Objectives To evaluate and compare the diagnostic performances of a commercialized artificial intelligence (AI) algorithm for diagnosing pulmonary embolism (PE) on CT pulmonary angiogram (CTPA) with those of emergency radiologists in routine clinical practice. Methods This was an IRB-approved retrospective multicentric study including patients with suspected PE from September to December 2019 (i.e., during a preliminary evaluation period of an approved AI algorithm). CTPA quality and conclusions by emergency radiologists were retrieved from radiological reports. The gold standard was a retrospective review of CTPA, radiological and clinical reports, AI outputs, and patient outcomes. Diagnostic performance metrics for AI and radiologists were assessed in the entire cohort and depending on CTPA quality. Results Overall, 1202 patients were included (median age: 66.2 years). PE prevalence was 15.8% (190/1202). The AI algorithm detected 219 suspicious PEs, of which 176 were true PEs, including 19 true PEs missed by radiologists. In the cohort, the highest sensitivity and negative predictive values (NPVs) were obtained with AI (92.6% versus 90% and 98.6% versus 98.1%, respectively), while the highest specificity and positive predictive value (PPV) were found with radiologists (99.1% versus 95.8% and 95% versus 80.4%, respectively). Accuracy, specificity, and PPV were significantly higher for radiologists except in subcohorts with poor-to-average injection quality. Radiologists positively evaluated the AI algorithm to improve their diagnostic comfort (55/79 [69.6%]). Conclusion Instead of replacing radiologists, AI for PE detection appears to be a safety net in emergency radiology practice due to high sensitivity and NPV, thereby increasing the self-confidence of radiologists. Key Points • Both the AI algorithm and emergency radiologists showed excellent performance in diagnosing PE on CTPA (sensitivity and specificity ≥ 90%; accuracy ≥ 95%). • The AI algorithm for PE detection can help increase the sensitivity and NPV of emergency radiologists in clinical practice, especially in cases of poor-to-moderate injection quality. • Emergency radiologists recommended the use of AI for PE detection in satisfaction surveys to increase their confidence and comfort in their final diagnosis. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-022-08645-2.
Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) omicron variant has a higher infection rate than previous variants but results in less severe disease. However, the impacts of omicron and vaccination on chest CT findings are difficult to evaluate. Purpose To investigate the impact of vaccination status and predominant variant on chest CT findings, diagnostic and severity scores in multicenter sample of consecutive patients referred to emergency departments for proven COVID-19. Materials and Methods This retrospective, multicenter study included adults referred to 93 emergency departments with SARS-CoV-2 infection according to RT-PCR and known vaccination status between July 2021 and March 2022. Clinical data and structured chest CT reports including semiquantitative diagnostic and severity scores following the French Society of Radiology-Thoracic Imaging Society guidelines were extracted from a teleradiology database. Observations were divided into ‘delta-predominant’, ‘transition’, and ‘omicron-predominant’ periods. Associations between scores and variant and vaccination status were investigated with Chi-square tests and ordinal regressions. Multivariable analyses evaluated the influence of omicron variant and vaccination status on the diagnostic and severity scores. Results Overall, 3876 patients were included (median age: 68 years [Q1-Q3: 54-80], 1695 females). Diagnostic and severity scores were associated with the predominant variant (delta- versus omicron-predominant, Chi-square=112.4 and 33.7, both P <.001) and vaccination (Chi-square=243.6 and 210, both P <.001) and their interaction (Chi-square=4.3, P =.04 and Chi-square=28.7, P <.001, respectively). In multivariable analyses, omicron variant was associated with lower odds of typical CT findings than delta variant (OR=0.46, P <.001). Two and three vaccine doses were associated with lower odds of demonstrating typical CT findings (OR=0.32 and OR=0.20, both P <.001), and of having high severity score (OR=0.47 and OR=0.33, both P <.001), compared with unvaccinated patients. Conclusion Both the omicron variant and vaccination were associated with less typical chest CT manifestations for COVID-19 and lesser extent of disease. See also the editorial by Yoon and Goo in this issue.
Background COVID-19 pandemic highlighted the need for real-time monitoring of diseases evolution to rapidly adapt restrictive measures. This prospective multicentric study aimed at investigating radiological markers of COVID-19-related emergency activity as global estimators of pandemic evolution in France. We incorporated two sources of data from March to November 2020: an open-source epidemiological dataset, collecting daily hospitalisations, intensive care unit admissions, hospital deaths and discharges, and a teleradiology dataset corresponding to the weekly number of CT-scans performed in 65 emergency centres and interpreted remotely. CT-scans specifically requested for COVID-19 suspicion were monitored. Teleradiological and epidemiological time series were aligned. Their relationships were estimated through a cross-correlation function, and their extremes and breakpoints were compared. Dynamic linear models were trained to forecast the weekly hospitalisations based on teleradiological activity predictors. Results A total of 100,018 CT-scans were included over 36 weeks, and 19,133 (19%) performed within the COVID-19 workflow. Concomitantly, 227,677 hospitalisations were reported. Teleradiological and epidemiological time series were almost perfectly superimposed (cross-correlation coefficients at lag 0: 0.90–0.92). Maximal number of COVID-19 CT-scans was reached the week of 2020-03-23 (1 086 CT-scans), 1 week before the highest hospitalisations (23,542 patients). The best valid forecasting model combined the number of COVID-19 CT-scans and the number of hospitalisations during the prior two weeks and provided the lowest mean absolute percentage (5.09%, testing period: 2020-11-02 to 2020-11-29). Conclusion Monitoring COVID-19 CT-scan activity in emergencies accurately and instantly predicts hospitalisations and helps adjust medical resources, paving the way for complementary public health indicators.
Objectives To evaluate the impact of COVID-19’s lockdown on radiological examinations in emergency services. Methods Retrospective, multicentre analysis of radiological examinations requested, via our teleradiology network, from 2017 to 2020 during two timeframes (calendar weeks 5–8 and then 12–15). We included CT scans or MRIs performed for strokes, multiple traumas (Body-CT), cranial traumas (CTr) and acute non-traumatic abdominal pain (ANTAP). We evaluated the number and percentages of examinations performed, of those with a pathological conclusion, and of examinations involving the chest. Results Our study included 25 centres in 2017, 29 in 2018, 43 in 2019 and 59 in 2020. From 2017 to 2019, the percentages of examinations were constant, which was also true for chest CTs. Each centre’s number of examinations, gender distribution and patient ages were unchanged. In 2020, examinations significantly decreased: suspected strokes decreased by 36% (1052 vs 675, p < 0.001), Body-CT by 62% (349 vs 134, p < 0.001), CTr by 52% (1853 vs 895, p < 0.001) and for ANTAP, appendicitis decreased by 38% (45 vs 90, not statistically significant (NS)) sigmoiditis by 44% (98 vs 55, NS), and renal colic by 23% (376 vs 288, NS). The number of examinations per centre decreased by 13% (185.5 vs 162.5, p < 0.001), whereas the number of examinations of the "chest" region increased by 170% (1205 vs 3766, p < 0.001). Conclusion Teleradiology enabled us to monitor the impact of the COVID-19 pandemic management on emergency activities, showing a global decrease in the population's use of care.
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