Evaluating the Performance of a Commercially Available AI Algorithm for Automated Detection of Pulmonary Embolism on CECT and CTPA of COVID-19 Patients

Objective To investigate the performance of a commercially available artificial intelligence (AI) algorithm for detection of pulmonary embolism (PE) on contrast-enhanced CTs in patients hospitalized for COVID-19. Patients & Methods Retrospective analysis was performed of all contrast-enhanced chest CTs on patients admitted for COVID-19 between March 2020 and December 2021. Based on the original radiology reports, all PE-positive exams were included (n=527). Using a reversed flow single gate diagnostic accuracy case-control model, a randomly selected cohort of PE-negative exams (n=977) was included. Pulmonary parenchymal disease severity was assessed for all included studies using a semi-quantitative system, the Total Severity Score (TSS). All included CTs were sent for interpretation by the commercially available AI algorithm, Aidoc. Discrepancies between AI and original radiology reports were resolved by three blinded radiologists, who rendered a final determination of indeterminate, positive, or negative. Results A total of 78 studies were found to be discrepant, of which 13 (16.6%) were deemed indeterminate by readers and excluded. The sensitivity and specificity of AI was 93.2%;(95% confidence interval [CI] 90.6-95.2%), and 99.6%;(95% CI 98.9-99.9%), respectively. AI's accuracy for all TSS groups (mild, moderate, severe) was high (98.4%, 96.7%, and 97.2%, respectively). AI was more accurate in PE detection on CTPAs vs CECTs (P < .001), with optimal HU of 362 (P=.048). Conclusion The AI algorithm demonstrated high sensitivity, specificity, and accuracy for PE on contrast enhanced CTs in COVID-19 patients regardless of parenchymal disease. Accuracy was significantly affected by the mean attenuation of the pulmonary vasculature. How this affects the legitimacy of the binary outcomes reported by AI is not yet known.

Evaluating the Performance of a Commercially Available Artificial Intelligence Algorithm for Automated Detection of Pulmonary Embolism on Contrast-Enhanced Computed Tomography and Computed Tomography Pulmonary Angiography in Patients With Coronavirus Disease 2019.

Objective: To investigate the performance of a commercially available artificial intelligence (AI) algorithm for the detection of pulmonary embolism (PE) on contrast-enhanced computed tomography (CT) scans in patients hospitalized for coronavirus disease 2019 (COVID-19). Patients and Methods: Retrospective analysis was performed of all contrast-enhanced chest CT scans of patients admitted for COVID-19 between March 1, 2020 and December 31, 2021. Based on the original radiology reports, all PE-positive examinations were included (n=527). Using a reversed-flow single-gate diagnostic accuracy case-control model, a randomly selected cohort of PE-negative examinations (n=977) was included. Pulmonary parenchymal disease severity was assessed for all the included studies using a semiquantitative system, the total severity score. All included CT scans were sent for interpretation by the commercially available AI algorithm, Aidoc. Discrepancies between AI and original radiology reports were resolved by 3 blinded radiologists, who rendered a final determination of indeterminate, positive, or negative. Results: A total of 78 studies were found to be discrepant, of which 13 (16.6%) were deemed indeterminate by readers and were excluded. The sensitivity and specificity of AI were 93.2% (95% CI, 90.6%-95.2%) and 99.6% (95% CI, 98.9%-99.9%), respectively. The accuracy of AI for all total severity score groups (mild, moderate, and severe) was high (98.4%, 96.7%, and 97.2%, respectively). Artificial intelligence was more accurate in PE detection on CT pulmonary angiography scans than on contrast-enhanced CT scans (P<.001), with an optimal Hounsfield unit of 362 (P=.048). Conclusion: The AI algorithm demonstrated high sensitivity, specificity, and accuracy for PE on contrast-enhanced CT scans in patients with COVID-19 regardless of parenchymal disease. Accuracy was significantly affected by the mean attenuation of the pulmonary vasculature. How this affects the legitimacy of the binary outcomes reported by AI is not yet known.

Trends in coronary calcium score and coronary CT angiography imaging volume during the COVID-19 pandemic.

OBJECTIVES: The COVID-19 pandemic disrupted the delivery of preventative care and management of acute diseases. This study assesses the effect of the COVID-19 pandemic on coronary calcium score and coronary CT angiography imaging volume. MATERIALS AND METHODS: A single institution retrospective review of consecutive patients presenting for coronary calcium score or coronary CT angiography examinations between January 1, 2020 to January 4, 2022 was performed. The weekly volume of calcium score and coronary CT angiogram exams were compared. RESULTS: In total, 1,817 coronary calcium score CT and 5,895 coronary CT angiogram examinations were performed. The average weekly volume of coronary CTA and coronary calcium score CT exams decreased by up to 83% and 100%, respectively, during the COVID-19 peak period compared to baseline (P < 0.0001). The post-COVID recovery through 2020 saw weekly coronary CTA volumes rebound to 86% of baseline (P = 0.024), while coronary calcium score CT volumes remained muted at only a 53% recovery (P < 0.001). In 2021, coronary CTA imaging eclipsed pre-COVID rates (P = 0.012), however coronary calcium score CT volume only reached 67% of baseline (P < 0.001). CONCLUSIONS: A significant decrease in both coronary CTA and coronary calcium score CT volume occurred during the peak-COVID-19 period. In 2020 and 2021, coronary CTA imaging eventually superseded baseline rates, while coronary calcium score CT volumes only reached two thirds of baseline. These findings highlight the importance of resumption of screening exams and should prompt clinicians to be aware of potential undertreatment of patients with coronary artery disease.

United States lung cancer screening program websites: radiology representation, multimedia and multilingual content.

PURPOSE: To assess radiology representation, multimedia content, and multilingual content of United States lung cancer screening (LCS) program websites. MATERIALS AND METHODS: We identified the websites of US LCS programs with the Google internet search engine using the search terms lung cancer screening, low-dose CT screening, and lung screening. We used a standardized checklist to assess and collect specific content, including information regarding LCS staff composition and references to radiologists and radiology. We also tabulated types and frequencies of included multimedia and multilingual content and patient narratives. RESULTS: We analyzed 257 unique websites. Of these, only 48% (124 of 257) referred to radiologists or radiology in text, images, or videos. Radiologists were featured in images or videos on only 14% (36 of 257) of websites. Radiologists were most frequently acknowledged for their roles in reading or interpreting imaging studies (35% [90 of 574]). Regarding multimedia content, only 36% (92 of 257) of websites had 1 image, 27% (70 of 257) included 2 or more images, and 26% (68 of 257) of websites included one or more videos. Only 3% (7 of 257) of websites included information in a language other than English. Patient narratives were found on only 15% (39 of 257) of websites. CONCLUSIONS: The field of Radiology is mentioned in text, images, or videos by less than half of LCS program websites. Most websites make only minimal use of multimedia content such as images, videos, and patient narratives. Few websites provide LCS information in languages other than English, potentially limiting accessibility to diverse populations.

Resolution of acute pulmonary embolism using anticoagulation therapy alone in coronavirus disease 2019.

OBJECTIVE: To investigate the radiographic resolution of acute pulmonary embolism (PE) using contrast-enhanced computed tomography (CECT) examinations in patients diagnosed with acute PE while hospitalized with coronavirus disease 2019 (COVID-19) and to understand the mid-term and long-term implications of anticoagulation therapy. METHODS: We identified patients with acute PE per CECT and at least one follow-up CECT from March 11, 2020, to May 27, 2021, using a prospective registry of all hospitalized patients with COVID-19 infection receiving care within a multicenter Health System. Initial and follow-up CECT examinations were reviewed independently by two radiologists to evaluate for PE resolution. The Modified Miller Score was used to assess for thrombus burden at diagnosis and on follow-up. RESULTS: Of the 6070 hospitalized patients with COVID-19 infection, 5.7% (348/6070) were diagnosed with acute PE and 13.5% (47/348) had a follow-up CECT examination. The mean ± standard deviation time to follow-up imaging was 44 ± 48 days (range, 3-161 days). Of 47 patients, 47 (72.3%) had radiographic resolution of PE, with a mean time to follow-up of 48 ± 43 days (range, 6-239 days). All patients received anticoagulation monotherapy for a mean of 149 ± 95 days and this included apixaban (63.8%), warfarin (12.8%), and rivaroxaban (8.5%), among others. The mean Modified Miller Score at PE diagnosis and follow-up was 4.8 ± 4.2 (range, 1-14) and 1.4 ± 3.3 (range, 0-16; P < .0001), respectively. Nine patients (19%) died at a mean of 13 ± 8 days after follow-up CECT (range, 1-27 days) and at a mean of 28 ± 16 days after admission (range, 11-68 days). Seen of the nine deaths (78%) deaths were associated with progression of COVID-19 pneumonia. CONCLUSIONS: Hospitalized patients with COVID-19 have a clinically apparent 5.7% rate of developing PE. In patients with follow-up imaging, 72.3% had radiographic thrombus resolution at a mean of 44 days while on anticoagulation. Prospective studies of the natural history of PEs with COVID-19 that include systematic follow-up imaging are warranted to help guide anticoagulation recommendations.