RETRACTED ARTICLE: Deep learning system to screen coronavirus disease 2019 pneumonia
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ABSTRACT:
SUBMITTER: Butt C
PROVIDER: S-EPMC7175452 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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: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: 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
Project description:A novel coronavirus, called 2019-nCoV, was recently found in Wuhan, Hubei Province of China, and now is spreading across China and other parts of the world. Although there are some drugs to treat 2019-nCoV, there is no proper scientific evidence about its activity on the virus. It is of high significance to develop a drug that can combat the virus effectively to save valuable human lives. It usually takes a much longer time to develop a drug using traditional methods. For 2019-nCoV, it is now better to rely on some alternative methods such as deep learning to develop drugs that can combat such a disease effectively since 2019-nCoV is highly homologous to SARS-CoV. In the present work, we first collected virus RNA sequences of 18 patients reported to have 2019-nCoV from the public domain database, translated the RNA into protein sequences, and performed multiple sequence alignment. After a careful literature survey and sequence analysis, 3C-like protease is considered to be a major therapeutic target and we built a protein 3D model of 3C-like protease using homology modeling. Relying on the structural model, we used a pipeline to perform large scale virtual screening by using a deep learning based method to accurately rank/identify protein-ligand interacting pairs developed recently in our group. Our model identified potential drugs for 2019-nCoV 3C-like protease by performing drug screening against four chemical compound databases (Chimdiv, Targetmol-Approved_Drug_Library, Targetmol-Natural_Compound_Library, and Targetmol-Bioactive_Compound_Library) and a database of tripeptides. Through this paper, we provided the list of possible chemical ligands (Meglumine, Vidarabine, Adenosine, D-Sorbitol, D-Mannitol, Sodium_gluconate, Ganciclovir and Chlorobutanol) and peptide drugs (combination of isoleucine, lysine and proline) from the databases to guide the experimental scientists and validate the molecules which can combat the virus in a shorter time.
| S-EPMC7266118 | biostudies-literature
Project description:Since its first outbreak, Coronavirus Disease 2019 (COVID-19) has been rapidly spreading worldwide and caused a global pandemic. Rapid and early detection is essential to contain COVID-19. Here, we first developed a deep learning (DL) integrated radiomics model for end-to-end identification of COVID-19 using CT scans and then validated its clinical feasibility. We retrospectively collected CT images of 386 patients (129 with COVID-19 and 257 with other community-acquired pneumonia) from three medical centers to train and externally validate the developed models. A pre-trained DL algorithm was utilized to automatically segment infected lesions (ROIs) on CT images which were used for feature extraction. Five feature selection methods and four machine learning algorithms were utilized to develop radiomics models. Trained with features selected by L1 regularized logistic regression, classifier multi-layer perceptron (MLP) demonstrated the optimal performance with AUC of 0.922 (95% CI 0.856-0.988) and 0.959 (95% CI 0.910-1.000), the same sensitivity of 0.879, and specificity of 0.900 and 0.887 on internal and external testing datasets, which was equivalent to the senior radiologist in a reader study. Additionally, diagnostic time of DL-MLP was more efficient than radiologists (38 s vs 5.15 min). With an adequate performance for identifying COVID-19, DL-MLP may help in screening of suspected cases.
| S-EPMC7886892 | biostudies-literature
Project description:Objectives: To develop and validate a radiomics model for distinguishing coronavirus disease 2019 (COVID-19) pneumonia from influenza virus pneumonia. Materials and Methods: A radiomics model was developed on the basis of 56 patients with COVID-19 pneumonia and 90 patients with influenza virus pneumonia in this retrospective study. Radiomics features were extracted from CT images. The radiomics features were reduced by the Max-Relevance and Min-Redundancy algorithm and the least absolute shrinkage and selection operator method. The radiomics model was built using the multivariate backward stepwise logistic regression. A nomogram of the radiomics model was established, and the decision curve showed the clinical usefulness of the radiomics nomogram. Results: The radiomics features, consisting of nine selected features, were significantly different between COVID-19 pneumonia and influenza virus pneumonia in both training and validation data sets. The receiver operator characteristic curve of the radiomics model showed good discrimination in the training sample [area under the receiver operating characteristic curve (AUC), 0.909; 95% confidence interval (CI), 0.859-0.958] and in the validation sample (AUC, 0.911; 95% CI, 0.753-1.000). The nomogram was established and had good calibration. Decision curve analysis showed that the radiomics nomogram was clinically useful. Conclusions: The radiomics model has good performance for distinguishing COVID-19 pneumonia from influenza virus pneumonia and may aid in the diagnosis of COVID-19 pneumonia.
| S-EPMC8239147 | biostudies-literature
Project description:Purpose of reviewThe first studies on COVID-19 patients with acute respiratory distress syndrome (ARDS) described a high rate of secondary bacterial ventilator-associated pneumonia (VAP). The specificity of VAP diagnoses in these patients are reviewed, including their actual rate.Recent findingsPublished studies described high rates of bacterial VAP among COVID-19 patients with ARDS, and these VAP episodes are usually severe and of specifically poor prognosis with high mortality. Indeed, Severe acute respiratory syndrome - coronavirus disease 19 (SARS-CoV2) infection elicits alterations that may explain a high risk of VAP. In addition, breaches in the aseptic management of patients might have occurred when the burden of care was heavy. In addition, VAP in these patients is more frequently suspected, and more often investigated with diagnostic tools based on molecular techniques.SummaryVAP is frequented and of particularly poor prognosis in COVID-19 patients with ARDS. It can be explained by SARS-CoV-2 pathophysiology, and also breaches in the aseptic procedures. In addition, tools based on molecular techniques allow an early diagnosis and unmask VAP usually underdiagnosed by traditional culture-based methods. The impact of molecular technique-based diagnostics in improving antibacterial therapy and COVID-19 prognosis remain to be evaluated.
| S-EPMC8900889 | biostudies-literature
Project description:BackgroundExcessive inflammation contributes to the morbidity and mortality of severe coronavirus disease 2019 (COVID-19) pneumonia. Recombinant human plasma gelsolin (rhu-pGSN) improves disease outcomes in diverse experimental models of infectious and noninfectious inflammation.MethodsIn a blinded, randomized study, 61 subjects with documented COVID-19 pneumonia having a World Health Organization (WHO) Severity Score of 4 to 6 and evidence of a hyperinflammatory state were treated with standard care and either adjunctive rhu-pGSN 12 mg/kg or an equal volume of saline placebo given intravenously at entry, 12 hours, and 36 hours. The prespecified coprimary outcomes were survival without major respiratory, hemodynamic, or renal support on Day 14 and the incidence of serious adverse events (SAEs) during the 90-day study period.ResultsAll subjects receiving ≥1 dose of study drug were analyzed. Fifty-four of 61 subjects (88.5%) were WHO severity level 4 at entry. The proportions of subjects alive without support on Day 14 were 25 of 30 rhu-pGSN recipients (83.3%) and 27 of 31 placebo recipients (87.1%). Over the duration of the study, WHO Severity Scores improved similarly in both treatment groups. No statistically significant differences were observed between treatment groups at any time point examined. Two subjects died in each group. Numerically fewer subjects in the rhu-pGSN group had SAEs (5 subjects; 16.7%) or ≥ Grade 3 adverse events (5 subjects; 16.7%) than in the placebo group (8 subjects [25.8%] and 9 subjects [29.0%], respectively), mostly involving the lungs. Three rhu-pGSN recipients (10.0%) were intubated compared to 6 placebo recipients (19.4%).ConclusionsOverall, subjects in this study did well irrespective of treatment arm. When added to dexamethasone and remdesivir, no definitive benefit was demonstrated for rhu-pGSN relative to placebo. Safety signals were not identified after the administration of 3 doses of 12 mg/kg rhu-pGSN over 36 hours. The frequencies of SAEs and intubation were numerically fewer in the rhu-pGSN group compared with placebo.
| S-EPMC9345409 | biostudies-literature
Project description:BackgroundSegmentation of coronavirus disease 2019 (COVID-19) lesions is a difficult task due to high uncertainty in the shape, size and location of the lesions. CT scan image is an important means of diagnosing COVID-19, but it requires doctors to observe a large number of scan images repeatedly to determine the patient's condition. Moreover, the low contrast of CT scan and the presence of tissues such as blood vessels in the background increase the difficulty of diagnosis. To solve this problem, we proposed an improved segmentation model called the residual attention U-shaped network (ResAU-Net).MethodsA novel method to detect and segment coronavirus pneumonia was established based on the deep-learning algorithm. Firstly, the CT scan image was input, and lung segmentation was then realized by U-net. Then, the region of interest was selected by the minimum circumscribed rectangle clipping method. Finally, the proposed ResAU-Net, which includes attention module (AMB), residual module (RBM) and sub-pixel convolution module (SPCBM), was used to segment the infected area and generate the segmentation results.ResultsWe evaluated our model using cross-validation on 100 chest CT scans test images. The experimental results showed that our method achieved start-of-the-art performance on the pneumonia dataset. The mIoU and Dice cofficients of Lesion segmentation were 73.40%±2.24% and 84.5%±2.46%, and realize fast real-time processing.ConclusionsOur model can effectively solve the problems of poor segmentation accuracy in the segmentation of COVID-19 lesions, and the segmentation result image can effectively assist medical staff in the diagnosis and quantitative analysis of infection degree, and improve the screening and diagnosis efficiency of pneumonia.
| S-EPMC8263886 | biostudies-literature