Comparison of doses received from non-contrast enhanced brain CT examinations between two CT scanners.

Objectives: Medical devices based on X-ray imaging, such as computed tomography, are considered notable sources of artificial radiation. The aim of this study was to compare the computed tomography dose volume index, the dose length product, and the effective dose of the brain non-contrast enhanced examination on two CT scanners to determine the current state in terms of radiation doses, compare doses to the reference values, and possibly optimize the examination. Materials and methods: Data from January 2020 to the second half of 2021 were retrospectively obtained by accessing dose reports from the Picture Archiving and Communication System (PACS). Data were collected and analyzed in Microsoft Excel. The effective dose was estimated using the dose-length product parameter and the normalized conversion factor for a given anatomical region. For statistical analysis, a two-sample t-test was used. Results: The first data set consists of 200 patients (100 and 100 for older and newer CT scanners) regardless of the scan technique; the average CTDIvol and DLP for the older CT scanner were 57.61 ± 2.89 mGy and 993.28 ± 146.18 mGy cm, and for the newer CT scanner, 43.66 ± 11.15 mGy and 828.14 ± 130.06 mGy cm. The second data set consists of 100 patients (50 for the older CT scanner and 50 for the newer CT scanner) for a sequential scan; the average CTDIvol and DLP for the older CT scanner were 58.63 ± 3.33 mGy and 949.42 ± 80.87 mGy.cm, and for the newer CT, 57.25 ± 3.4 mGy and 942.13 ± 73.05 mGy cm. The third data set consists of 40 patients (20 and 20 for older and newer CT scanners) for the helical scan - the average CTDIvol and DLP for the older CT scanner were 54.6 ± 0 mGy and 1252.2 ± 52.11 mGy.cm, and for the newer CT, 37.18 ± 2.52 mGy and 859.66 ± 72.04 mGy cm. The difference between the older and newer CT scanners in terms of dose reduction was approximately 30 % in favor of the newer scanner for noncontrast enhanced brain examinations performed using the helical scan technique. Conclusion: A non-contrast enhanced brain examination scanned with newer CT equipment was associated with a lower radiation burden on the patient.

Hybrid Artificial Intelligence Solution Combining Convolutional Neural Network and Analytical Approach Showed Higher Accuracy in A-lines Detection on Lung Ultrasound in Thoracic Surgery Patients Compared with Radiology Resident.

OBJECTIVES: Lung ultrasound reduces the number of chest X-rays after thoracic surgery and thus the radiation. COVID-19 pandemic has accelerated research in lung ultrasound artifacts detection using artificial intelligence. This study evaluates the accuracy of artificial intelligence in A-lines detection in thoracic surgery patients using a novel hybrid solution that combines convolutional neural networks and analytical approach and compares it with a radiology resident and radiology experts' results. DESIGN: Prospective observational study. MATERIAL AND METHODS: Single-center study evaluates the accuracy of artificial intelligence and a radiology resident in A-line detection on lung ultrasound footages compared with the consensual opinion of two expert radiologists as the reference. After resident's first reading, the artificial intelligence results were presented to the resident and he was asked to revise the results based on artificial intelligence. RESULTS: 82 consecutive patients underwent 82 ultrasound examinations. 328 ultrasound recordings were evaluated. Accuracy, sensitivity, specificity, positive and negative predictive values of artificial inelligence in A-line detection were 0.866, 0.928, 0.834, 0.741 and 0.958 respectively. The resident's values were 0.558, 0.973, 0.346, 0.432 and 0.962 respectively. The resident's values after correction based on artificial intelligence results were 0.854, 0.991, 0.783, 0.701 and 0.994 respectively. CONCLUSION: Artificial intelligence showed high accuracy in A-line detection in thoracic surgery patients and was more accurate compared to a resident. Artificial intelligence could play important role in lung ultrasound artifact detection in thoracic surgery patients and in residents' education.

The role of computed tomography pulmonary angiography in COVID-19 patients with suspected pulmonary embolism.

OBJECTIVES: This study is aimed to determine the location and distribution of pulmonary embolism (PE) and presence of signs potentially indicative of right heart overload on computed tomography pulmonary angiography (CTPA) in COVID-19 and non-COVID-19 patients. We also evaluated the extent and severity of COVID-19-associated lung changes in relation to PE. METHODS: The total number of 1,698 patients with CTPA included in the study were divided into 2 groups according to their COVID-19 status and each group was divided into 2 subgroups based on their PE status. These groups and subgroups were compared in terms of location of PE, diameter of pulmonary artery, right heart strain, ground-glass opacities (GGO), consolidations and other imaging features. RESULTS: In COVID-19 patients, there was a significant predominance of PE in peripheral branches of pulmonary artery (p < 0.001). There was an increased right-to-left ratio of ventricular diameters in cases with PE (p = 0.032 in patients with COVID-19 and p < 0.001 in non-COVID-19 patients). There was no association between the extent and severity of the disease and distribution of PE. CONCLUSION: COVID-19 is associated with a higher incidence of peripheral location of PE and presence of GGO. There were signs indicative of right heart overload in cases with PE regardless of COVID-19 (Tab. 3, Fig. 1, Ref. 29) Keywords: COVID-19, computed tomography, CTPA, pneumonia, pulmonary embolism.

Association between pulmonary embolism and COVID-19 disease.

OBJECTIVES: The current retrospective study focused on evaluation of the relationship between pulmonary embolism during COVID-19 pandemic and demographic, presenting symptoms, comorbidities and laboratory results in patients who underwent CT angiography of the pulmonary arteries. METHODS: The study enrolled all adult patients with suspected acute pulmonary embolism (PE) who underwent computed tomography pulmonary angiography (CTPA) between March 1, 2020, and April 30, 2022, during the SARS-CoV-2 pandemic. 1698 CTPAs were reviewed and various data were collected. Based on examination results, patients were divided into 4 groups: a group with positive PE and a group with negative PE for both COVID-19 and non-COVID-19 patients. RESULTS: When comparing different predictors of COVID-19 patients and non-COVID-19 patients we noticed lower probability of PE in female gender (OR 0.77, 95% CI: 0.60-1.00, p = 0.052) and in chronic obstructive pulmonary disease (COPD) patients (OR 0.6, 95% CI: 0.38-0.90, p = 0.017). Higher probability of PE was in cases of older age (OR 1.02, 95% CI: 1.01-1.02, p < 0.001), increased heart rate (OR 1.01, 95% CI: 1.01-1.02, p < 0.001) and increased D-dimer levels (OR 1.03, 95% CI: 1.02-1.04, p < 0.001). CONCLUSION: Considering predictors of PE there was a significantly lower risk of PE in the female gender and COPD, and a higher risk with increasing age, heart rate, and D-dimer levels.