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1.
J Transl Med ; 19(1): 29, 2021 01 07.
Artigo em Inglês | MEDLINE | ID: mdl-33413480

RESUMO

BACKGROUND: Limited data was available for rapid and accurate detection of COVID-19 using CT-based machine learning model. This study aimed to investigate the value of chest CT radiomics for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS), and develop an open-source diagnostic tool with the constructed radiomics model. METHODS: This study enrolled 115 laboratory-confirmed COVID-19 and 435 non-COVID-19 pneumonia patients (training dataset, n = 379; validation dataset, n = 131; testing dataset, n = 40). Key radiomics features extracted from chest CT images were selected to build a radiomics signature using least absolute shrinkage and selection operator (LASSO) regression. Clinical and clinico-radiomics combined models were constructed. The combined model was further validated in the viral pneumonia cohort, and compared with performance of two radiologists using CO-RADS. The diagnostic performance was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). RESULTS: Eight radiomics features and 5 clinical variables were selected to construct the combined radiomics model, which outperformed the clinical model in diagnosing COVID-19 pneumonia with an area under the ROC (AUC) of 0.98 and good calibration in the validation cohort. The combined model also performed better in distinguishing COVID-19 from other viral pneumonia with an AUC of 0.93 compared with 0.75 (P = 0.03) for clinical model, and 0.69 (P = 0.008) or 0.82 (P = 0.15) for two trained radiologists using CO-RADS. The sensitivity and specificity of the combined model can be achieved to 0.85 and 0.90. The DCA confirmed the clinical utility of the combined model. An easy-to-use open-source diagnostic tool was developed using the combined model. CONCLUSIONS: The combined radiomics model outperformed clinical model and CO-RADS for diagnosing COVID-19 pneumonia, which can facilitate more rapid and accurate detection.


Assuntos
/métodos , /diagnóstico , Pneumonia Viral/diagnóstico por imagem , Pneumonia Viral/diagnóstico , Tomografia Computadorizada por Raios X/métodos , Adulto , Idoso , /estatística & dados numéricos , China/epidemiologia , Feminino , Ensaios de Triagem em Larga Escala/métodos , Ensaios de Triagem em Larga Escala/estatística & dados numéricos , Humanos , Aprendizado de Máquina , Masculino , Pessoa de Meia-Idade , Modelos Estatísticos , Nomogramas , Pandemias , Pneumonia Viral/epidemiologia , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Interpretação de Imagem Radiográfica Assistida por Computador/estatística & dados numéricos , Estudos Retrospectivos , Sensibilidade e Especificidade , Tomografia Computadorizada por Raios X/estatística & dados numéricos , Pesquisa Médica Translacional
2.
J Med Internet Res ; 23(1): e25535, 2021 01 06.
Artigo em Inglês | MEDLINE | ID: mdl-33404516

RESUMO

BACKGROUND: Effectively identifying patients with COVID-19 using nonpolymerase chain reaction biomedical data is critical for achieving optimal clinical outcomes. Currently, there is a lack of comprehensive understanding in various biomedical features and appropriate analytical approaches for enabling the early detection and effective diagnosis of patients with COVID-19. OBJECTIVE: We aimed to combine low-dimensional clinical and lab testing data, as well as high-dimensional computed tomography (CT) imaging data, to accurately differentiate between healthy individuals, patients with COVID-19, and patients with non-COVID viral pneumonia, especially at the early stage of infection. METHODS: In this study, we recruited 214 patients with nonsevere COVID-19, 148 patients with severe COVID-19, 198 noninfected healthy participants, and 129 patients with non-COVID viral pneumonia. The participants' clinical information (ie, 23 features), lab testing results (ie, 10 features), and CT scans upon admission were acquired and used as 3 input feature modalities. To enable the late fusion of multimodal features, we constructed a deep learning model to extract a 10-feature high-level representation of CT scans. We then developed 3 machine learning models (ie, k-nearest neighbor, random forest, and support vector machine models) based on the combined 43 features from all 3 modalities to differentiate between the following 4 classes: nonsevere, severe, healthy, and viral pneumonia. RESULTS: Multimodal features provided substantial performance gain from the use of any single feature modality. All 3 machine learning models had high overall prediction accuracy (95.4%-97.7%) and high class-specific prediction accuracy (90.6%-99.9%). CONCLUSIONS: Compared to the existing binary classification benchmarks that are often focused on single-feature modality, this study's hybrid deep learning-machine learning framework provided a novel and effective breakthrough for clinical applications. Our findings, which come from a relatively large sample size, and analytical workflow will supplement and assist with clinical decision support for current COVID-19 diagnostic methods and other clinical applications with high-dimensional multimodal biomedical features.


Assuntos
/diagnóstico , Sistemas de Apoio a Decisões Clínicas , Saúde , Aprendizado de Máquina , Pneumonia Viral/diagnóstico , /diagnóstico por imagem , Diagnóstico Diferencial , Humanos , Pessoa de Meia-Idade , Pneumonia Viral/diagnóstico por imagem , Máquina de Vetores de Suporte , Tomografia Computadorizada por Raios X
3.
J Clin Ultrasound ; 49(2): 85-90, 2021 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-33188533

RESUMO

PURPOSE: To describe our experience concerning lung ultrasound (LUS) in the pediatric emergency clinic, and to investigate the diagnostic value of LUS in coronavirus disease-2019 (COVID-19). METHODS: Patients aged under 18 admitted to the pediatric emergency clinic with suspicion of COVID-19, who underwent point-of-care LUS and from whom COVID-19 reverse transcription polymerase chain reaction (RT-PCR) samples were collected, were included in the study. RESULTS: Point-of-care LUS was performed on 74 patients in the emergency room. LUS findings were more sensitive than chest X-ray in the early stages of the disease and in mild cases. Involvement was observed at LUS despite RT-PCR being negative in some symptomatic patients with a COVID-19 contact history. CONCLUSIONS: We think that LUS can be beneficial in terms of identifying patients with lung involvement and staging their severity in this new disease in pediatric emergency clinics. The procedure is noninvasive, rapid, reproducible, and low cost, involving simple sterilization. Based on the current literature and our own practical experience, we think that increased use of point-of-care LUS can protect patients from unnecessary radiation and treatment delays during the COVID-19 pandemic.


Assuntos
/diagnóstico por imagem , Pulmão/diagnóstico por imagem , Pneumonia Viral/diagnóstico por imagem , Sistemas Automatizados de Assistência Junto ao Leito , Ultrassonografia/métodos , Adolescente , Instituições de Assistência Ambulatorial , Criança , Serviço Hospitalar de Emergência , Feminino , Humanos , Masculino , Radiografia Torácica , Estudos Retrospectivos
4.
J Thorac Imaging ; 36(1): 31-36, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-33003105

RESUMO

BACKGROUND: An expert consensus recently proposed a standardized coronavirus disease 2019 (COVID-19) reporting language for computed tomography (CT) findings of COVID-19 pneumonia. PURPOSE: The purpose of the study was to evaluate the performance of CT in differentiating COVID-19 from other viral infections using a standardized reporting classification. METHODS: A total of 175 consecutive patients were retrospectively identified from a single tertiary-care medical center from March 15 to March 24, 2020, including 87 with positive reverse transcription-polymerase chain reaction (RT-PCR) test for COVID-19 and 88 with negative COVID-19 RT-PCR test, but positive respiratory pathogen panel. Two thoracic radiologists, who were blinded to RT-PCR and respiratory pathogen panel results, reviewed chest CT images independently and classified the imaging findings under 4 categories: "typical" appearance, "indeterminate," "atypical," and "negative" for pneumonia. The final classification was based on consensus between the readers. RESULTS: Patients with COVID-19 were older than patients with other viral infections (P=0.038). The inter-rater agreement of CT categories between the readers ranged from good to excellent, κ=0.80 (0.73 to 0.87). Final CT categories were statistically different among COVID-19 and non-COVID-19 groups (P<0.001). CT "typical" appearance was more prevalent in the COVID-19 group (64/87, 73.6%) than in the non-COVID-19 group (2/88, 2.3%). When considering CT "typical" appearance as a positive test, a sensitivity of 73.6% (95% confidence interval [CI]: 63%-82.4%), specificity of 97.7% (95% CI: 92%-99.7%), positive predictive value of 97% (95% CI: 89.5%-99.6%), and negative predictive value of 78.9% (95% CI: 70%-86.1%) were observed. CONCLUSION: The standardized chest CT classification demonstrated high specificity and positive predictive value in differentiating COVID-19 from other viral infections when presenting a "typical" appearance in a high pretest probability environment. Good to excellent inter-rater agreement was found regarding the CT standardized categories between the readers.


Assuntos
/diagnóstico por imagem , Pneumonia Viral/diagnóstico por imagem , Pneumonia Viral/virologia , Tomografia Computadorizada por Raios X/métodos , Adulto , Comorbidade , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Pandemias , Valor Preditivo dos Testes , Radiografia Torácica , Reprodutibilidade dos Testes , Estudos Retrospectivos , Sensibilidade e Especificidade
5.
J Thorac Imaging ; 36(1): 24-30, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-33075008

RESUMO

Filtering through the plethora of radiologic studies generated in response to the coronavirus disease of 2019 (COVID-19) pandemic can be time consuming and impractical for practicing thoracic radiologists with busy clinical schedules. To further complicate matters, several of the imaging findings in the pediatric patients differ from the adult population. This article is designed to highlight clinically useful information regarding the imaging manifestations of pediatric COVID-19 pneumonia, including findings more unique to pediatric patients, and multisystem inflammatory syndrome in children.


Assuntos
/diagnóstico por imagem , Pneumonia Viral/diagnóstico por imagem , Síndrome de Resposta Inflamatória Sistêmica/diagnóstico por imagem , Criança , Diagnóstico Diferencial , Humanos
6.
J Thorac Imaging ; 36(1): W1-W10, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-32852419

RESUMO

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the current outbreak of Coronavirus disease 2019 (COVID-19). Although imaging should not be used for first-line screening or diagnosis, radiologists need to be aware of its imaging features, and those of common conditions that may mimic COVID-19 pneumonia. In this Pictorial Essay, we review frequently encountered conditions with imaging features that overlap with those that are typical of COVID-19 (including other viral pneumonias, chronic eosinophilic pneumonia, and organizing pneumonia), and those with features that are indeterminate for COVID-19 (including hypersensitivity pneumonitis, pneumocystis pneumonia, diffuse alveolar hemorrhage, pulmonary edema, and pulmonary alveolar proteinosis).


Assuntos
/diagnóstico por imagem , Pneumonia Viral/diagnóstico por imagem , Pneumonia Viral/virologia , Tomografia Computadorizada por Raios X , Diagnóstico Diferencial , Humanos , Pandemias
7.
Clin Imaging ; 69: 261-265, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-33002753

RESUMO

RATIONALE AND OBJECTIVES: There is a rising onus on understanding the common features of COVID-19 pneumonia on different imaging modalities. In this study, we aimed to review and depict the common MRI features of COVID-19 pneumonia in our laboratory confirmed case series, the first comprehensive reported cohort in the literature. MATERIALS AND METHODS: Upon IRB approval, eight laboratory confirmed COVID-19 patients who presented to our outpatient imaging clinic underwent chest CT and, once various features of COVID-19 pneumonia were identified, a dedicated multisequence chest MRI was performed on the same day with an institutional protocol. Demographic data and the morphology, laterality and location of the lesions were recorded for each case. RESULTS: Five males and three females with the mean age of 40.63 ± 12.64 years old were present in this case series. Five cases had typical CT features with ground glass opacities and consolidations, readily visible on different MRI sequences. Three cases had indeterminate or atypical features which were also easily seen on MRI. The comprehensive review of MRI features for each case and representative images have been illustrated. CONCLUSION: Becoming familiar with typical findings of COVID-19 pneumonia in MRI is crucial for every radiologist. Although MRI is not the modality of choice for evaluation of pulmonary opacities, it has similar capabilities in detection of COVID-19 pneumonia when compared to chest CT.


Assuntos
Betacoronavirus , Infecções por Coronavirus , Pneumonia Viral , Adulto , Infecções por Coronavirus/epidemiologia , Feminino , Humanos , Pulmão/diagnóstico por imagem , Imagem por Ressonância Magnética , Masculino , Pessoa de Meia-Idade , Pandemias , Pneumonia Viral/diagnóstico por imagem , Pneumonia Viral/epidemiologia
8.
Clin Imaging ; 69: 266-268, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-33010733

RESUMO

Pregnant women with 2019 novel coronavirus disease (COVID-19) pneumonia are a special group of patients in the pandemic. We report a case of pregnant woman with COVID-19 pneumonia in the second trimester. Clinical and imaging features of the patient were similar to that reported in the literatures for both perinatal patients and non-pregnant patients.


Assuntos
Betacoronavirus , Coronavirus , Pneumonia Viral , Complicações Infecciosas na Gravidez , Feminino , Humanos , Pandemias , Pneumonia Viral/diagnóstico por imagem , Pneumonia Viral/epidemiologia , Gravidez , Complicações Infecciosas na Gravidez/diagnóstico por imagem , Complicações Infecciosas na Gravidez/epidemiologia , Segundo Trimestre da Gravidez , Gestantes , Tórax , Tomografia Computadorizada por Raios X
9.
Med Image Anal ; 67: 101860, 2021 01.
Artigo em Inglês | MEDLINE | ID: mdl-33171345

RESUMO

Coronavirus disease 2019 (COVID-19) emerged in 2019 and disseminated around the world rapidly. Computed tomography (CT) imaging has been proven to be an important tool for screening, disease quantification and staging. The latter is of extreme importance for organizational anticipation (availability of intensive care unit beds, patient management planning) as well as to accelerate drug development through rapid, reproducible and quantified assessment of treatment response. Even if currently there are no specific guidelines for the staging of the patients, CT together with some clinical and biological biomarkers are used. In this study, we collected a multi-center cohort and we investigated the use of medical imaging and artificial intelligence for disease quantification, staging and outcome prediction. Our approach relies on automatic deep learning-based disease quantification using an ensemble of architectures, and a data-driven consensus for the staging and outcome prediction of the patients fusing imaging biomarkers with clinical and biological attributes. Highly promising results on multiple external/independent evaluation cohorts as well as comparisons with expert human readers demonstrate the potentials of our approach.


Assuntos
Inteligência Artificial , Pneumonia Viral/diagnóstico por imagem , Biomarcadores/análise , Progressão da Doença , Humanos , Redes Neurais de Computação , Prognóstico , Interpretação de Imagem Radiográfica Assistida por Computador , Triagem
10.
AJR Am J Roentgenol ; 216(1): 71-79, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-32755175

RESUMO

OBJECTIVE. The purpose of this study was to investigate differences in CT manifestations of coronavirus disease (COVID-19) pneumonia and those of influenza virus pneumonia. MATERIALS AND METHODS. We conducted a retrospective study of 52 patients with COVID-19 pneumonia and 45 patients with influenza virus pneumonia. All patients had positive results for the respective viruses from nucleic acid testing and had complete clinical data and CT images. CT findings of pulmonary inflammation, CT score, and length of largest lesion were evaluated in all patients. Mean density, volume, and mass of lesions were further calculated using artificial intelligence software. CT findings and clinical data were evaluated. RESULTS. Between the group of patients with COVID-19 pneumonia and the group of patients with influenza virus pneumonia, the largest lesion close to the pleura (i.e., no pulmonary parenchyma between the lesion and the pleura), mucoid impaction, presence of pleural effusion, and axial distribution showed statistical difference (p < 0.05). The properties of the largest lesion, presence of ground-glass opacity, presence of consolidation, mosaic attenuation, bronchial wall thickening, centrilobular nodules, interlobular septal thickening, crazy paving pattern, air bronchogram, unilateral or bilateral distribution, and longitudinal distribution did not show significant differences (p > 0.05). In addition, no significant difference was seen in CT score, length of the largest lesion, mean density, volume, or mass of the lesions between the two groups (p > 0.05). CONCLUSION. Most lesions in patients with COVID-19 pneumonia were located in the peripheral zone and close to the pleura, whereas influenza virus pneumonia was more prone to show mucoid impaction and pleural effusion. However, differentiating between COVID-19 pneumonia and influenza virus pneumonia in clinical practice remains difficult.


Assuntos
/diagnóstico por imagem , Influenza Humana/diagnóstico por imagem , Pneumonia Viral/diagnóstico por imagem , Pneumonia Viral/virologia , Tomografia Computadorizada por Raios X/métodos , Adolescente , Adulto , Idoso , Inteligência Artificial , Diagnóstico Diferencial , Feminino , Humanos , Influenza Humana/virologia , Masculino , Pessoa de Meia-Idade , Interpretação de Imagem Radiográfica Assistida por Computador , Radiografia Torácica , Estudos Retrospectivos
11.
AJR Am J Roentgenol ; 216(1): 80-84, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-32755198

RESUMO

OBJECTIVE. Although chest CT is the standard imaging modality in early diagnosis and management of coronavirus disease (COVID-19), the use of lung ultrasound (US) presents some advantages over the use of chest CT and may play a complementary role in the workup of COVID-19. The objective of our study was to investigate US findings in patients with COVID-19 and the relationship of the US findings with the duration of symptoms and disease severity. MATERIALS AND METHODS. From March 3, 2020, to March 30, 2020, consecutive patients with a positive reverse transcriptase polymerase chain reaction test result for the virus that causes COVID-19 were enrolled in this study. Lung US was performed, and the imaging features were analyzed. The Fisher exact test was used to compare the percentages of patients with each US finding between groups with different symptom durations and disease severity. RESULTS. Our study population comprised 28 patients (14 men and 14 women; mean age ± SD, 59.8 ± 18.3 years; age range, 21-92 years). All 28 patients (100.0%, 28/28) had positive lung US findings. The most common findings were the following: B-lines (100.0%, 28/28), consolidation (67.9%, 19/28), and a thickened pleural line (60.7%, 17/28). A thickened pleural line was observed in a higher percentage of patients with a longer duration of the disease than in those with a shorter duration of the disease, and pulmonary consolidations were more common in severe and critical cases than in moderate cases. CONCLUSION. Typical lung US findings in patients with COVID-19 included B-lines, pulmonary consolidation, and a thickened pleural line. In addition, our results indicate that lung US findings can be be used to reflect both the infection duration and disease severity.


Assuntos
/diagnóstico por imagem , Pneumonia Viral/diagnóstico por imagem , Ultrassonografia/métodos , Adulto , Idoso , Idoso de 80 Anos ou mais , China , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Pneumonia Viral/virologia , Índice de Gravidade de Doença
12.
AJR Am J Roentgenol ; 216(1): 264-270, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-32845160

RESUMO

OBJECTIVE. This article presents the perspectives of radiologists in different sub-specialties at three institutions across the United States regarding inpatient imaging of patients confirmed to have coronavirus disease (COVID-19) and persons under investigation (i.e., patients suspected to have COVID-19). CONCLUSION. The COVID-19 pandemic has prompted radiologists to become aware of imaging findings related to the disease and to develop workflows for the imaging of patients with COVID-19 and persons under investigation, to optimize care for all patients and preserve the health of health care workers.


Assuntos
/diagnóstico por imagem , Diagnóstico por Imagem , Pacientes Internados , Pneumonia Viral/diagnóstico por imagem , Adulto , Idoso , Diagnóstico Diferencial , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Pandemias , Pneumonia Viral/epidemiologia , Pneumonia Viral/virologia , Estados Unidos/epidemiologia , Fluxo de Trabalho
14.
Med Image Anal ; 67: 101824, 2021 01.
Artigo em Inglês | MEDLINE | ID: mdl-33091741

RESUMO

With the rapidly worldwide spread of Coronavirus disease (COVID-19), it is of great importance to conduct early diagnosis of COVID-19 and predict the conversion time that patients possibly convert to the severe stage, for designing effective treatment plans and reducing the clinicians' workloads. In this study, we propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time formulated as a classification task, and if yes, the conversion time will be predicted formulated as a classification task. To do this, the proposed method takes into account 1) the weight for each sample to reduce the outliers' influence and explore the problem of imbalance classification, and 2) the weight for each feature via a sparsity regularization term to remove the redundant features of the high-dimensional data and learn the shared information across two tasks, i.e., the classification and the regression. To our knowledge, this study is the first work to jointly predict the disease progression and the conversion time, which could help clinicians to deal with the potential severe cases in time or even save the patients' lives. Experimental analysis was conducted on a real data set from two hospitals with 408 chest computed tomography (CT) scans. Results show that our method achieves the best classification (e.g., 85.91% of accuracy) and regression (e.g., 0.462 of the correlation coefficient) performance, compared to all comparison methods. Moreover, our proposed method yields 76.97% of accuracy for predicting the severe cases, 0.524 of the correlation coefficient, and 0.55 days difference for the conversion time.


Assuntos
/classificação , Pneumonia Viral/classificação , Pneumonia Viral/diagnóstico por imagem , Tomografia Computadorizada por Raios X/métodos , Progressão da Doença , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Valor Preditivo dos Testes , Interpretação de Imagem Radiográfica Assistida por Computador , Radiografia Torácica , Índice de Gravidade de Doença , Fatores de Tempo
15.
Med Image Anal ; 67: 101844, 2021 01.
Artigo em Inglês | MEDLINE | ID: mdl-33091743

RESUMO

While image analysis of chest computed tomography (CT) for COVID-19 diagnosis has been intensively studied, little work has been performed for image-based patient outcome prediction. Management of high-risk patients with early intervention is a key to lower the fatality rate of COVID-19 pneumonia, as a majority of patients recover naturally. Therefore, an accurate prediction of disease progression with baseline imaging at the time of the initial presentation can help in patient management. In lieu of only size and volume information of pulmonary abnormalities and features through deep learning based image segmentation, here we combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit (ICU) admission. To our knowledge, this is the first study that uses holistic information of a patient including both imaging and non-imaging data for outcome prediction. The proposed methods were thoroughly evaluated on datasets separately collected from three hospitals, one in the United States, one in Iran, and another in Italy, with a total 295 patients with reverse transcription polymerase chain reaction (RT-PCR) assay positive COVID-19 pneumonia. Our experimental results demonstrate that adding non-imaging features can significantly improve the performance of prediction to achieve AUC up to 0.884 and sensitivity as high as 96.1%, which can be valuable to provide clinical decision support in managing COVID-19 patients. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia. The source code of our work is available at https://github.com/DIAL-RPI/COVID19-ICUPrediction.


Assuntos
/diagnóstico por imagem , Unidades de Terapia Intensiva/estatística & dados numéricos , Admissão do Paciente/estatística & dados numéricos , Pneumonia Viral/diagnóstico por imagem , Adulto , Idoso , Conjuntos de Dados como Assunto , Progressão da Doença , Feminino , Humanos , Irã (Geográfico)/epidemiologia , Itália/epidemiologia , Masculino , Pessoa de Meia-Idade , Valor Preditivo dos Testes , Prognóstico , Estados Unidos/epidemiologia
16.
Med Image Anal ; 67: 101836, 2021 01.
Artigo em Inglês | MEDLINE | ID: mdl-33129141

RESUMO

The recent global outbreak and spread of coronavirus disease (COVID-19) makes it an imperative to develop accurate and efficient diagnostic tools for the disease as medical resources are getting increasingly constrained. Artificial intelligence (AI)-aided tools have exhibited desirable potential; for example, chest computed tomography (CT) has been demonstrated to play a major role in the diagnosis and evaluation of COVID-19. However, developing a CT-based AI diagnostic system for the disease detection has faced considerable challenges, which is mainly due to the lack of adequate manually-delineated samples for training, as well as the requirement of sufficient sensitivity to subtle lesions in the early infection stages. In this study, we developed a dual-branch combination network (DCN) for COVID-19 diagnosis that can simultaneously achieve individual-level classification and lesion segmentation. To focus the classification branch more intensively on the lesion areas, a novel lesion attention module was developed to integrate the intermediate segmentation results. Furthermore, to manage the potential influence of different imaging parameters from individual facilities, a slice probability mapping method was proposed to learn the transformation from slice-level to individual-level classification. We conducted experiments on a large dataset of 1202 subjects from ten institutes in China. The results demonstrated that 1) the proposed DCN attained a classification accuracy of 96.74% on the internal dataset and 92.87% on the external validation dataset, thereby outperforming other models; 2) DCN obtained comparable performance with fewer samples and exhibited higher sensitivity, especially in subtle lesion detection; and 3) DCN provided good interpretability on the loci of infection compared to other deep models due to its classification guided by high-level semantic information. An online CT-based diagnostic platform for COVID-19 derived from our proposed framework is now available.


Assuntos
/diagnóstico por imagem , Redes Neurais de Computação , Pneumonia Viral/diagnóstico por imagem , Tomografia Computadorizada por Raios X , /classificação , Humanos , Pneumonia Viral/classificação , Radiografia Torácica , Sensibilidade e Especificidade
17.
J Infect Chemother ; 27(1): 70-75, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-32950393

RESUMO

OBJECTIVES: The symptoms of Coronavirus disease 2019 (COVID-19) vary among patients. The aim of this study was to investigate the clinical manifestation and disease duration in young versus elderly patients. METHODS: We retrospectively analyzed 187 patients (87 elderly and 100 young patients) with confirmed COVID-19. The clinical characteristics and chest computed tomography (CT) extent as defined by a score were compared between the two groups. RESULTS: The numbers of asymptomatic cases and severe cases were significantly higher in the elderly group (elderly group vs. young group; asymptomatic cases, 31 [35.6%] vs. 10 [10%], p < 0.0001; severe cases, 25 [28.7%] vs. 8 [8.0%], p = 0.0002). The proportion of asymptomatic patients and severe patients increased across the 10-year age groups. There was no significant difference in the total CT score and number of abnormal cases. A significant positive correlation between the disease duration and patient age was observed in asymptomatic patients (ρ = 0.4570, 95% CI 0.1198-0.6491, p = 0.0034). CONCLUSIONS: Although the extent of lung involvement did not have a significant difference between the young and elderly patients, elderly patients were more likely to have severe clinical manifestations. Elderly patients were also more likely to be asymptomatic and a source of COVID-19 viral shedding.


Assuntos
Infecções Assintomáticas/epidemiologia , Infecções por Coronavirus/epidemiologia , Pneumonia Viral/epidemiologia , Eliminação de Partículas Virais , Adulto , Fatores Etários , Idoso , Betacoronavirus , Infecções por Coronavirus/diagnóstico por imagem , Infecções por Coronavirus/patologia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Pandemias , Pneumonia Viral/diagnóstico por imagem , Pneumonia Viral/patologia , Estudos Retrospectivos , Índice de Gravidade de Doença , Tomografia Computadorizada por Raios X
18.
AJR Am J Roentgenol ; 216(1): 66-70, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-32368928

RESUMO

OBJECTIVE. The purpose of this study was to explore the value of CT in the diagnosis of coronavirus disease (COVID-19) pneumonia, especially for patients who have negative initial results of reverse transcription-polymerase chain reaction (RT-PCR) testing. MATERIALS AND METHODS. Patients with COVID-19 pneumonia from January 19, 2020, to February 20, 2020, were included. All patients underwent chest CT and swab RT-PCR tests within 3 days. Patients were divided into groups with negative (seven patients) and positive (14 patients) initial RT-PCR results. The imaging findings in both groups were recorded and compared. RESULTS. Twenty-one patients with symptoms (nine men, 12 women; age range, 26-90 years) were evaluated. Most of the COVID-19 lesions were located in multiple lobes (67%) in both lungs (72%) in our study. The main CT features were ground-glass opacity (95%) and consolidation (72%) with a subpleural distribution (100%). Otherwise, 33% of patients had other lesions around the bronchovascular bundle. The other CT features included air bronchogram (57%), vascular enlargement (67%), interlobular septal thickening (62%), and pleural effusions (19%). Compared with that in the group with positive initial RT-PCR results, CT of the group with negative initial RT-PCR results was less likely to show pulmonary consolidation (p < 0.05). CONCLUSION. The less pulmonary consolidation found at CT, the greater is the possibility of negative initial RT-PCR results. Chest CT is important in the screening of patients in whom disease is clinically suspected, especially those who have negative initial RT-PCR results.


Assuntos
/diagnóstico por imagem , Pneumonia Viral/diagnóstico por imagem , Tomografia Computadorizada por Raios X/métodos , Adulto , Idoso , Idoso de 80 Anos ou mais , Diagnóstico Diferencial , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Pneumonia Viral/virologia , Radiografia Torácica , Reação em Cadeia da Polimerase Via Transcriptase Reversa , Sensibilidade e Especificidade
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