Robust prediction of mortality of COVID-19 patients based on quantitative, operator-independent, lung CT densitometry

Artificial intelligence for differentiating COVID-19 from other viral pneumonias on CT: comparative analysis of different models based on quantitative and radiomic approaches

BACKGROUND

To develop a pipeline for automatic extraction of quantitative metrics and radiomic features from lung computed tomography (CT) and develop artificial intelligence (AI) models supporting differential diagnosis between coronavirus disease 2019 (COVID-19) and other viral pneumonia (non-COVID-19).

METHODS

Chest CT of 1,031 patients (811 for model building; 220 as independent validation set (IVS) with positive swab for severe acute respiratory syndrome coronavirus-2 (647 COVID-19) or other respiratory viruses (384 non-COVID-19) were segmented automatically. A Gaussian model, based on the HU histogram distribution describing well-aerated and ill portions, was optimised to calculate quantitative metrics (QM, n = 20) in both lungs (2L) and four geometrical subdivisions (GS) (upper front, lower front, upper dorsal, lower dorsal; n = 80). Radiomic features (RF) of first (RF1, n = 18) and second (RF2, n = 120) order were extracted from 2L using PyRadiomics tool. Extracted metrics were used to develop four multilayer-perceptron classifiers, built with different combinations of QM and RF: Model1 (RF1-2L); Model2 (QM-2L, QM-GS); Model3 (RF1-2L, RF2-2L); Model4 (RF1-2L, QM-2L, GS-2L, RF2-2L).

RESULTS

The classifiers showed accuracy from 0.71 to 0.80 and area under the receiving operating characteristic curve (AUC) from 0.77 to 0.87 in differentiating COVID-19 versus non-COVID-19 pneumonia. Best results were associated with Model3 (AUC 0.867 ± 0.008) and Model4 (AUC 0.870 ± 0.011. For the IVS, the AUC values were 0.834 ± 0.008 for Model3 and 0.828 ± 0.011 for Model4.

CONCLUSIONS

Four AI-based models for classifying patients as COVID-19 or non-COVID-19 viral pneumonia showed good diagnostic performances that could support clinical decisions.

Residual lung damage following ARDS in COVID-19 ICU survivors

BACKGROUND

Coronavirus disease 2019 acute respiratory distress syndrome (COVID-19 ARDS) is a disease that often requires invasive ventilation. Little is known about COVID-19 ARDS sequelae. We assessed the mid-term lung status of COVID-19 survivors and investigated factors associated with pulmonary sequelae.

METHODS

All adult COVID-19 patients admitted to the intensive care unit from 25th February to 27th April 2020 were included. Lung function was evaluated through chest CT scan and pulmonary function tests (PFT). Logistic regression was used to identify predictors of persisting lung alterations.

RESULTS

Forty-nine patients (75%) completed lung assessment. Chest CT scan was performed after a median (interquartile range) time of 97 (89-105) days, whilst PFT after 142 (133-160) days. The median age was 58 (52-65) years and most patients were male (90%). The median duration of mechanical ventilation was 11 (6-16) days. Median tidal volume/ideal body weight (TV/IBW) was 6.8 (5.71-7.67) ml/Kg. 59% and 63% of patients showed radiological and functional lung sequelae, respectively. The diffusion capacity of carbon monoxide (DL_CO ) was reduced by 59%, with a median per cent of predicted DL_CO of 72.1 (57.9-93.9) %. Mean TV/IBW during invasive ventilation emerged as an independent predictor of persistent CT scan abnormalities, whilst the duration of mechanical ventilation was an independent predictor of both CT and PFT abnormalities. The extension of lung involvement at hospital admission (evaluated through Radiographic Assessment of Lung Edema, RALE score) independently predicted the risk of persistent alterations in PFTs.

CONCLUSIONS

Both the extent of lung parenchymal involvement and mechanical ventilation protocols predict morphological and functional lung abnormalities months after COVID-19.

DW-UNet: Loss Balance under Local-Patch for 3D Infection Segmentation from COVID-19 CT Images

(1) Background: COVID-19 has been global epidemic. This work aims to extract 3D infection from COVID-19 CT images; (2) Methods: Firstly, COVID-19 CT images are processed with lung region extraction and data enhancement. In this strategy, gradient changes of voxels in different directions respond to geometric characteristics. Due to the complexity of tubular tissues in lung region, they are clustered to the lung parenchyma center based on their filtered possibility. Thus, infection is improved after data enhancement. Then, deep weighted UNet is established to refining 3D infection texture, and weighted loss function is introduced. It changes cost calculation of different samples, causing target samples to dominate convergence direction. Finally, the trained network effectively extracts 3D infection from CT images by adjusting driving strategy of different samples. (3) Results: Using Accuracy, Precision, Recall and Coincidence rate, 20 subjects from a private dataset and eight subjects from Kaggle Competition COVID-19 CT dataset tested this method in hold-out validation framework. This work achieved good performance both in the private dataset (99.94-00.02%, 60.42-11.25%, 70.79-09.35% and 63.15-08.35%) and public dataset (99.73-00.12%, 77.02-06.06%, 41.23-08.61% and 52.50-08.18%). We also applied some extra indicators to test data augmentation and different models. The statistical tests have verified the significant difference of different models. (4) Conclusions: This study provides a COVID-19 infection segmentation technology, which provides an important prerequisite for the quantitative analysis of COVID-19 CT images.