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1.
J Korean Med Sci ; 35(43): e391, 2020 Nov 09.
Artigo em Inglês | MEDLINE | ID: mdl-33169560

RESUMO

Since mid-April 2020, cases of multisystem inflammatory syndrome in children (MIS-C) associated with coronavirus disease 2019 that mimics Kawasaki disease (KD) have been reported in Europe and North America. However, no cases have been reported in Korea. We describe an 11-year old boy with fever, abdominal pain, and diarrhea who developed hypotension requiring inotropes in intensive care unit. His blood test revealed elevated inflammatory markers, thrombocytopenia, hypoalbuminemia, and coagulopathy. Afterward, he developed signs of KD such as conjunctival injection, strawberry tongue, cracked lip, and coronary artery dilatation, and parenchymal consolidation without respiratory symptoms. Microbiological tests were all negative including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reverse transcription polymerase chain reaction. However, serum immunoglobulin G against SARS-CoV-2 was positive in repeated tests using enzyme-linked immunosorbent assay and fluorescent immunoassay. He was recovered well after intravenous immunoglobulin administration and discharged without complication on hospital day 13. We report the first Korean child who met all the criteria of MIS-C with features of incomplete KD or KD shock syndrome.


Assuntos
Infecções por Coronavirus/patologia , Pneumonia Viral/patologia , Síndrome de Resposta Inflamatória Sistêmica/diagnóstico , Abdome/diagnóstico por imagem , Anticorpos Antivirais/sangue , Betacoronavirus/genética , Betacoronavirus/imunologia , Criança , Infecções por Coronavirus/complicações , Infecções por Coronavirus/virologia , Humanos , Imunoglobulinas Intravenosas/administração & dosagem , Masculino , Síndrome de Linfonodos Mucocutâneos/patologia , Pandemias , Pneumonia Viral/complicações , Pneumonia Viral/virologia , Síndrome de Resposta Inflamatória Sistêmica/complicações , Tórax/diagnóstico por imagem , Tomografia Computadorizada por Raios X , Ultrassonografia
2.
In Vivo ; 34(6): 3735-3746, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33144492

RESUMO

BACKGROUND/AIM: This study investigated the correlation of chest computed tomography (CT), findings, graded using two different scoring methods, with clinical and laboratory features and disease outcome, including a novel clinical predictive score, in patients with novel coronavirus-infected pneumonia (NCIP). PATIENTS AND METHODS: In this retrospective, observational study, CT scan of 92 NCIP patients admitted to Policlinico Tor Vergata, were analyzed using a quantitative, computed-based and a semiquantitative, radiologist-assessed scoring system. Correlations of the two radiological scores with clinical and laboratory features, the CALL score, and their association with a composite adverse outcome were assessed. RESULTS: The two scores correlated significantly with each other (ρ=0.637, p<0.0001) and were independently associated with age, LDH, estimated glomerular filtration rate, diabetes, and with the composite outcome, which occurred in 24 patients. CONCLUSION: In NCIP patients, two different radiological scores correlated with each other and with several clinical, laboratory features, and the CALL score. The quantitative score was a better independent predictor of the composite adverse outcome than the semiquantitative score.


Assuntos
Infecções por Coronavirus/diagnóstico por imagem , Pneumonia Viral/diagnóstico por imagem , Pneumonia/diagnóstico por imagem , Tórax/diagnóstico por imagem , Idoso , Idoso de 80 Anos ou mais , Betacoronavirus/patogenicidade , Infecções por Coronavirus/mortalidade , Infecções por Coronavirus/fisiopatologia , Infecções por Coronavirus/terapia , Infecções por Coronavirus/virologia , Feminino , Hospitalização , Humanos , Unidades de Terapia Intensiva , Masculino , Pessoa de Meia-Idade , Pandemias , Pneumonia/mortalidade , Pneumonia/fisiopatologia , Pneumonia/virologia , Pneumonia Viral/fisiopatologia , Pneumonia Viral/terapia , Pneumonia Viral/virologia , Tórax/fisiopatologia , Tórax/virologia , Tomografia Computadorizada por Raios X
3.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4118-4121, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018904

RESUMO

This paper introduces an automatic non-contact monitoring method for measuring the respiratory rate of neonates using a structured light camera. The current monitoring bears several issues causing pressure marks, skin irritations and eczema. A structured light camera provides distance data. Our non-contact approach detects the thorax area automatically using a plane segmentation and calculates the respiratory rate from the movement of the thorax. Our method was tested and validated using the baby simulator SimBaby by Laerdal. We used different breathing rates corresponding to preterm neonates, mature neonates and babies aged up to nine months as well as two different breathing modes with differing breathing strokes. Furthermore, measurements were taken of two positions: the baby lying on its back and on its stomach.


Assuntos
Respiração , Taxa Respiratória , Humanos , Lactente , Recém-Nascido , Movimento , Tórax
4.
JMIR Public Health Surveill ; 6(4): e19424, 2020 10 20.
Artigo em Inglês | MEDLINE | ID: mdl-33001830

RESUMO

BACKGROUND: Computed tomography (CT) scans are increasingly available in clinical care globally. They enable a rapid and detailed assessment of tissue and organ involvement in disease processes that are relevant to diagnosis and management, particularly in the context of the COVID-19 pandemic. OBJECTIVE: The aim of this paper is to identify differences in the CT scan findings of patients who were COVID-19 positive (confirmed via nucleic acid testing) to patients who were confirmed COVID-19 negative. METHODS: A retrospective cohort study was proposed to compare patient clinical characteristics and CT scan findings in suspected COVID-19 cases. A multivariable logistic model with LASSO (least absolute shrinkage and selection operator) selection for variables was used to identify the good predictors from all available predictors. The area under the curve (AUC) with 95% CI was calculated for each of the selected predictors and the combined selected key predictors based on receiver operating characteristic curve analysis. RESULTS: A total of 94 (56%) patients were confirmed positive for COVID-19 from the suspected 167 patients. We found that elderly people were more likely to be infected with COVID-19. Among the 94 confirmed positive patients, 2 (2%) patients were admitted to an intensive care unit. No patients died during the study period. We found that the presence, distribution, and location of CT lesions were associated with the presence of COVID-19. White blood cell count, cough, and a travel history to Wuhan were also the top predictors for COVID-19. The overall AUC of these selected predictors is 0.97 (95% CI 0.93-1.00). CONCLUSIONS: Taken together with nucleic acid testing, we found that CT scans can allow for the rapid diagnosis of COVID-19. This study suggests that chest CT scans should be more broadly adopted along with nucleic acid testing in the initial assessment of suspected COVID-19 cases, especially for patients with nonspecific symptoms.


Assuntos
Técnicas de Laboratório Clínico/métodos , Tórax/diagnóstico por imagem , Tomografia Computadorizada por Raios X , Adolescente , Adulto , Infecções por Coronavirus/diagnóstico , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Curva ROC , Reprodutibilidade dos Testes , Estudos Retrospectivos , Adulto Jovem
7.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2732-2735, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018571

RESUMO

Demand of portable health monitoring has been growing due to increasing cardiovascular and respiratory diseases. While both cardiovascular monitoring and respiratory monitoring have been developed independently, there lacks a simple integrated solution to monitor both simultaneously. Seismocardiography (SCG), a method of recording cardiac vibrations with an accelerometer can also be used to extract respiratory information via low frequency chest oscillations. This study used an inertial measurement unit which pairs a 3-axis accelerometer and a 3-axis gyroscope to monitor respiration while maintaining optimum placement protocol for recording SCG. Additionally, the connection between inertial measurement and both respiratory rate and volume were explored based on their correlation with a Spirometer. Respiratory volume was shown to have moderate correlation with chest motion with an average best-case correlation coefficient of 0.679 across acceleration and gyration. The techniques described will assist the design of future SCG algorithms by understanding the sources behind their modulation from respiration. This paper shows that a simplified processing technique can be added to SCG algorithms for respiration monitoring.


Assuntos
Respiração , Processamento de Sinais Assistido por Computador , Humanos , Monitorização Fisiológica , Taxa Respiratória , Tórax
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4636-4639, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019027

RESUMO

Breathing rate was estimated from chest-worn accelerometry collected from 1,522 servicemembers during training by a wearable physiological monitor. A total of 29,189 hours of training and sleep data were analyzed. The primary purpose of the monitor was to assess thermal-work strain and avoid heat injuries. The monitor design was thus not optimized to estimate breathing rate. Since breathing rate cannot be accurately estimated during periods of high activity, a qualifier was applied to identify sedentary time periods, totaling 8,867 hours. Breathing rate was estimated for a total of 4,179 hours, or 14% of the total collection and 47% of the sedentary total, primarily during periods of sleep. The breathing rate estimation method was compared to an FDA 510(K)-cleared criterion breathing rate sensor (Zephyr, Annapolis MD, USA) in a controlled laboratory experiment, which showed good agreement between the two techniques. Contributions of this paper are to: 1) provide the first analysis of accelerometry-derived breathing rate on free-living data including periods of high activity as well as sleep, along with a qualifier that effectively identifies sedentary periods appropriate for estimating breathing rate; 2) test breathing rate estimation on a data set with a total duration that is more than 60 times longer than that of the largest previously reported study, 3) test breathing rate estimation on data from a physiological monitor that has not been expressly designed for that purpose.


Assuntos
Acelerometria , Taxa Respiratória , Humanos , Monitorização Fisiológica , Sono , Tórax
9.
Infect Dis Poverty ; 9(1): 143, 2020 Oct 19.
Artigo em Inglês | MEDLINE | ID: mdl-33076968

RESUMO

BACKGROUND: Effective management of imported cases is an important part of epidemic prevention and control. Hainan Province, China reported 168 coronavirus disease 2019 (COVID-19), including 112 imported cases on February 19, 2020, but successfully contained the epidemic within 1 month. We described the epidemiological and clinical characteristics of COVID-19 in Hainan and compared these features between imported and local cases to provide information for other international epidemic areas. METHODS: We included 91 patients (56 imported and 35 local cases) from two designated hospitals for COVID-19 in Haikou, China, from January 20 to February 19, 2020. Data on the demographic, epidemiological, clinical and laboratory characteristics were extracted from medical records. Patients were followed until April 21, 2020, and the levels of antibodies at the follow-ups were also analysed by the Wilcoxon matched-pairs signed ranks test. RESULTS: Of the 91 patients, 78 (85.7%) patients were diagnosed within the first three weeks after the first case was identified (Day 1: Jan 22, 2020), while the number of local cases started to increase during the third week. No new cases occurred after Day 29. Fever and cough were two main clinical manifestations. In total, 15 (16.5%) patients were severe, 14 (15.4%) had complicated infections, nine (9.9%) were admitted to the intensive care unit, and three died. The median duration of viral shedding in feces was longer than that in nasopharyngeal swabs (19 days vs 16 days, P = 0.007). Compared with local cases, imported cases were older and had a higher incidence of fever and concurrent infections. There was no difference in outcomes between the two groups. IgG was positive in 92.8% patients (77/83) in the follow-up at week 2 after discharge, while 88.4% patients (38/43) had a reduction in IgG levels in the follow-up at week 4 after discharge, and the median level was lower than that in the follow-up at week 2 (10.95 S/Cut Off (S/CO) vs 15.02 S/CO, P <  0.001). CONCLUSION: Imported cases were more severe than local cases but had similar prognoses. The level of IgG antibodies declined from week 6 to week 8 after onset. The short epidemic period in Hainan suggests that the epidemic could be quickly brought under control if proper timely measures were taken.


Assuntos
Doenças Transmissíveis Importadas/epidemiologia , Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/epidemiologia , Pneumonia Viral/diagnóstico , Pneumonia Viral/epidemiologia , Adulto , Idoso , Betacoronavirus/isolamento & purificação , China/epidemiologia , Doenças Transmissíveis Importadas/diagnóstico , Doenças Transmissíveis Importadas/terapia , Doenças Transmissíveis Importadas/virologia , Infecções por Coronavirus/virologia , Fezes/virologia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Pandemias , Pneumonia Viral/virologia , Estudos Retrospectivos , Tórax/diagnóstico por imagem , Resultado do Tratamento , Eliminação de Partículas Virais
10.
Sci Rep ; 10(1): 17236, 2020 10 14.
Artigo em Inglês | MEDLINE | ID: mdl-33057039

RESUMO

To assess the use of a structured report in the Chest Computed Tomography (CT) reporting of patients with suspicious viral pneumonia by COVID-19 and the evaluation of the main CT patterns. This study included 134 patients (43 women and 91 men; 68.8 years of mean age, range 29-93 years) with suspicious COVID-19 viral infection evaluated by reverse transcription real-time fluorescence polymerase chain reaction (RT-PCR) test. All patients underwent CT examinations at the time of admission. CT images were reviewed by two radiologists who identified COVID-19 CT patterns using a structured reports. Temporal difference mean value between RT-PCRs and CT scan was 0.18 days ± 2.0 days. CT findings were positive for viral pneumonia in 94.0% patients while COVID-19 was diagnosed at RT-PCR in 77.6% patients. Time mean value to complete the structured report by radiologist was 8.5 min ± 2.4 min. The disease on chest CT predominantly affected multiple lobes and the main CT feature was ground glass opacity (GGO) with or without consolidation (96.8%). GGO was predominantly bilateral (89.3%), peripheral (80.3%), multifocal/patching (70.5%). Consolidation disease was predominantly bilateral (83.9%) with prevalent peripheral (87.1%) and segmental (47.3%) distribution. Additional CT signs were the crazy-paving pattern in 75.4% of patients, the septal thickening in 37.3% of patients, the air bronchogram sign in 39.7% and the "reversed halo" sign in 23.8%. Less frequent characteristics at CT regard discrete pulmonary nodules, increased trunk diameter of the pulmonary artery, pleural effusion and pericardium effusion (7.9%, 6.3%, 14.3% and 16.7%, respectively). Barotrauma sign was absent in all the patients. High percentage (54.8%) of the patients had mediastinal lymphadenopathy. Using a Chest CT structured report, with a standardized language, we identified that the cardinal hallmarks of COVID-19 infection were bilateral, peripheral and multifocal/patching GGO and bilateral consolidation with peripheral and segmental distribution.


Assuntos
Infecções por Coronavirus/diagnóstico , Registros Eletrônicos de Saúde , Pneumonia Viral/diagnóstico , Tórax/diagnóstico por imagem , Tomografia Computadorizada por Raios X , Adulto , Idoso , Idoso de 80 Anos ou mais , Betacoronavirus/genética , Betacoronavirus/isolamento & purificação , Infecções por Coronavirus/virologia , Feminino , Humanos , Itália , Masculino , Pessoa de Meia-Idade , Pandemias , Pneumonia Viral/virologia , RNA Viral/metabolismo , Reação em Cadeia da Polimerase em Tempo Real , Estudos Retrospectivos
11.
Sci Rep ; 10(1): 17365, 2020 10 15.
Artigo em Inglês | MEDLINE | ID: mdl-33060676

RESUMO

To analyze the clinical characteristics of re-positive discharged COVID-19 patients and find distinguishing markers. The demographic features, clinical symptoms, laboratory results, comorbidities, co-infections, treatments, illness severities and chest CT scan results of 267 patients were collected from 1st January to 15th February 2020. COVID-19 was diagnosed by RT-PCR. Clinical symptoms and nucleic acid test results were collected during the 14 days post-hospitalization quarantine. 30 out of 267 COVID-19 patients were detected re-positive during the post-hospitalization quarantine. Re-positive patients could not be distinguished by demographic features, clinical symptoms, laboratory results, comorbidities, co-infections, treatments, chest CT scan results or subsequent clinical symptoms. However, re-positive rate was found to be correlated to illness severity, according the Acute Physiology and Chronic Health Evaluation II (APACHE II) severity-of-disease classification system, and the confusion, urea, respiratory rate and blood pressure (CURB-65) score. Common clinical characteristics were not able to distinguish re-positive patients. However, severe and critical cases classified high according APACHE II and CURB-65 scores, were more likely to become re-positive after discharge.


Assuntos
Betacoronavirus/genética , Infecções por Coronavirus/patologia , Pneumonia Viral/patologia , Adulto , Idoso , Betacoronavirus/isolamento & purificação , China , Comorbidade , Infecções por Coronavirus/virologia , Feminino , Seguimentos , Humanos , Modelos Logísticos , Masculino , Pessoa de Meia-Idade , Pandemias , Alta do Paciente , Pneumonia Viral/virologia , Quarentena , RNA Viral/metabolismo , Reação em Cadeia da Polimerase Via Transcriptase Reversa , Índice de Gravidade de Doença , Tórax/diagnóstico por imagem , Tomografia Computadorizada por Raios X
12.
Sci Rep ; 10(1): 17532, 2020 10 16.
Artigo em Inglês | MEDLINE | ID: mdl-33067538

RESUMO

This study aimed to develop and validate computer-aided diagnosis (CXDx) system for classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray (CXR) images. From two public datasets, 1248 CXR images were obtained, which included 215, 533, and 500 CXR images of COVID-19 pneumonia patients, non-COVID-19 pneumonia patients, and the healthy samples, respectively. The proposed CADx system utilized VGG16 as a pre-trained model and combination of conventional method and mixup as data augmentation methods. Other types of pre-trained models were compared with the VGG16-based model. Single type or no data augmentation methods were also evaluated. Splitting of training/validation/test sets was used when building and evaluating the CADx system. Three-category accuracy was evaluated for test set with 125 CXR images. The three-category accuracy of the CAD system was 83.6% between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy. Sensitivity for COVID-19 pneumonia was more than 90%. The combination of conventional method and mixup was more useful than single type or no data augmentation method. In conclusion, this study was able to create an accurate CADx system for the 3-category classification. Source code of our CADx system is available as open source for COVID-19 research.


Assuntos
Infecções por Coronavirus/diagnóstico , Pneumonia Viral/diagnóstico , Pneumonia/diagnóstico , Tórax/diagnóstico por imagem , Adulto , Idoso , Automação , Betacoronavirus/isolamento & purificação , Infecções por Coronavirus/virologia , Bases de Dados Factuais , Aprendizado Profundo , Diagnóstico por Computador , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Pandemias , Pneumonia/classificação , Pneumonia Viral/virologia
13.
Sci Rep ; 10(1): 17543, 2020 10 16.
Artigo em Inglês | MEDLINE | ID: mdl-33067524

RESUMO

The aim of this study was to assess the prognostic value of baseline clinical and high resolution CT (HRCT) findings in patients with severe COVID-19. In this retrospective, two-center study, we included two groups of inpatients with severe COVID-19 who had been discharged or died in Jin Yin-tan hospital and Wuhan union hospital between January 5, 2020, and February 22, 2020. Cases were confirmed by real-time polymerase chain reaction. Demographic, clinical, and laboratory data, and HRCT imaging were collected and compared between discharged and deceased patients. Univariable and multivariable logistic regression models were used to assess predictors of mortality risk in these patients. 101 patients were included in this study, of whom 66 were discharged and 35 died in the hospital. The mean age was 56.6 ± 15.1 years and 67 (66.3%) were men. Of the 101 patients, hypertension (38, 37.6%), cardiovascular disease (21,20.8%), diabetes (18,17.8%), and chronic pulmonary disease (16,15.8%) were the most common coexisting conditions. The multivariable regression analysis showed older age (OR: 1.142, 95% CI 1.059-1.231, p < 0.001), acute respiratory distress syndrome (ARDS) (OR: 10.142, 95% CI 1.611-63.853, p = 0.014), reduced lymphocyte count (OR: 0.004, 95% CI 0.001-0.306, p = 0.013), and elevated HRCT score (OR: 1.276, 95% CI 1.002-1.625, p = 0.049) to be independent predictors of mortality risk on admission in severe COVID-19 patients. These findings may have important clinical implications for decision-making based on risk stratification of severe COVID-19 patients.


Assuntos
Infecções por Coronavirus/patologia , Pneumonia Viral/patologia , Tomografia Computadorizada por Raios X , Adulto , Idoso , Betacoronavirus/isolamento & purificação , Comorbidade , Infecções por Coronavirus/mortalidade , Infecções por Coronavirus/virologia , Feminino , Humanos , Modelos Logísticos , Contagem de Linfócitos , Masculino , Pessoa de Meia-Idade , Razão de Chances , Pandemias , Pneumonia Viral/mortalidade , Pneumonia Viral/virologia , Prognóstico , Estudos Retrospectivos , Fatores de Risco , Índice de Gravidade de Doença , Tórax/diagnóstico por imagem
14.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4571-4574, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019011

RESUMO

Cerebellar ataxia (CA) refers to the impaired balance and coordination resulting from injury or degeneration of the cerebellum. Testing balance is one of the simplest means of assessing CA. This study compares instrumented assessment and clinical assessment scales of the balance test called Romberg's test. Inertial Measurement Unit (IMU) data were collected from a sensor attached to their chest of 53 subjects while they performed the test. The corresponding clinical scores were also tabulated. Using this data, 99 features were extracted to quantify acceleration, tremor and displacement of body sway. These features were filtered to identify the subset that better characterize the distinctive behavior of CA subjects. Elastic Net Regression model resulted a greater agreement (0.70 Pearson coefficient) with the clinical SARA scores. The overall results indicated that data from a single IMU sensor is sufficient to accurately assess balance in CA. The significance of this study is that evaluation of balance using Recurrence Quantification Analysis produces a comprehensive framework for the assessment of CA.


Assuntos
Ataxia Cerebelar , Aceleração , Ataxia Cerebelar/diagnóstico , Humanos , Equilíbrio Postural , Tórax , Tremor
15.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4632-4635, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019026

RESUMO

Various measurement systems can be used to obtain dynamic circumferences of the human upper body, but each of these systems has disadvantages. In this feasibility study we introduce a non-invasive and wearable thoracic belt to measure dynamic changes of circumferences of thorax or abdomen. To evaluate this approach, five subjects undertook various breaths of disparate tidal volumes, which were measured by the belt and simultaneously by a motion capture system which provided a reference metric.The results of the belt concurred with the reference system. A coefficient of determination (adjusted R2) of 0.99 and a mean squared error of less than 0.87 mm2 showed that the belt is capable of measuring changes accurately and a couple of respiratory parameters, such as the respiratory rate, can be obtained.Clinical Relevance-The introduced system links surface motions of the upper body with the underlying respiratory mechanics. Thus it provides some respiratory parameters without the disadvantages of a facemask or a mouthpiece. The system could allow the analysis of breathing status in some clinical applications and could be used for low-cost monitoring in homecare or to analyse respiratory parameters during sports.


Assuntos
Respiração , Tórax , Estudos de Viabilidade , Humanos , Projetos Piloto , Volume de Ventilação Pulmonar
16.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5388-5393, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019199

RESUMO

Pectus Excavatum (PE) is a congenital anomaly of the ribcage, at the level of the sterno-costal plane, which consists of an inward angle of the sternum, in the direction of the spine. PE is the most common of all thoracic malformations, with an incidence of 1 in 300-400 people. To monitor the progress of the pathology, severity indices, or thoracic indices, have been used over the years. Among these indices, recent studies focus on the calculation of optical measures, calculated on the optical scan of the patient's chest, which can be very accurate without exposing the patient to invasive treatments such as CT scans. In this work, data from a sample of PE patients and corresponding doctors' severity assessments have been collected and used to create a decision tool to automatically assign a severity value to the patient. The idea is to provide the physician with an objective and easy to use measuring instrument that can be exploited in an outpatient clinic context. Among several classification tools, a Probabilistic Neural Network was chosen for this task for its simple structure and learning mode.


Assuntos
Tórax em Funil , Tórax em Funil/diagnóstico por imagem , Humanos , Monitorização Ambulatorial , Redes Neurais de Computação , Esterno , Tórax
17.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1238-1241, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018211

RESUMO

Pneumonia is one of the leading causes of childhood mortality worldwide. Chest x-ray (CXR) can aid the diagnosis of pneumonia, but in the case of low contrast images, it is important to include computational tools to aid specialists. Deep learning is an alternative because it can identify patterns automatically, even in low-resolution images. We propose herein a convolutional neural network (CNN) architecture with different training strategies towards detecting pneumonia on CXRs and distinguishing its subforms of bacteria and virus. We also evaluated different image pre-processing methods to improve the classification. This study used CXRs from pediatric patients from a public pneumonia CXR dataset. The pre-processing methods evaluated were image cropping and histogram equalization. To classify the images, we adopted the VGG16 CNN and replaced its fully-connected layers with a customized multilayer perceptron. With this architecture, we proposed and evaluated four different training strategies: original CXR image (baseline), chest-cavity-cropped image (A), and histogram-equalized segmented image (B). The last strategy method (C) implemented is based on ensemble between strategies A and B. The performance was assessed by the area under the ROC curve (AUC) with 95% confidence interval (CI), accuracy, sensitivity, specificity, and F1-score. The ensemble model C yielded the highest performances: AUC of 0.97 (CI: 0.96-0.99) to classify pneumonia vs. normal, and AUC of 0.91 (CI: 0.88-0.94) to classify bacterial vs. viral cases. All models that used pre-processed images showed higher AUC than baseline, which used the original CXR image. Image cropping and histogram equalization reduced irrelevant information from the exam, enhanced contrast, and was able to identify fine CXR texture details. The proposed ensemble model increased the representation of inflammatory patterns from bacteria and viruses with few epochs to train the deep CNNs.Clinical relevance- Deep learning can identify complex radiographic patterns in low contrast images due to pneumonia and distinguish its subforms of bacteria and virus. The correlation of imaging with lab results could accelerate the adoption of complementary exams to confirm the disease's cause.


Assuntos
Aprendizado Profundo , Pneumonia , Criança , Humanos , Redes Neurais de Computação , Pneumonia/diagnóstico por imagem , Tórax , Raios X
18.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1242-1245, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018212

RESUMO

Automatic and accurate lung segmentation in chest X-ray (CXR) images is fundamental for computer-aided diagnosis systems since the lung is the region of interest in many diseases and also it can reveal useful information by its contours. While deep learning models have reached high performances in the segmentation of anatomical structures, the large number of training parameters is a concern since it increases memory usage and reduces the generalization of the model. To address this, a deep CNN model called Dense-Unet is proposed in which, by dense connectivity between various layers, information flow increases throughout the network. This lets us design a network with significantly fewer parameters while keeping the segmentation robust. To the best of our knowledge, Dense-Unet is the lightest deep model proposed for the segmentation of lung fields in CXR images. The model is evaluated on the JSRT and Montgomery datasets and experiments show that the performance of the proposed model is comparable with state-of-the-art methods.


Assuntos
Redes Neurais de Computação , Tórax , Diagnóstico por Computador , Pulmão/diagnóstico por imagem , Raios X
19.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1246-1249, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018213

RESUMO

Lung cancer is, by far, the leading cause of cancer death in the world. Tools for automated medical imaging analysis development of a Computer-Aided Diagnosis method comprises several tasks. In general, the first one is the segmentation of region of interest, for example, lung region segmentation from Chest X-ray imaging in the task of detecting lung cancer. Deep Convolutional Neural Networks (DCNN) have shown promising results in the task of segmentation in medical images. In this paper, to implement the lung region segmentation task on chest X-ray images, was evaluated three different DCNN architectures in association with different regularization (Dropout, L2, and Dropout + L2) and optimization methods (SGDM, RMSPROP and ADAM). All networks were applied in the Japanese Society of Radiological Technology (JSRT) database. The best results were obtained using Dropout + L2 as regularization method and ADAM as optimization method. Considering the Jaccard Coefficient obtained (0.97967 ± 0.00232) the proposal outperforms the state of the art.Clinical Relevance- The presented method reduces the time that a professional takes to perform lung segmentation, improving the effectiveness.


Assuntos
Redes Neurais de Computação , Tórax , Diagnóstico por Computador , Pulmão/diagnóstico por imagem , Raios X
20.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1258-1261, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018216

RESUMO

Despite the potential of deep convolutional neural networks for classification of thorax diseases from chest X-ray images, this task is still challenging as it is categorized as a weakly supervised learning problem, and deep neural networks in general suffer from a lack of interpretability. In this paper, a deep convolutional neural network framework with recurrent attention mechanism was investigated to annotate abnormalities in chest X-ray images. A modified MobileNet architecture was adapted in the framework for classification and the prediction difference analysis method was utilized to visualize the basis of network's decision on each image. A long short-term memory network was utilized as the attention model to focus on relevant regions of each image for classification. The framework was evaluated on NIH chest X-ray dataset. The attention-guided model versus the model with no attention mechanism could annotate the images in an independent test set with an F1-score of 0.58 versus 0.46, and an AUC of 0.94 versus 0.73. The obtained results implied that the proposed attention-guided model could outperform the other methods investigated previously for annotating the same dataset.


Assuntos
Algoritmos , Redes Neurais de Computação , Atenção , Tórax , Raios X
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