positive patients. Among a total of 905 patients tested by real-time RT-PCR assay and next-generation sequencing RT-PCR, 419 (46.3%) tested positive for SARSCoV-2. In a test set of 279 patients, the AI system achieved an AUC of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of RT-PCR positive COVID-19 patients who presented with normal CT scans, correctly identifying 17 of 25 (68%) patients, whereas radiologists classified all of these patients as COVID-19 negative. When CT scans and associated clinical history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients.