RETRACTED ARTICLE: Deep learning system to screen coronavirus disease 2019 pneumonia
Ontology highlight
ABSTRACT:
SUBMITTER: Butt C
PROVIDER: S-EPMC7175452 | biostudies-literature | 2020 Apr
REPOSITORIES: biostudies-literature
Similar Datasets
Project description: Not available
| S-EPMC7272237 | biostudies-literature
Project description:ObjectivesTo utilize a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients and compare its quantitative determination performance with radiological residents.MethodsA deep learning algorithm consisted of lesion detection, segmentation, and location was trained and validated in 14,435 participants with chest CT images and definite pathogen diagnosis. The algorithm was tested in a non-overlapping dataset of 96 confirmed COVID-19 patients in three hospitals across China during the outbreak. Quantitative detection performance of the model was compared with three radiological residents with two experienced radiologists' reading reports as reference standard by assessing the accuracy, sensitivity, specificity, and F1 score.ResultsOf 96 patients, 88 had pneumonia lesions on CT images and 8 had no abnormities on CT images. For per-patient basis, the algorithm showed superior sensitivity of 1.00 (95% confidence interval (CI) 0.95, 1.00) and F1 score of 0.97 in detecting lesions from CT images of COVID-19 pneumonia patients. While for per-lung lobe basis, the algorithm achieved a sensitivity of 0.96 (95% CI 0.94, 0.98) and a slightly inferior F1 score of 0.86. The median volume of lesions calculated by algorithm was 40.10 cm3. An average running speed of 20.3 s ± 5.8 per case demonstrated the algorithm was much faster than the residents in assessing CT images (all p < 0.017). The deep learning algorithm can also assist radiologists make quicker diagnosis (all p < 0.0001) with superior diagnostic performance.ConclusionsThe algorithm showed excellent performance in detecting COVID-19 pneumonia on chest CT images compared with resident radiologists.Key points• The higher sensitivity of deep learning model in detecting COVID-19 pneumonia were found compared with radiological residents on a per-lobe and per-patient basis. • The deep learning model improves diagnosis efficiency by shortening processing time. • The deep learning model can automatically calculate the volume of the lesions and whole lung.
| S-EPMC7331494 | biostudies-literature
Project description: Not available
| S-EPMC8818372 | biostudies-literature
Project description:Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Twenty-seven prospective consecutive patients in Renmin Hospital of Wuhan University were collected to evaluate the efficiency of radiologists against 2019-CoV pneumonia with that of the model. An external test was conducted in Qianjiang Central Hospital to estimate the system's robustness. The model achieved a per-patient accuracy of 95.24% and a per-image accuracy of 98.85% in internal retrospective dataset. For 27 internal prospective patients, the system achieved a comparable performance to that of expert radiologist. In external dataset, it achieved an accuracy of 96%. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice.
| S-EPMC7645624 | biostudies-literature
Project description:BackgroundChest computed tomography (CT) has been found to have high sensitivity in diagnosing novel coronavirus pneumonia (NCP) at the early stage, giving it an advantage over nucleic acid detection during the current pandemic. In this study, we aimed to develop and validate an integrated deep learning framework on chest CT images for the automatic detection of NCP, focusing particularly on differentiating NCP from influenza pneumonia (IP).MethodsA total of 148 confirmed NCP patients [80 male; median age, 51.5 years; interquartile range (IQR), 42.5-63.0 years] treated in 4 NCP designated hospitals between January 11, 2020 and February 23, 2020 were retrospectively enrolled as a training cohort, along with 194 confirmed IP patients (112 males; median age, 65.0 years; IQR, 55.0-78.0 years) treated in 5 hospitals from May 2015 to February 2020. An external validation set comprising 57 NCP patients and 50 IP patients from 8 hospitals was also enrolled. Two deep learning schemes (the Trinary scheme and the Plain scheme) were developed and compared using receiver operating characteristic (ROC) curves.ResultsOf the NCP lesions, 96.6% were >1 cm and 76.8% were of a density <-500 Hu, indicating them to have less consolidation than IP lesions, which had nodules ranging from 5-10 mm. The Trinary scheme accurately distinguished NCP from IP lesions, with an area under the curve (AUC) of 0.93. For patient-level classification in the external validation set, the Trinary scheme outperformed the Plain scheme (AUC: 0.87 vs. 0.71) and achieved human specialist-level performance.ConclusionsOur study has potentially provided an accurate tool on chest CT for early diagnosis of NCP with high transferability and showed high efficiency in differentiating between NCP and IP; these findings could help to reduce misdiagnosis and contain the pandemic transmission.
| S-EPMC7867927 | biostudies-literature
Project description:Direct acting antivirals and monoclonal antibodies reduce morbidity and mortality associated with severe acute respiratory syndrome coronavirus 2 infection. Persons at higher risk for disease progression and hospitalized patients with coronavirus disease-2019 (COVID-19) benefit most from available therapies. Following an emphasis on inpatient treatment of COVID-19 during the early pandemic, several therapeutic options were developed for outpatients with COVID-19. Additional clinical trials and real-world studies are needed to keep pace with the evolving pandemic.
| S-EPMC9701636 | biostudies-literature
Project description: Not available
| S-EPMC8321889 | biostudies-literature
Project description: Not available
| S-EPMC7903031 | biostudies-literature
Project description: Not available
| S-EPMC8014714 | biostudies-literature
Project description:Background/aimsAlthough it is near concluded that renin-angiotensin system inhibitors do not have a harmful effect on coronavirus disease 2019 (COVID-19), there is no report about whether angiotensin receptor blockers (ARBs) and angiotensin-converting enzyme inhibitors (ACEIs) offer any protective role. This study aimed to compare the association of ARBs and ACEIs with COVID-19-related mortality.MethodsAll patients with COVID-19 in Korea between January 19 and April 16, 2020 were enrolled. The association of ARBs and ACEIs with mortality within 60 days were evaluated. A comparison of hazard ratio (HR) was performed between COVID-19 patients and a retrospective cohort of pneumonia patients hospitalized in 2019 in Korea.ResultsAmong 10,448 COVID-19 patients, ARBs and ACEIs were prescribed in 1,231 (11.7%) and 57 (0.6%) patients, respectively. After adjusting for age, sex, and history of comorbidities, the ARB group showed neutral association (HR, 1.034; 95% CI, 0.765 to 1.399; p = 0.8270) and the ACEI groups showed no significant associations likely owing to the small population size (HR, 0.736; 95% CI, 0.314 to 1.726; p = 0.4810). When comparing HR between COVID-19 patients and a retrospective cohort of patients hospitalized with pneumonia in 2019, the trend of ACEIs showed similar benefits, whereas the protective effect of ARBs observed in the retrospective cohort was absent in COVID-19 patients. Meta-analyses showed significant positive correlation with survival of ACEIs, whereas a neutral association between ARBs and mortality.ConclusionAlthough ARBs or ACEIs were not associated with fatal outcomes, potential beneficial effects of ARBs observed in pneumonia were attenuated in COVID-19.
| S-EPMC8137390 | biostudies-literature