Automated quantification of COVID-19 severity and progression using chest CT images.

OBJECTIVE: To develop and test computer software to detect, quantify, and monitor progression of pneumonia associated with COVID-19 using chest CT scans. METHODS: One hundred twenty chest CT scans from subjects with lung infiltrates were used for training deep learning algorithms to segment lung regions and vessels. Seventy-two serial scans from 24 COVID-19 subjects were used to develop and test algorithms to detect and quantify the presence and progression of infiltrates associated with COVID-19. The algorithm included (1) automated lung boundary and vessel segmentation, (2) registration of the lung boundary between serial scans, (3) computerized identification of the pneumonitis regions, and (4) assessment of disease progression. Agreement between radiologist manually delineated regions and computer-detected regions was assessed using the Dice coefficient. Serial scans were registered and used to generate a heatmap visualizing the change between scans. Two radiologists, using a five-point Likert scale, subjectively rated heatmap accuracy in representing progression. RESULTS: There was strong agreement between computer detection and the manual delineation of pneumonic regions with a Dice coefficient of 81% (CI 76-86%). In detecting large pneumonia regions (> 200 mm3), the algorithm had a sensitivity of 95% (CI 94-97%) and specificity of 84% (CI 81-86%). Radiologists rated 95% (CI 72 to 99) of heatmaps at least "acceptable" for representing disease progression. CONCLUSION: The preliminary results suggested the feasibility of using computer software to detect and quantify pneumonic regions associated with COVID-19 and to generate heatmaps that can be used to visualize and assess progression. KEY POINTS: • Both computer vision and deep learning technology were used to develop computer software to quantify the presence and progression of pneumonia associated with COVID-19 depicted on CT images. • The computer software was tested using both quantitative experiments and subjective assessment. • The computer software has the potential to assist in the detection of the pneumonic regions, monitor disease progression, and assess treatment efficacy related to COVID-19.

Any unique image biomarkers associated with COVID-19?

OBJECTIVE: To define the uniqueness of chest CT infiltrative features associated with COVID-19 image characteristics as potential diagnostic biomarkers. METHODS: We retrospectively collected chest CT exams including n = 498 on 151 unique patients RT-PCR positive for COVID-19 and n = 497 unique patients with community-acquired pneumonia (CAP). Both COVID-19 and CAP image sets were partitioned into three groups for training, validation, and testing respectively. In an attempt to discriminate COVID-19 from CAP, we developed several classifiers based on three-dimensional (3D) convolutional neural networks (CNNs). We also asked two experienced radiologists to visually interpret the testing set and discriminate COVID-19 from CAP. The classification performance of the computer algorithms and the radiologists was assessed using the receiver operating characteristic (ROC) analysis, and the nonparametric approaches with multiplicity adjustments when necessary. RESULTS: One of the considered models showed non-trivial, but moderate diagnostic ability overall (AUC of 0.70 with 99% CI 0.56-0.85). This model allowed for the identification of 8-50% of CAP patients with only 2% of COVID-19 patients. CONCLUSIONS: Professional or automated interpretation of CT exams has a moderately low ability to distinguish between COVID-19 and CAP cases. However, the automated image analysis is promising for targeted decision-making due to being able to accurately identify a sizable subsect of non-COVID-19 cases. KEY POINTS: • Both human experts and artificial intelligent models were used to classify the CT scans. • ROC analysis and the nonparametric approaches were used to analyze the performance of the radiologists and computer algorithms. • Unique image features or patterns may not exist for reliably distinguishing all COVID-19 from CAP; however, there may be imaging markers that can identify a sizable subset of non-COVID-19 cases.