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1.
PLoS One ; 16(3): e0247686, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33657140

RESUMO

OBJECTIVES: The aim of this study was to investigate possible patterns of demand for chest imaging during the first wave of the SARS-CoV-2 pandemic and derive a decision aid for the allocation of resources in future pandemic challenges. MATERIALS AND METHODS: Time data of requests for patients with suspected or confirmed coronavirus disease 2019 (COVID-19) lung disease were analyzed between February 27th and May 27th 2020. A multinomial logistic regression model was used to evaluate differences in the number of requests between 3 time intervals (I1: 6am - 2pm, I2: 2pm - 10pm, I3: 10pm - 6am). A cosinor model was applied to investigate the demand per hour. Requests per day were compared to the number of regional COVID-19 cases. RESULTS: 551 COVID-19 related chest imagings (32.8% outpatients, 67.2% in-patients) of 243 patients were conducted (33.3% female, 66.7% male, mean age 60 ± 17 years). Most exams for outpatients were required during I2 (I1 vs. I2: odds ratio (OR) = 0.73, 95% confidence interval (CI) 0.62-0.86, p = 0.01; I2 vs. I3: OR = 1.24, 95% CI 1.04-1.48, p = 0.03) with an acrophase at 7:29 pm. Requests for in-patients decreased from I1 to I3 (I1 vs. I2: OR = 1.24, 95% CI 1.09-1.41, p = 0.01; I2 vs. I3: OR = 1.16, 95% CI 1.05-1.28, p = 0.01) with an acrophase at 12:51 pm. The number of requests per day for outpatients developed similarly to regional cases while demand for in-patients increased later and persisted longer. CONCLUSIONS: The demand for COVID-19 related chest imaging displayed distinct distribution patterns depending on the sector of patient care and point of time during the SARS-CoV-2 pandemic. These patterns should be considered in the allocation of resources in future pandemic challenges with similar disease characteristics.


Assuntos
/diagnóstico por imagem , Diagnóstico por Imagem/tendências , Tórax/diagnóstico por imagem , Adulto , Idoso , Testes Diagnósticos de Rotina/tendências , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Modelos Teóricos , Pandemias , Projetos Piloto , Tórax/virologia
2.
PLoS One ; 16(3): e0248957, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33755708

RESUMO

The characteristics and evolution of pulmonary fibrosis in patients with coronavirus disease 2019 (COVID-19) have not been adequately studied. AI-assisted chest high-resolution computed tomography (HRCT) was used to investigate the proportion of COVID-19 patients with pulmonary fibrosis, the relationship between the degree of fibrosis and the clinical classification of COVID-19, the characteristics of and risk factors for pulmonary fibrosis, and the evolution of pulmonary fibrosis after discharge. The incidence of pulmonary fibrosis in patients with severe or critical COVID-19 was significantly higher than that in patients with moderate COVID-19. There were significant differences in the degree of pulmonary inflammation and the extent of the affected area among patients with mild, moderate and severe pulmonary fibrosis. The IL-6 level in the acute stage and albumin level were independent risk factors for pulmonary fibrosis. Ground-glass opacities, linear opacities, interlobular septal thickening, reticulation, honeycombing, bronchiectasis and the extent of the affected area were significantly improved 30, 60 and 90 days after discharge compared with at discharge. The more severe the clinical classification of COVID-19, the more severe the residual pulmonary fibrosis was; however, in most patients, pulmonary fibrosis was improved or even resolved within 90 days after discharge.


Assuntos
Inteligência Artificial , Fibrose Pulmonar/diagnóstico , Tórax/diagnóstico por imagem , /complicações , Feminino , Humanos , Processamento de Imagem Assistida por Computador , Interleucina-6/metabolismo , Masculino , Pessoa de Meia-Idade , Alta do Paciente , Fibrose Pulmonar/etiologia , Fatores de Risco , Índice de Gravidade de Doença , Tomografia Computadorizada por Raios X
3.
Mol Med Rep ; 23(5)2021 May.
Artigo em Inglês | MEDLINE | ID: mdl-33760212

RESUMO

Although the COVID­19 epidemic has lasted for months, it has not yet been successfully controlled, and little is known about neonatal COVID­19. Therefore, literature search was conducted for references in PubMed, Science Direct, ProQuest, Web of Science and China National Knowledge Infrastructure for detailed case reports on neonatal COVID­19 published as of July 15, 2020, to facilitate the clinical treatment, epidemic prevention and control of neonatal COVID­19. Forty nonoverlapping case reports focusing mainly on the demographic characteristics, transmission modes, clinical features, treatments and prognosis of neonatal COVID­19, including 3 in Chinese and 37 in English, were available.


Assuntos
/patologia , /fisiologia , Anticorpos Antivirais/análise , Antivirais/uso terapêutico , Doenças Assintomáticas , /transmissão , Humanos , Recém-Nascido , Leite Humano/virologia , RNA Viral/metabolismo , /imunologia , Tórax/diagnóstico por imagem
4.
Environ Health Prev Med ; 26(1): 35, 2021 Mar 20.
Artigo em Inglês | MEDLINE | ID: mdl-33743595

RESUMO

BACKGROUND: Body mass-independent parameters might be more appropriate for assessing cardiometabolic abnormalities than weight-dependent indices in Asians who have relatively high visceral adiposity but low body fat. Dual-energy X-ray absorptiometry (DXA)-measured trunk-to-peripheral fat ratio is one such body mass-independent index. However, there are no reports on relationships between DXA-measured regional fat ratio and cardiometabolic risk factors targeting elderly Asian men. METHODS: We analyzed cross-sectional data of 597 elderly men who participated in the baseline survey of the Fujiwara-kyo Osteoporosis Risk in Men (FORMEN) study, a community-based single-center prospective cohort study conducted in Japan. Whole-body fat and regional fat were measured with a DXA scanner. Trunk-to-appendicular fat ratio (TAR) was calculated as trunk fat divided by appendicular fat (sum of arm and leg fat), and trunk-to-leg fat ratio (TLR) as trunk fat divided by leg fat. RESULTS: Both TAR and TLR in the group of men who used ≥ 1 medication for hypertension, dyslipidemia, or diabetes ("user group"; N = 347) were significantly larger than those who did not use such medication ("non-user group"; N = 250) (P < 0.05). After adjusting for potential confounding factors including whole-body fat, both TAR and TLR were significantly associated with low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglyceride, fasting serum insulin, and the insulin resistance index in the non-user group and non-overweight men in the non-user group (N = 199). CONCLUSION: The trunk-to-peripheral fat ratio was associated with cardiometabolic risk factors independently of whole-body fat mass. Parameters of the fat ratio may be useful for assessing cardiometabolic risk factors, particularly in underweight to normal-weight populations.


Assuntos
Adiposidade/fisiologia , Gordura Intra-Abdominal , Absorciometria de Fóton , Idoso , Idoso de 80 Anos ou mais , Biomarcadores/metabolismo , Estudos Transversais , Humanos , Gordura Intra-Abdominal/anatomia & histologia , Gordura Intra-Abdominal/diagnóstico por imagem , Japão , Masculino , Osteoporose/etiologia , Estudos Prospectivos , Medição de Risco , Fatores de Risco , Tórax/anatomia & histologia , Tórax/diagnóstico por imagem
5.
Sci Rep ; 11(1): 6422, 2021 03 19.
Artigo em Inglês | MEDLINE | ID: mdl-33742041

RESUMO

Coronavirus disease 2019 (COVID-19) has spread in more than 100 countries and regions around the world, raising grave global concerns. COVID-19 has a similar pattern of infection, clinical symptoms, and chest imaging findings to influenza pneumonia. In this retrospective study, we analysed clinical and chest CT data of 24 patients with COVID-19 and 79 patients with influenza pneumonia. Univariate analysis demonstrated that the temperature, systolic pressure, cough and sputum production could distinguish COVID-19 from influenza pneumonia. The diagnostic sensitivity and specificity for the clinical features are 0.783 and 0.747, and the AUC value is 0.819. Univariate analysis demonstrates that nine CT features, central-peripheral distribution, superior-inferior distribution, anterior-posterior distribution, patches of GGO, GGO nodule, vascular enlargement in GGO, air bronchogram, bronchiectasis within focus, interlobular septal thickening, could distinguish COVID-19 from influenza pneumonia. The diagnostic sensitivity and specificity for the CT features are 0.750 and 0.962, and the AUC value is 0.927. Finally, a multivariate logistic regression model combined the variables from the clinical variables and CT features models was made. The combined model contained six features: systolic blood pressure, sputum production, vascular enlargement in the GGO, GGO nodule, central-peripheral distribution and bronchiectasis within focus. The diagnostic sensitivity and specificity for the combined features are 0.87 and 0.96, and the AUC value is 0.961. In conclusion, some CT features or clinical variables can differentiate COVID-19 from influenza pneumonia. Moreover, CT features combined with clinical variables had higher diagnostic performance.


Assuntos
/diagnóstico , Influenza Humana/diagnóstico , Pneumonia Viral/diagnóstico , Adulto , Diagnóstico Diferencial , Feminino , Humanos , Influenza Humana/diagnóstico por imagem , Masculino , Pessoa de Meia-Idade , Pneumonia Viral/diagnóstico por imagem , Estudos Retrospectivos , Tórax/diagnóstico por imagem , Tomografia Computadorizada por Raios X , Adulto Jovem
6.
Medicine (Baltimore) ; 100(9): e24760, 2021 Mar 05.
Artigo em Inglês | MEDLINE | ID: mdl-33655939

RESUMO

ABSTRACT: Radiation overexposure is common in chest X-ray (CXRs) of pediatric patients. However, overexposure may reveal incidental findings that can help to guide patient management or warrant quality improvement.To assess the prevalence of overexposure in CXRs in pediatric intensive care unit (PICU); and identify the incidental findings within overexposed areas, we conducted a retrospective cohort study of children who were admitted to PICU. Two independent evaluators reviewed patient's charts and digital CXRs according to the American College of Radiology standards; to evaluate overexposure of the anatomical parameters and incidental findings.A total of 400 CXRs of 85 patients were reviewed. The mean number of CXRs per patient was 4.7. Almost all (99.75%) CXRs met the criteria for overexposure, with the most common being upper abdomen (99.2%), upper limbs (97%) and neck (95.7%). In addition, 43% of these X-rays were cropped by the radiology technician to appear within the requested perimeter. There was a significant association between field cropping and overexposure (t-test: t = 9.8, P < .001). Incidental findings were seen in 41.5% of the radiographs; with the most common being gaseous abdominal distension (73.1%), low-positioned nasogastric tube (24.6%), and constipation (10.3%).Anatomical overexposure in routine CXRs remains high and raises a concern in PICU practice. Appropriate collimation of the X-ray beam, rather than electronically cropping the image, is highly recommended to minimize hiding incidental findings in the cropped-out areas. Redefining the anatomic boundaries of CXR in critically ill infants and children may need further studies and consideration. Quality improvement initiatives to minimize radiation overexposure in PICU are recommended, especially in younger children and those with more severe illness upon PICU admission.


Assuntos
Unidades de Terapia Intensiva Pediátrica/estatística & dados numéricos , Erros Médicos/estatística & dados numéricos , Exposição à Radiação/análise , Lesões por Radiação/epidemiologia , Radiografia Torácica/efeitos adversos , Adolescente , Criança , Pré-Escolar , Feminino , Humanos , Achados Incidentais , Lactente , Recém-Nascido , Masculino , Prevalência , Melhoria de Qualidade , Estudos Retrospectivos , Tórax/diagnóstico por imagem
7.
Sci Rep ; 11(1): 5975, 2021 03 16.
Artigo em Inglês | MEDLINE | ID: mdl-33727641

RESUMO

Since the emergence of SARS-CoV-2, numerous studies have been attempting to determine biomarkers, which could rapidly and efficiently predict COVID-19 severity, however there is lack of consensus on a specific one. This retrospective cohort study is a comprehensive analysis of the initial symptoms, comorbidities and laboratory evaluation of patients, diagnosed with COVID-19 in Huoshenshan Hospital, Wuhan, from 4th February to 12th March, 2020. Based on the data collected from 63 severely ill patients from the onset of symptoms till the full recovery or demise, we found not only age (average 70) but also blood indicators as significant risk factors associated with multiple organ failure. The blood indices of all patients showed hepatic, renal, cardiac and hematopoietic dysfunction with imbalanced coagulatory biomarkers. We noticed that the levels of LDH (85%, P < .001), HBDH (76%, P < .001) and CRP (65%, P < .001) were significantly elevated in deceased patients, indicating hepatic impairment. Similarly, increased CK (15%, P = .002), Cre (37%, P = 0.102) and CysC (74%, P = 0.384) indicated renal damage. Cardiac injury was obvious from the significantly elevated level of Myoglobin (52%, P < .01), Troponin-I (65%, P = 0.273) and BNP (50%, P = .787). SARS-CoV-2 disturbs the hemolymphatic system as WBC# (73%, P = .002) and NEUT# (78%, P < .001) were significantly elevated in deceased patients. Likewise, the level of D-dimer (80%, P < .171), PT (87%, P = .031) and TT (57%, P = .053) was elevated, indicating coagulatory imbalances. We identified myoglobin and CRP as specific risk factors related to mortality and highly correlated to organ failure in COVID-19 disease.


Assuntos
Proteína C-Reativa/análise , Mioglobina/análise , Idoso , Idoso de 80 Anos ou mais , Biomarcadores/sangue , /mortalidade , Comorbidade , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Insuficiência de Múltiplos Órgãos/etiologia , Estudos Retrospectivos , Fatores de Risco , Índice de Gravidade de Doença , Análise de Sobrevida , Tórax/diagnóstico por imagem , Tomografia Computadorizada por Raios X , Troponina I/sangue
8.
Interdiscip Sci ; 13(1): 73-82, 2021 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-33565027

RESUMO

Corona Virus Disease (COVID-19) has spread globally quickly, and has resulted in a large number of causalities and medical resources insufficiency in many countries. Reverse-transcriptase polymerase chain reaction (RT-PCR) testing is adopted as biopsy tool for confirmation of virus infection. However, its accuracy is as low as 60-70%, which is inefficient to uncover the infected. In comparison, the chest CT has been considered as the prior choice in diagnosis and monitoring progress of COVID-19 infection. Although the COVID-19 diagnostic systems based on artificial intelligence have been developed for assisting doctors in diagnosis, the small sample size and the excessive time consumption limit their applications. To this end, this paper proposed a diagnosis prototype system for COVID-19 infection testing. The proposed deep learning model is trained and is tested on 2267 CT sequences from 1357 patients clinically confirmed with COVID-19 and 1235 CT sequences from non-infected people. The main highlights of the prototype system are: (1) no data augmentation is needed to accurately discriminate the COVID-19 from normal controls with the specificity of 0.92 and sensitivity of 0.93; (2) the raw DICOM image is not necessary in testing. Highly compressed image like Jpeg can be used to allow a quick diagnosis; and (3) it discriminates the virus infection within 6 seconds and thus allows an online test with light cost. We also applied our model on 48 asymptomatic patients diagnosed with COVID-19. We found that: (1) the positive rate of RT-PCR assay is 63.5% (687/1082). (2) 45.8% (22/48) of the RT-PCR assay is negative for asymptomatic patients, yet the accuracy of CT scans is 95.8%. The online detection system is available: http://212.64.70.65/covid .


Assuntos
/diagnóstico por imagem , Compressão de Dados , Aprendizado Profundo , Tórax/diagnóstico por imagem , Tomografia Computadorizada por Raios X , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Criança , Pré-Escolar , Estudos de Coortes , Feminino , Humanos , Lactente , Recém-Nascido , Masculino , Pessoa de Meia-Idade , Curva ROC , Reprodutibilidade dos Testes , Adulto Jovem
9.
Medicine (Baltimore) ; 100(3): e23926, 2021 Jan 22.
Artigo em Inglês | MEDLINE | ID: mdl-33545964

RESUMO

ABSTRACT: Mycoplasma pneumoniae infection may induce a systemic hypercoagulable abnormality, like organ embolism and infarction. Indexes of blood coagulation and C-reactive protein (CRP) have been reported different between healthy people and mycoplasma pneumoniae pneumonia (MPP) patients, but this difference in MPP patients with different chest imaging findings has rarely been reported.We performed a retrospective study of 101 children with MPP and 119 controls, combined with radiological examination and blood tests, to compare the blood coagulation and CRP level among MPP children with different chest imaging findings.For the MPP children with different chest imaging findings, there were significant differences in CRP, fibrinogen (FIB) and D-dimer (D-D) levels among subgroups (P = .004, P = .008 and P < .001 respectively). The CRP level in group of interstitial pneumonia was significantly higher than that in groups of bronchopneumonia and hilar shadow thickening (P = .003 and P = .001 respectively). And the FIB and D-D values in group of lung consolidation were significantly higher than that in the other 3 groups (all P < .05). When compared with controls, the white blood cell, CRP, FIB, and D-D levels in MPP children were significantly higher, and the activated partial thromboplastin time and thrombin time levels were significantly lower (all P < .05).Our results showed that CRP level changed most significantly in group of interstitial pneumonia, whereas FIB, D-D levels changed most significantly in the lung consolidation group.


Assuntos
Fatores de Coagulação Sanguínea/análise , Proteína C-Reativa/análise , Pneumonia por Mycoplasma/diagnóstico por imagem , Tórax/diagnóstico por imagem , Adolescente , Criança , Pré-Escolar , Feminino , Produtos de Degradação da Fibrina e do Fibrinogênio/análise , Fibrinogênio/análise , Humanos , Lactente , Masculino , Mycoplasma pneumoniae/patogenicidade , Pneumonia por Mycoplasma/sangue
10.
BMC Infect Dis ; 21(1): 141, 2021 Feb 03.
Artigo em Inglês | MEDLINE | ID: mdl-33535989

RESUMO

BACKGROUND: The impact of COVID-19 has been devastating on a global scale. The negative conversion time (NCT) of SARS-CoV-2 RNA is closely related to clinical manifestation and disease progression in COVID-19 patients. Our study aimed to predict factors associated with prolonged NCT of SARS-CoV-2 RNA in mild/moderate COVID-19 patients. METHODS: The clinical features, laboratory data and treatment outcomes of COVID-19 patients were retrospectively analyzed. Then univariate and multivariate analysis were used to screen out risk factors of influencing prolonged NCT of SARS-CoV-2 RNA. RESULTS: Thirty-two hospitalized mild/moderate COVID-19 patients were enrolled. The general clinical symptoms were cough (78.1%), fever (75%), diarrhea (68.8%), expectoration (56.3%), and nausea (37.5%). More than 40% of the patients had decreased erythrocyte, hemoglobin and leucocyte and 93.8% patients were detected in abnormalities of chest CT. The median NCT of SARS-CoV-2 RNA was 19.5 days (IQR: 14.25-25). Univariate analysis found fever, nausea, diarrhea and abnormalities in chest CTs were positively associated with prolonged NCT of viral RNA (P< 0.05). The multivariate Cox proportional hazard model revealed that fever [Exp (B), 0.284; 95% CI, 0.114-0.707; P<0.05] and nausea [Exp (B), 0.257; 95%CI, 0.096-0.689; P<0.05] were two significant independent factors. CONCLUSIONS: Fever and nausea were two significant independent factors in prolonged NCT of viral RNA in mild/moderate COVID-19 patients, which provided a useful references for disease progression and treatment of COVID-19.


Assuntos
/diagnóstico , RNA Viral/metabolismo , /genética , Adulto , /patologia , Tosse/etiologia , Diarreia/etiologia , Feminino , Febre/etiologia , Hospitalização , Humanos , Masculino , Pessoa de Meia-Idade , Análise Multivariada , Modelos de Riscos Proporcionais , Estudos Retrospectivos , Fatores de Risco , Índice de Gravidade de Doença , Tórax/diagnóstico por imagem , Fatores de Tempo
11.
J Coll Physicians Surg Pak ; 31(1): 14-20, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-33546527

RESUMO

OBJECTIVE:   To identify utility of chest computed tomography severity score (CT-SS) as an additional tool to COVID-19 pneumonia imaging classification in assessing severity of COVID-19. STUDY DESIGN: Descriptive analytical study Place and Duration of Study: Armed Forces Institute of Radiology and Imaging, (AFIRI) Rawalpindi, from April 2020 to June 2020. METHODOLOGY: Five hundred suspected COVID-19 cases referred for high resolution computed tomography - chest were included in the study. Cases were categorised by radiological findings using COVID-19 pneumonia imaging classification, proposed in the radiological society of North America expert consensus statement on reporting chest CT findings related to COVID-19. CT-SS was calculated for all scans. Patients were clinically classified according to disease severity as per 'Diagnosis And Treatment Program of Pneumonia of New Coronavirus Infection' recommended by China's National Health Commission. The relationships between radiological findings, CT-SS, and clinical severity were explored. RESULTS: Based on the radiological findings, 298 cases were graded as typical, 34 as indeterminate, 15 as atypical, and 153 as negative for pneumonia. The apical and posterior basal segments of lower lobes were most commonly involved. The CT-SS showed higher values in patients of severe group as compared to those in moderate group (p < 0.05). CT-SS threshold for recognising severe COVID-19 was 18.5 (area under curve, 0.960), with 84.3% sensitivity and 92.5% specificity. CONCLUSION: In coherence with COVID-19 pneumonia imaging classification, CT-SS may provide a comprehensive and objective assessment of COVID-19 severity. Key Words: COVID-19, COVID-19 pneumonia, CT-SS, High resolution computed tomography.


Assuntos
Pulmão/diagnóstico por imagem , Pneumonia Viral/diagnóstico por imagem , Tórax/diagnóstico por imagem , Tomografia Computadorizada por Raios X/métodos , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Criança , Pré-Escolar , Testes Diagnósticos de Rotina , Feminino , Humanos , Lactente , Recém-Nascido , Masculino , Pessoa de Meia-Idade , Paquistão , Radiografia Torácica/métodos , Atenção Terciária à Saúde , Adulto Jovem
12.
J Infect Dev Ctries ; 15(1): 69-72, 2021 Jan 31.
Artigo em Inglês | MEDLINE | ID: mdl-33571147

RESUMO

There is rising concern that patients who recover from COVID-19 may be at risk of recurrence. Increased rates of infection and recurrence in healthcare workers could cause the healthcare system collapse and a further worsening of the COVID-19 pandemic. Herein, we reported the clinically symptomatic recurrent COVID-19 cases in the two healthcare workers who treated and recovered from symptomatic and laboratory confirmed COVID-19. We discuss important questions in the COVID-19 pandemic waiting to be answered, such as the protection period of the acquired immunity, the severity of recurrence and how long after the first infection occurs. We aimed to emphasize that healthcare workers should continue to pay maximum attention to the measures without compromising.


Assuntos
/diagnóstico , Pessoal de Saúde , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Recidiva , Tórax/diagnóstico por imagem , Tomografia Computadorizada por Raios X
13.
PLoS One ; 16(2): e0247176, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33626053

RESUMO

The outbreak of coronavirus disease 2019 (COVID-19) has had an immense impact on world health and daily life in many countries. Sturdy observing of the initial site of infection in patients is crucial to gain control in the struggle with COVID-19. The early automated detection of the recent coronavirus disease (COVID-19) will help to limit its dissemination worldwide. Many initial studies have focused on the identification of the genetic material of coronavirus and have a poor detection rate for long-term surgery. The first imaging procedure that played an important role in COVID-19 treatment was the chest X-ray. Radiological imaging is often used as a method that emphasizes the performance of chest X-rays. Recent findings indicate the presence of COVID-19 in patients with irregular findings on chest X-rays. There are many reports on this topic that include machine learning strategies for the identification of COVID-19 using chest X-rays. Other current studies have used non-public datasets and complex artificial intelligence (AI) systems. In our research, we suggested a new COVID-19 identification technique based on the locality-weighted learning and self-organization map (LWL-SOM) strategy for detecting and capturing COVID-19 cases. We first grouped images from chest X-ray datasets based on their similar features in different clusters using the SOM strategy in order to discriminate between the COVID-19 and non-COVID-19 cases. Then, we built our intelligent learning model based on the LWL algorithm to diagnose and detect COVID-19 cases. The proposed SOM-LWL model improved the correlation coefficient performance results between the Covid19, no-finding, and pneumonia cases; pneumonia and no-finding cases; Covid19 and pneumonia cases; and Covid19 and no-finding cases from 0.9613 to 0.9788, 0.6113 to 1 0.8783 to 0.9999, and 0.8894 to 1, respectively. The proposed LWL-SOM had better results for discriminating COVID-19 and non-COVID-19 patients than the current machine learning-based solutions using AI evaluation measures.


Assuntos
/diagnóstico por imagem , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Radiografia/métodos , Algoritmos , Inteligência Artificial , Bases de Dados Factuais , Aprendizado Profundo , Humanos , Processamento de Imagem Assistida por Computador/métodos , Pulmão/diagnóstico por imagem , Pulmão/patologia , Aprendizado de Máquina , Redes Neurais de Computação , Tórax/diagnóstico por imagem , Tomografia Computadorizada por Raios X/métodos
14.
Phys Med ; 81: 52-59, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-33440281

RESUMO

PURPOSE: This study was aimed to evaluate the utility based on imaging quality of the fast non-local means (FNLM) filter in diagnosing lung nodules in pediatric chest computed tomography (CT). METHODS: We retrospectively reviewed the chest CT reconstructed with both filtered back projection (FBP) and iterative reconstruction (IR) in pediatric patients with metastatic lung nodules. After applying FNLM filter with six h values (0.0001, 0.001, 0.01, 0.1, 1, and 10) to the FBP images, eight sets of images including FBP, IR, and FNLM were analyzed. The image quality of the lung nodules was evaluated objectively for coefficient of variation (COV), contrast to noise ratio (CNR), and point spread function (PSF), and subjectively for noise, sharpness, artifacts, and diagnostic acceptability. RESULTS: The COV was lowest in IR images and decreased according to increasing h values and highest with FBP images (P < 0.001). The CNR was highest with IR images, increased according to increasing h values and lowest with FBP images (P < 0.001). The PSF was lower only in FNLM filter with h value of 0.0001 or 0.001 than in IR images (P < 0.001). In subjective analysis, only images of FNLM filter with h value of 0.0001 or 0.001 rarely showed unacceptable quality and had comparable results with IR images. There were less artifacts in FNLM images with h value of 0.0001 compared with IR images (p < 0.001). CONCLUSION: FNLM filter with h values of 0.0001 allows comparable image quality with less artifacts compared with IR in diagnosing metastatic lung nodules in pediatric chest CT.


Assuntos
Nódulos Pulmonares Múltiplos/diagnóstico por imagem , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Radiografia Torácica/métodos , Tórax/diagnóstico por imagem , Tomografia Computadorizada por Raios X/métodos , Algoritmos , Artefatos , Criança , Humanos , Imagens de Fantasmas , Estudos Retrospectivos
15.
Int J Comput Assist Radiol Surg ; 16(3): 435-445, 2021 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-33484428

RESUMO

PURPOSE: Severity scoring is a key step in managing patients with COVID-19 pneumonia. However, manual quantitative analysis by radiologists is a time-consuming task, while qualitative evaluation may be fast but highly subjective. This study aims to develop artificial intelligence (AI)-based methods to quantify disease severity and predict COVID-19 patient outcome. METHODS: We develop an AI-based framework that employs deep neural networks to efficiently segment lung lobes and pulmonary opacities. The volume ratio of pulmonary opacities inside each lung lobe gives the severity scores of the lobes, which are then used to predict ICU admission and mortality with three different machine learning methods. The developed methods were evaluated on datasets from two hospitals (site A: Firoozgar Hospital, Iran, 105 patients; site B: Massachusetts General Hospital, USA, 88 patients). RESULTS: AI-based severity scores are strongly associated with those evaluated by radiologists (Spearman's rank correlation 0.837, [Formula: see text]). Using AI-based scores produced significantly higher ([Formula: see text]) area under the ROC curve (AUC) values. The developed AI method achieved the best performance of AUC = 0.813 (95% CI [0.729, 0.886]) in predicting ICU admission and AUC = 0.741 (95% CI [0.640, 0.837]) in mortality estimation on the two datasets. CONCLUSIONS: Accurate severity scores can be obtained using the developed AI methods over chest CT images. The computed severity scores achieved better performance than radiologists in predicting COVID-19 patient outcome by consistently quantifying image features. Such developed techniques of severity assessment may be extended to other lung diseases beyond the current pandemic.


Assuntos
Inteligência Artificial , Tórax/diagnóstico por imagem , Adulto , Idoso , Idoso de 80 Anos ou mais , Bases de Dados Factuais , Feminino , Hospitalização , Humanos , Pulmão/diagnóstico por imagem , Masculino , Pessoa de Meia-Idade , Redes Neurais de Computação , Pandemias , Prognóstico , Estudos Retrospectivos , Índice de Gravidade de Doença , Tomografia Computadorizada por Raios X/métodos , Resultado do Tratamento
16.
Interdiscip Sci ; 13(1): 103-117, 2021 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-33387306

RESUMO

Corona virus disease (COVID-19) acknowledged as a pandemic by the WHO and mankind all over the world is vulnerable to this virus. Alternative tools are needed that can help in diagnosis of the coronavirus. Researchers of this article investigated the potential of machine learning methods for automatic diagnosis of corona virus with high accuracy from X-ray images. Two most commonly used classifiers were selected: logistic regression (LR) and convolutional neural networks (CNN). The main reason was to make the system fast and efficient. Moreover, a dimensionality reduction approach was also investigated based on principal component analysis (PCA) to further speed up the learning process and improve the classification accuracy by selecting the highly discriminate features. The deep learning-based methods demand large amount of training samples compared to conventional approaches, yet adequate amount of labelled training samples was not available for COVID-19 X-ray images. Therefore, data augmentation technique using generative adversarial network (GAN) was employed to further increase the training samples and reduce the overfitting problem. We used the online available dataset and incorporated GAN to have 500 X-ray images in total for this study. Both CNN and LR showed encouraging results for COVID-19 patient identification. The LR and CNN models showed 95.2-97.6% overall accuracy without PCA and 97.6-100% with PCA for positive cases identification, respectively.


Assuntos
/diagnóstico por imagem , Imageamento Tridimensional , Aprendizado de Máquina , Tórax/diagnóstico por imagem , Algoritmos , Bases de Dados como Assunto , Humanos , Modelos Logísticos , Redes Neurais de Computação , Raios X
17.
Ann Med ; 53(1): 169-180, 2021 12.
Artigo em Inglês | MEDLINE | ID: mdl-33426973

RESUMO

OBJECTIVES: Coronavirus disease 2019 (COVID-19) has rapidly swept across the world. This study aimed to explore the relationship between the chest CT findings and clinical characteristics of COVID-19 patients. METHODS: Patients with COVID-19 confirmed by next-generation sequencing or RT-PCR who had undergone more than 4 serial chest CT procedures were retrospectively enrolled. RESULTS: This study included 361 patients - 192 men and 169 women. On initial chest CT, more lesions were identified as multiple bilateral lungs lesions and localised in the peripheral lung. The predominant patterns of abnormality were ground-glass opacities (GGO) (28.5%), consolidation (13.0%), nodule (23.0%), fibrous stripes (5.3%) and mixed (30.2%). Severe cases were more common in patients with a mixed pattern (21.1%) and less common in patients with nodules (2.4%). During follow-up CT, the mediumtotal severity score (TSS) in patients with nodules and fibrous strips was significantly lower than that in patients with mixed patterns in all three stages (p < .01). CONCLUSION: Chest CT plays an important role in diagnosing COVID-19. The CT features may vary by age. Different CT features are not only associated with clinical manifestation but also patient prognosis. Key messages The initial chest CT findings of COVID-19 could help us monitor and predict the outcome. Nodules were more common in non severe cases and had a favorable prognosis. The mixed pattern was more common in severe cases and usually had a relatively poor outcome.


Assuntos
/diagnóstico por imagem , Tórax/diagnóstico por imagem , Tomografia Computadorizada por Raios X , Adolescente , Adulto , /epidemiologia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Estudos Retrospectivos , Adulto Jovem
18.
Biomark Med ; 15(4): 285-293, 2021 03.
Artigo em Inglês | MEDLINE | ID: mdl-33501850

RESUMO

Background: Troponin levels may be elevated in COVID-19 infection. The aim of this study was to the explore relation between troponin levels and COVID-19 severity. Materials, methods & Results: One hundred and forty consecutive patients with COVID-19 pneumonia were included. Diagnosis of COVID-19 pneumonia was based on positive chest computed tomography (CT) findings. Quantitative PCR test was performed in all patients. Only 74 patients were quantitative PCR-positive. Twenty four patients had severe CT findings and 27 patients had progressive disease. These patients had significantly lower albumin and higher ferritin, D-dimer, lactate dehydrogenase, C-reactive protein, and high-sensitivity cardiac troponin I (hs-cTnI). Conclusion: COVID-19 patients with severe CT findings and progressive disease had higher hs-cTnI levels suggesting the use of hs-cTnI in risk stratification.


Assuntos
Reação em Cadeia da Polimerase em Tempo Real , Tomografia Computadorizada por Raios X , Adulto , Idoso , Biomarcadores/sangue , Proteína C-Reativa/metabolismo , /diagnóstico , Feminino , Ferritinas/metabolismo , Produtos de Degradação da Fibrina e do Fibrinogênio/metabolismo , Cardiopatias , Humanos , L-Lactato Desidrogenase/sangue , Masculino , Pessoa de Meia-Idade , Albumina Sérica Humana/metabolismo , Tórax/diagnóstico por imagem , Troponina I/sangue
19.
Sensors (Basel) ; 21(2)2021 Jan 11.
Artigo em Inglês | MEDLINE | ID: mdl-33440674

RESUMO

This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopted advanced deep network architectures and proposed a transfer learning strategy using custom-sized input tailored for each deep architecture to achieve the best performance. We conducted extensive sets of experiments on two CT image datasets, namely, the SARS-CoV-2 CT-scan and the COVID19-CT. The results show superior performances for our models compared with previous studies. Our best models achieved average accuracy, precision, sensitivity, specificity, and F1-score values of 99.4%, 99.6%, 99.8%, 99.6%, and 99.4% on the SARS-CoV-2 dataset, and 92.9%, 91.3%, 93.7%, 92.2%, and 92.5% on the COVID19-CT dataset, respectively. For better interpretability of the results, we applied visualization techniques to provide visual explanations for the models' predictions. Feature visualizations of the learned features show well-separated clusters representing CT images of COVID-19 and non-COVID-19 cases. Moreover, the visualizations indicate that our models are not only capable of identifying COVID-19 cases but also provide accurate localization of the COVID-19-associated regions, as indicated by well-trained radiologists.


Assuntos
/diagnóstico , Aprendizado Profundo , Tórax/diagnóstico por imagem , Tomografia Computadorizada por Raios X , Algoritmos , /virologia , Bases de Dados Factuais , Humanos , Redes Neurais de Computação , Interpretação de Imagem Radiográfica Assistida por Computador , Tórax/patologia , Tórax/virologia
20.
Sci Rep ; 11(1): 417, 2021 01 11.
Artigo em Inglês | MEDLINE | ID: mdl-33432072

RESUMO

This study aims to explore and compare a novel deep learning-based quantification with the conventional semi-quantitative computed tomography (CT) scoring for the serial chest CT scans of COVID-19. 95 patients with confirmed COVID-19 and a total of 465 serial chest CT scans were involved, including 61 moderate patients (moderate group, 319 chest CT scans) and 34 severe patients (severe group, 146 chest CT scans). Conventional CT scoring and deep learning-based quantification were performed for all chest CT scans for two study goals: (1) Correlation between these two estimations; (2) Exploring the dynamic patterns using these two estimations between moderate and severe groups. The Spearman's correlation coefficient between these two estimation methods was 0.920 (p < 0.001). predicted pulmonary involvement (CT score and percent of pulmonary lesions calculated using deep learning-based quantification) increased more rapidly and reached a higher peak on 23rd days from symptom onset in severe group, which reached a peak on 18th days in moderate group with faster absorption of the lesions. The deep learning-based quantification for COVID-19 showed a good correlation with the conventional CT scoring and demonstrated a potential benefit in the estimation of disease severities of COVID-19.


Assuntos
/diagnóstico por imagem , Aprendizado Profundo , Pulmão/diagnóstico por imagem , Tórax/diagnóstico por imagem , Tomografia Computadorizada por Raios X , Adulto , Idoso , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Estudos Retrospectivos , Tomografia Computadorizada por Raios X/métodos
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