Deep learning of chest X-rays can predict mechanical ventilation outcome in ICU-admitted COVID-19 patients.

The COVID-19 pandemic repeatedly overwhelms healthcare systems capacity and forced the development and implementation of triage guidelines in ICU for scarce resources (e.g. mechanical ventilation). These guidelines were often based on known risk factors for COVID-19. It is proposed that image data, specifically bedside computed X-ray (CXR), provide additional predictive information on mortality following mechanical ventilation that can be incorporated in the guidelines. Deep transfer learning was used to extract convolutional features from a systematically collected, multi-institutional dataset of COVID-19 ICU patients. A model predicting outcome of mechanical ventilation (remission or mortality) was trained on the extracted features and compared to a model based on known, aggregated risk factors. The model reached a 0.702 area under the curve (95% CI 0.707-0.694) at predicting mechanical ventilation outcome from pre-intubation CXRs, higher than the risk factor model. Combining imaging data and risk factors increased model performance to 0.743 AUC (95% CI 0.746-0.732). Additionally, a post-hoc analysis showed an increase performance on high-quality than low-quality CXRs, suggesting that using only high-quality images would result in an even stronger model.

In vivo coronary artery plaque assessment with computed tomography angiography: is there an impact of iterative reconstruction on plaque volume and attenuation metrics?

Background Coronary computed tomography angiography (CTA) allows the evaluation of coronary plaque volume and low attenuation (lipid-rich) component, for plaque vulnerability assessment. Purpose To determine the effect of iterative reconstruction (IR) on coronary plaque volume and composition. Material and Methods Consecutive patients without coronary artery disease were prospectively enrolled for 256-slice CT. Images were reconstructed with both filtered back projection (FBP) and a hybrid IR algorithm (iDose4, Philips) levels 1, 3, 5, and 7. Coronary plaques were assessed according to predefined Hounsfield unit (HU) attenuation intervals, for total plaque and HU-interval volumes. Results Fifty-three patients (mean age, 53.6 years) were included. Noise was significantly decreased and signal-to-noise ratio (SNR) / contrast-to-noise (CNR) were both significantly improved at all IR levels in comparison to FBP. Plaque characterization was performed in 41 patients for a total of 125 plaques. Total plaque volume ranged from 104.4 ± 120.7 to 107.4 ± 128.9 mm3 and low attenuation plaque component from 40.5 ± 54.7 to 43.5 ± 58.9 mm3, with no statistically significant differences between all IR levels and FBP ( P = 0.786 and P ≥ 0.078, respectively). Conclusion IR improved image quality. Total and low attenuation plaque volumes were similar using either IR or FBP.