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RATIONALE AND OBJECTIVES: Within global sustainable resource management efforts, reducing healthcare energy consumption is of public concern. This study aims to analyze the energy consumption of three Dual-Energy computed tomography (DECT) scanners and to predict the power consumption based on scan acquisition parameters. MATERIALS AND METHODS: This study consisted of two parts assessing three DECT scanners: one Dual-Source and two Single-Source DECT. In Part A, the energy consumption for various single- and DECT scans with different acquisition parameters using a chest phantom was measured. The measurements were compared to the calculated power consumption. In Part B, the energy consumption baselines during nonutilization states of the DECT devices: idle (ready to scan), low-power (incomplete shutdown), and system-off mode (complete shutdown) were measured. Descriptive statistics were used. RESULTS: The phantom study revealed a positive correlation between measured and calculated energy consumption (r2 =0.82), except for single-source split-filter DECT acquisitions, indicating a relationship between scan parameters and energy consumption. The baseline study results showed a mean energy consumption of 2.6kWh/hour ± 1.34kWh in idle, 0.89kWh/hour ± 0.42kWh in low-power, and < 0.01kWh/hour ± 0.003kWh in the system-off state. The potential total annual CO2 savings for the assessed DECT scanners amounted to 3767kg CO2 (low power) and 5868kg CO2 (system off) compared to the idle state. Time-related calculations indicated energy savings starting after 5 min in low-power- and after 2 min in the system-off state. Therefore, switching off the scanner, even during shorter periods of non-utilization, can be efficient. CONCLUSION: Our results emphasize a positive correlation between scan parameters and energy consumption in DECT. Complete shutdown of DECT devices can have a significant ecological-economic impact.
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Objective@#To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management. @*Materials and Methods@#All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients’ needs for intensive care (yeso) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans. @*Results@#While demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve [AUC] = 0.88; 95% confidence interval [CI] = 0.79–0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77–0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85–0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66–0.88). @*Conclusion@#Chest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.
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Objective@#To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management. @*Materials and Methods@#All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients’ needs for intensive care (yeso) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans. @*Results@#While demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve [AUC] = 0.88; 95% confidence interval [CI] = 0.79–0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77–0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85–0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66–0.88). @*Conclusion@#Chest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.
