Deep-learning algorithms for the interpretation of chest radiographs to aid in the triage of COVID-19 patients: A multicenter retrospective study

Abstract

The recent medical applications of deep-learning (DL) algorithms have demonstrated their clinical efficacy in improving speed and accuracy of image interpretation. If the DL algorithm achieves a performance equivalent to that achieved by physicians in chest radiography (CR) diagnoses with Coronavirus disease 2019 (COVID-19) pneumonia, the automatic interpretation of the CR with DL algorithms can significantly reduce the burden on clinicians and radiologists in sudden surges of suspected COVID-19 patients. The aim of this study was to evaluate the efficacy of the DL algorithm for detecting COVID-19 pneumonia on CR compared with formal radiology reports. This is a retrospective study of adult patients that were diagnosed as positive COVID-19 cases based on the reverse transcription polymerase chain reaction among all the patients who were admitted to five emergency departments and one community treatment center in Korea from February 18, 2020 to May 1, 2020. The CR images were evaluated with a publicly available DL algorithm. For reference, CR images without chest computed tomography (CT) scans classified as positive for COVID-19 pneumonia were used given that the radiologist identified ground-glass opacity, consolidation, or other infiltration in retrospectively reviewed CR images. Patients with evidence of pneumonia on chest CT scans were also classified as COVID-19 pneumonia positive outcomes. The overall sensitivity and specificity of the DL algorithm for detecting COVID-19 pneumonia on CR were 95.6%, and 88.7%, respectively. The area under the curve value of the DL algorithm for the detection of COVID-19 with pneumonia was 0.921. The DL algorithm demonstrated a satisfactory diagnostic performance comparable with that of formal radiology reports in the CR-based diagnosis of pneumonia in COVID-19 patients. The DL algorithm may offer fast and reliable examinations that can facilitate patient screening and isolation decisions, which can reduce the medical staff workload during COVID-19 pandemic situations.

Impact of temporary closures of emergency departments during the COVID-19 outbreak on clinical outcomes for emergency patients in a metropolitan area

“…In Daegu and Gyeongbuk, each ED has committed to minimizing ED closures through measures such as physicians, nurses, and healthcare workers wearing appropriate personal protective equipment (PPE); temporary increases in isolation beds with negative pressure; a cohort isolation space for each ED); and chest radiographies for triage patients, thereby establishing a screening and patient triage protocol [ 6 , 8 , 30 ]. However, preparedness for a disaster before it occurs will be much more effective in protecting the safety of all citizens, rather than attempting to mitigate the risk after a disaster.…”

Machine learning is the key to diagnose COVID-19: a proof-of-concept study

“…Some studies have already investigated the use of artificial intelligence for COVID-19 diagnosis and their number is progressively increasing with the pandemic duration 47 , 48 . Many of these studies are based on deep neural networks to improve COVID-19 diagnosis by chest-CT or X-ray imaging, particularly to help to differentiate between COVID-19 lesions and bacterial lung diseases 49 – 52 . For examples, the COVID-net tool based on 16,756 chest radiography images across 13,645 patients has an accuracy of 92.4%, the COVID-19 detection neural network (COVNet) based on 4356 chest-CT from 3322 patients has an accuracy of 95% 53 , 54 .…”

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

“…A vast majority of studies used EHR data, while two studies used administrative and claims as the primary dataset. 27 28 Study populations included adults in the ED, 26 27 28 29 30 31 32 33 34 35 36 37 home care patients, 38 and a mixture of adult and pediatric ED patients. 39 Most studies were based in the United States, but other study locations included Hong Kong, 27 Germany, 32 Italy, 39 Portugal, 37 and South Korea.…”

“…39 Most studies were based in the United States, but other study locations included Hong Kong, 27 Germany, 32 Italy, 39 Portugal, 37 and South Korea. 34 35 Sample size ranged from 199 to 2,910,321 observations.…”

“…Outcomes addressed in studies included mortality, 29 30 future posttraumatic stress disorder (PTSD) sequelae, 26 the novel coronavirus disease 2019 (COVID-19) infection status, 31 35 39 ED wait time, 27 intensive care unit (ICU) admission from the ED, 37 need for head computed tomography (CT), 32 cardiac arrest, 34 stroke severity, 28 and ED utilization. 30 33 36 38 ML methods were used in several studies, including the use of logistic regression, generalized linear models, neural network, and decision tree–based models to apply statistical learning to the prediction of deterioration, 30 37 stroke severity, 28 COVID-19 diagnosis, 31 35 39 wait time, 27 and need for head CT, 32 while the other study used autoregressive integrated moving average to explore time-dependent patient flow.…”