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1.
Sci Rep ; 11(1): 15523, 2021 09 01.
Artigo em Inglês | MEDLINE | ID: mdl-34471144

RESUMO

Chest radiography (CXR) is the most widely-used thoracic clinical imaging modality and is crucial for guiding the management of cardiothoracic conditions. The detection of specific CXR findings has been the main focus of several artificial intelligence (AI) systems. However, the wide range of possible CXR abnormalities makes it impractical to detect every possible condition by building multiple separate systems, each of which detects one or more pre-specified conditions. In this work, we developed and evaluated an AI system to classify CXRs as normal or abnormal. For training and tuning the system, we used a de-identified dataset of 248,445 patients from a multi-city hospital network in India. To assess generalizability, we evaluated our system using 6 international datasets from India, China, and the United States. Of these datasets, 4 focused on diseases that the AI was not trained to detect: 2 datasets with tuberculosis and 2 datasets with coronavirus disease 2019. Our results suggest that the AI system trained using a large dataset containing a diverse array of CXR abnormalities generalizes to new patient populations and unseen diseases. In a simulated workflow where the AI system prioritized abnormal cases, the turnaround time for abnormal cases reduced by 7-28%. These results represent an important step towards evaluating whether AI can be safely used to flag cases in a general setting where previously unseen abnormalities exist. Lastly, to facilitate the continued development of AI models for CXR, we release our collected labels for the publicly available dataset.


Assuntos
COVID-19/diagnóstico por imagem , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Tuberculose/diagnóstico por imagem , Adulto , Idoso , Algoritmos , Estudos de Casos e Controles , China , Aprendizado Profundo , Feminino , Humanos , Índia , Masculino , Pessoa de Meia-Idade , Radiografia Torácica , Estados Unidos
2.
Sensors (Basel) ; 21(17)2021 Aug 24.
Artigo em Inglês | MEDLINE | ID: mdl-34502591

RESUMO

The global COVID-19 pandemic that started in 2019 and created major disruptions around the world demonstrated the imperative need for quick, inexpensive, accessible and reliable diagnostic methods that would allow the detection of infected individuals with minimal resources. Radiography, and more specifically, chest radiography, is a relatively inexpensive medical imaging modality that can potentially offer a solution for the diagnosis of COVID-19 cases. In this work, we examined eleven deep convolutional neural network architectures for the task of classifying chest X-ray images as belonging to healthy individuals, individuals with COVID-19 or individuals with viral pneumonia. All the examined networks are established architectures that have been proven to be efficient in image classification tasks, and we evaluated three different adjustments to modify the architectures for the task at hand by expanding them with additional layers. The proposed approaches were evaluated for all the examined architectures on a dataset with real chest X-ray images, reaching the highest classification accuracy of 98.04% and the highest F1-score of 98.22% for the best-performing setting.


Assuntos
COVID-19 , Aprendizado Profundo , Algoritmos , Humanos , Pandemias , Radiografia , Radiografia Torácica , SARS-CoV-2 , Raios X
3.
Ethiop J Health Sci ; 31(3): 561-572, 2021 May.
Artigo em Inglês | MEDLINE | ID: mdl-34483613

RESUMO

Background: The cardiothoracic ratio (CTR) is a radiographic parameter commonly used in assessing the size of the heart. This study evaluated the gender and age-based differences in the average cardiothoracic ratios, and transverse cardiac diameters (TCD) of adults in Ghana. Method: Plain chest radiography reports of 2004 patients (without known chest related diseases) generated by two radiologists with at least 15 years' experience from July 2016 to June 2020 were retrospectively analyzed for this study. The CTR for each radiograph was calculated using the formula CTR=(TCD÷TTD)×100, where TCD and TTD represent transverse cardiac diameters and transverse thoracic diameters, respectively. Data were analyzed with the statistical package for social sciences version 23. The independent t-test and One-way Analysis of Variance tests were used in the analyses. Results: A total of 2004 patients' chest x-rays were used in the analyses. The ages of the patients ranged from 20-86 years old with a mean of 39.4±14.04 years. The mean CTR for males was 46.6 ± 3.7% while that of females was 47.7±3.7%. The difference in the overall CTR among the gender groupings was statistically significant (p = 0.001). There were statistically significant differences between the gender categories among patients in the following age groups: 30-39 (p=0.046), 40-49 (p=0.001), 50-59 (p=0.001) and 60-69 (p=0.001). Conclusion: The study reveals there are significant gender and age-related differences in cardiac size parameters obtained from routine, frontal chest radiographs. These differences, if considered, may result in early and appropriate treatment of cardiac pathology in some age groups.


Assuntos
Coração , Radiografia Torácica , Adulto , Idoso , Idoso de 80 Anos ou mais , Feminino , Gana , Coração/diagnóstico por imagem , Humanos , Masculino , Pessoa de Meia-Idade , Estudos Retrospectivos , Adulto Jovem
4.
Medicine (Baltimore) ; 100(35): e27138, 2021 Sep 03.
Artigo em Inglês | MEDLINE | ID: mdl-34477165

RESUMO

RATIONALE: The emergence of immune checkpoint inhibitors has brought new breakthroughs in the treatment of small cell lung cancer (SCLC). Programmed cell death-ligand 1 inhibitors combined with chemotherapy have been approved for the first-line treatment of extensive-stage small cell lung cancer (ES-SCLC). However, programmed death 1 inhibitors have limited efficacy in the treatment of SCLC. The reason may be related to the abnormal vascular state in the tumor microenvironment. PATIENT CONCERNS: A 55-year-old male patient, presenting cough and sputum for 1 month. DIAGNOSES: The patient was clinically diagnosed with SCLC and staged as ES-SCLC. INTERVENTIONS: Etoposide combined with lobaplatin treatment every 3 weeks for 4 cycles, evaluate as progressive disease. On the basis of the original plan, combined with camrelizumab for 2 cycles, evaluation as progressive disease. Then, the patient was treated with intravenous infusion of camrelizumab plus oral anlotinib. After 4 cycles, evaluation as partial response. Then we continued to use camrelizumab combined with anlotinib treatment for the patient. At the end of 26 cycles, the chest computed tomography examination revealed that the patient had achieved complete remission. OUTCOMES: After treated with carrelizumab combined with anlotinib for 26 cycles, the curative effect was evaluated as complete remission, progression-free survival was 24 months and there was no immune-related adverse reaction during treatment period. Besides, the patient developed complicated hand-foot syndrome, but this symptom was significantly relieved after reducing the dosage of anlotinib. LESSONS: In this case, antiangiogenesis combined with programmed death 1 inhibitors significantly inhibited tumor progression. It also indicated that anlotinib concurrent carrelizumab may be a superior choice for ES-SCLC. Further clinical trials required to confifirm its effificacy and safety.


Assuntos
Anticorpos Monoclonais Humanizados/uso terapêutico , Indóis/uso terapêutico , Neoplasias Pulmonares/tratamento farmacológico , Quinolinas/uso terapêutico , Carcinoma de Pequenas Células do Pulmão/tratamento farmacológico , Humanos , Neoplasias Pulmonares/induzido quimicamente , Masculino , Pessoa de Meia-Idade , Radiografia Torácica , Carcinoma de Pequenas Células do Pulmão/induzido quimicamente , Tomografia Computadorizada por Raios X
5.
Comput Biol Med ; 136: 104704, 2021 09.
Artigo em Inglês | MEDLINE | ID: mdl-34352454

RESUMO

Chest X-ray images are used in deep convolutional neural networks for the detection of COVID-19, the greatest human challenge of the 21st century. Robustness to noise and improvement of generalization are the major challenges in designing these networks. In this paper, we introduce a strategy for data augmentation using the determination of the type and value of noise density to improve the robustness and generalization of deep CNNs for COVID-19 detection. Firstly, we present a learning-to-augment approach that generates new noisy variants of the original image data with optimized noise density. We apply a Bayesian optimization technique to control and choose the optimal noise type and its parameters. Secondly, we propose a novel data augmentation strategy, based on denoised X-ray images, that uses the distance between denoised and original pixels to generate new data. We develop an autoencoder model to create new data using denoised images corrupted by the Gaussian and impulse noise. A database of chest X-ray images, containing COVID-19 positive, healthy, and non-COVID pneumonia cases, is used to fine-tune the pre-trained networks (AlexNet, ShuffleNet, ResNet18, and GoogleNet). The proposed method performs better results compared to the state-of-the-art learning to augment strategies in terms of sensitivity (0.808), specificity (0.915), and F-Measure (0.737). The source code of the proposed method is available at https://github.com/mohamadmomeny/Learning-to-augment-strategy.


Assuntos
COVID-19 , Aprendizado Profundo , Teorema de Bayes , Humanos , Radiografia Torácica , SARS-CoV-2 , Raios X
7.
Radiat Prot Dosimetry ; 195(2): 75-82, 2021 Sep 08.
Artigo em Inglês | MEDLINE | ID: mdl-34341827

RESUMO

Determination of appropriate radiation doses to paediatric patients in accordance with the as low as reasonably achievable (ALARA) principle is important, as it allows for effective optimization of imaging techniques. This study assessed the status of radiation dose levels in paediatric patients undergoing chest X-ray examinations at a tertiary hospital in Ghana. A population encompassing 86 paediatric patients categorised as infants (<1 y), young children (1-5 y) and older children (6-12 y) was selected using a quasi-experimental study design. The patients' anatomical data and X-ray beam exposure parameters were used to indirectly calculate the entrance surface doses (ESDs) received during the examinations. The infants received the highest mean ESD of 196 µGy (uncertainty = 0.37) compared to 158 µGy (uncertainty = 0.46) among the older children. The risk of developing radiation-induced biological effects was therefore higher for infant patients. The ESDs were generally higher than the internationally recommended reference doses. Careful adoption of internationally accepted exposure factors (high tube voltage and low tube load) is most recommended to optimise the dose.


Assuntos
Radiografia Torácica , Tórax , Adolescente , Criança , Pré-Escolar , Gana , Humanos , Lactente , Doses de Radiação , Radiografia , Centros de Atenção Terciária
8.
Sci Prog ; 104(3): 368504211016204, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34424791

RESUMO

As the coronavirus disease 2019 (COVID-19) epidemic spreads around the world, the demand for imaging examinations increases accordingly. The value of conventional chest radiography (CCR) remains unclear. In this study, we aimed to investigate the diagnostic value of CCR in the detection of COVID-19 through a comparative analysis of CCR and CT. This study included 49 patients with 52 CT images and chest radiographs of pathogen-confirmed COVID-19 cases and COVID-19-suspected cases that were found to be negative (non-COVID-19). The performance of CCR in detecting COVID-19 was compared to CT imaging. The major signatures that allowed for differentiation between COVID-19 and non-COVID-19 cases were also evaluated. Approximately 75% (39/52) of images had positive findings on the chest x-ray examinations, while 80.7% (42/52) had positive chest CT scans. The COVID-19 group accounted for 88.4% (23/26) of positive chest X-ray examinations and 96.1% (25/26) of positive chest CT scans. The sensitivity, specificity, and accuracy of CCR for abnormal shadows were 88%, 80%, and 87%, respectively, for all patients. For the COVID-19 group, the accuracy of CCR was 92%. The primary signature on CCR was flocculent shadows in both groups. The shadows were primarily in the bi-pulmonary, which was significantly different from non-COVID-19 patients (p = 0.008). The major CT finding of COVID-19 patients was ground-glass opacities in both lungs, while in non-COVID-19 patients, consolidations combined with ground-glass opacities were more common in one lung than both lungs (p = 0.0001). CCR showed excellent performance in detecting abnormal shadows in patients with confirmed COVID-19. However, it has limited value in differentiating COVID-19 patients from non-COVID-19 patients. Through the typical epidemiological history, laboratory examinations, and clinical symptoms, combined with the distributive characteristics of shadows, CCR may be useful to identify patients with possible COVID-19. This will allow for the rapid identification and quarantine of patients.


Assuntos
COVID-19/diagnóstico por imagem , Radiografia Torácica/métodos , Tomografia Computadorizada por Raios X/métodos , Adulto , Idoso , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Valor Preditivo dos Testes , Radiografia Torácica/normas , Tomografia Computadorizada por Raios X/normas
9.
J Int Med Res ; 49(8): 3000605211039791, 2021 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-34463562

RESUMO

OBJECTIVES: To compare the yield of early combined use of chest X-ray (CXR) and chest computed tomography (CT) in patients diagnosed with community-acquired pneumonia (CAP) presenting to the emergency department (ED) and assess the impact of chest CT on the initial diagnosis. METHODS: The medical records of 900 patients who presented to the ED and were diagnosed with CAP over a 1-year period were reviewed, and 130 patients who underwent CXR and chest CT within 48 hours were selected. CXR findings were classified as positive, negative, or inconclusive for CAP. Chest CT findings were defined as positive, negative, inconclusive, or positive with add-on to the CXR findings. CT was classified as having no benefit, large benefit, or moderate benefit based on the chest CT and CXR findings. RESULTS: Chest CT results were positive in 90.7% of patients, with 41.5% being newly diagnosed after negative or inconclusive CXR and 21.5% being diagnosed with add-on to the CXR findings. CT had large, moderate, and no benefit over CXR in diagnosing or excluding CAP in 45.3%, 21.5%, and 33.1% of patients, respectively. CONCLUSION: Early chest CT may be used to compliment CXR in the early diagnosis of CAP among patients in the ED.


Assuntos
Infecções Comunitárias Adquiridas , Pneumonia , Infecções Comunitárias Adquiridas/diagnóstico por imagem , Humanos , Pneumonia/diagnóstico por imagem , Radiografia Torácica , Estudos Retrospectivos , Tomografia Computadorizada por Raios X , Raios X
10.
Sci Rep ; 11(1): 16075, 2021 08 09.
Artigo em Inglês | MEDLINE | ID: mdl-34373530

RESUMO

The new coronavirus unleashed a worldwide pandemic in early 2020, and a fatality rate several times that of the flu. As the number of infections soared, and capabilities for testing lagged behind, chest X-ray (CXR) imaging became more relevant in the early diagnosis and treatment planning for patients with suspected or confirmed COVID-19 infection. In a few weeks, proposed new methods for lung screening using deep learning rapidly appeared, while quality assurance discussions lagged behind. This paper proposes a set of protocols to validate deep learning algorithms, including our ROI Hide-and-Seek protocol, which emphasizes or hides key regions of interest from CXR data. Our protocol allows assessing the classification performance for anomaly detection and its correlation to radiological signatures, an important issue overlooked in several deep learning approaches proposed so far. By running a set of systematic tests over CXR representations using public image datasets, we demonstrate the weaknesses of current techniques and offer perspectives on the advantages and limitations of automated radiography analysis when using heterogeneous data sources.


Assuntos
Algoritmos , COVID-19/diagnóstico , Aprendizado Profundo , Radiografia Torácica/métodos , COVID-19/epidemiologia , COVID-19/virologia , Humanos , Pulmão/diagnóstico por imagem , Pulmão/virologia , Redes Neurais de Computação , Pandemias , Reprodutibilidade dos Testes , SARS-CoV-2/fisiologia , Sensibilidade e Especificidade , Raios X
11.
Sci Rep ; 11(1): 16071, 2021 08 09.
Artigo em Inglês | MEDLINE | ID: mdl-34373554

RESUMO

To speed up the discovery of COVID-19 disease mechanisms by X-ray images, this research developed a new diagnosis platform using a deep convolutional neural network (DCNN) that is able to assist radiologists with diagnosis by distinguishing COVID-19 pneumonia from non-COVID-19 pneumonia in patients based on chest X-ray classification and analysis. Such a tool can save time in interpreting chest X-rays and increase the accuracy and thereby enhance our medical capacity for the detection and diagnosis of COVID-19. The explainable method is also used in the DCNN to select instances of the X-ray dataset images to explain the behavior of training-learning models to achieve higher prediction accuracy. The average accuracy of our method is above 96%, which can replace manual reading and has the potential to be applied to large-scale rapid screening of COVID-9 for widely use cases.


Assuntos
Algoritmos , COVID-19/prevenção & controle , Aprendizado Profundo , Redes Neurais de Computação , Pneumonia/diagnóstico , COVID-19/complicações , COVID-19/virologia , Diagnóstico Diferencial , Humanos , Pneumonia/complicações , Radiografia Torácica/métodos , SARS-CoV-2/fisiologia , Sensibilidade e Especificidade , Raios X
12.
Pan Afr Med J ; 38: 368, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34367447

RESUMO

Cleidocranial Dysostosis or Dysplasia (CCD) is an infrequent clinical condition, with an autosomal dominant hereditary mode of inheritance. Triad lesions: multiple supernumerary teeth, partial or complete absence of the clavicles and open sagittal sutures and fontanelles. Nine-year-old female patient comes to our service for outpatient consultation with the main complaint of upper limbs mobility restriction with shoulders hypermotility. The chest X-ray showed partial absence of the clavicles and a cone-shaped thorax. The diagnosis of CCD was performed. Treatment of these patients requires a multidisciplinary approach which includes orthopaedic and dental corrections. The premature diagnosis allows a proper orientation for the treatment, offering a better life quality for the patient.


Assuntos
Displasia Cleidocraniana/terapia , Assistência Odontológica/métodos , Procedimentos Ortopédicos/métodos , Criança , Displasia Cleidocraniana/diagnóstico por imagem , Feminino , Humanos , Radiografia Torácica
13.
Photodiagnosis Photodyn Ther ; 35: 102473, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34348186

RESUMO

BACKGROUND: The recent emergence of a highly infectious and contagious respiratory viral disease known as COVID-19 has vastly impacted human lives and overloaded the health care system. Therefore, it is crucial to develop a fast and accurate diagnostic system for the timely identification of COVID-19 infected patients and thus to help control its spread. METHODS: This work proposes a new deep CNN based technique for COVID-19 classification in X-ray images. In this regard, two novel custom CNN architectures, namely COVID-RENet-1 and COVID-RENet-2, are developed for COVID-19 specific pneumonia analysis. The proposed technique systematically employs Region and Edge-based operations along with convolution operations. The advantage of the proposed idea is validated by performing series of experimentation and comparing results with two baseline CNNs that exploited either a single type of pooling operation or strided convolution down the architecture. Additionally, the discrimination capacity of the proposed technique is assessed by benchmarking it against the state-of-the-art CNNs on radiologist's authenticated chest X-ray dataset. Implementation is available at https://github.com/PRLAB21/Coronavirus-Disease-Analysis-using-Chest-X-Ray-Images. RESULTS: The proposed classification technique shows good generalization as compared to existing CNNs by achieving promising MCC (0.96), F-score (0.98) and Accuracy (98%). This suggests that the idea of synergistically using Region and Edge-based operations aid in better exploiting the region homogeneity, textural variations, and region boundary-related information in an image, which helps to capture the pneumonia specific pattern. CONCLUSIONS: The encouraging results of the proposed classification technique on the test set with high sensitivity (0.98) and precision (0.98) suggest the effectiveness of the proposed technique. Thus, it suggests the potential use of the proposed technique in other X-ray imagery-based infectious disease analysis.


Assuntos
COVID-19 , Aprendizado Profundo , Fotoquimioterapia , Algoritmos , Humanos , Redes Neurais de Computação , Fotoquimioterapia/métodos , Fármacos Fotossensibilizantes , Radiografia Torácica , SARS-CoV-2 , Raios X
14.
Br J Hosp Med (Lond) ; 82(7): 1-6, 2021 Jul 02.
Artigo em Inglês | MEDLINE | ID: mdl-34338010

RESUMO

Pulmonary embolism remains a common and potentially deadly disease, despite advances in diagnostic imaging, treatment and prevention. Managing pulmonary embolism requires a multifactorial approach involving risk stratification, determining appropriate diagnostics and selecting individualised therapy. The first part of this article reviewed the pathophysiology, risk factors, clinical presentation, diagnostic evaluation and therapeutic management and early outpatient management of pulmonary embolism. This second part summarises pulmonary embolism in the setting of pregnancy, COVID-19, recurrent disease and chronic thromboembolic pulmonary hypertension.


Assuntos
COVID-19/epidemiologia , Embolia Pulmonar/epidemiologia , Embolia Pulmonar/patologia , COVID-19/patologia , Doença Crônica , Feminino , Humanos , Hipertensão Pulmonar/epidemiologia , Hipertensão Pulmonar/patologia , Masculino , Gravidez , Embolia Pulmonar/diagnóstico por imagem , Radiografia Torácica , Recidiva , Fatores de Risco , SARS-CoV-2 , Ultrassonografia Doppler , Tromboembolia Venosa/epidemiologia , Tromboembolia Venosa/patologia
15.
Medicine (Baltimore) ; 100(31): e26841, 2021 Aug 06.
Artigo em Inglês | MEDLINE | ID: mdl-34397855

RESUMO

ABSTRACT: Smear-positive pulmonary tuberculosis (SPPTB) is the major contributor to the spread of tuberculosis (TB) infection, and it creates high morbidity and mortality worldwide. The objective of this study was to determine the predictors of delayed sputum smear conversion at the end of the intensive phase of TB treatment in Kota Kinabalu, Malaysia.This retrospective study was conducted utilising data of SPPTB patients treated in 5 TB treatment centres located in Kota Kinabalu, Malaysia from 2013 to 2018. Pulmonary TB (PTB) patients included in the study were those who had at least completed the intensive phase of anti-TB treatment with sputum smear results at the end of the 2nd month of treatment. The factors associated with delayed sputum smear conversion were analyzed using multiple logistic regression analysis. Predictors of sputum smear conversion at the end of intensive phase were evaluated.A total of 2641 patients from the 2013 to 2018 periods were included in this study. One hundred eighty nine (7.2%) patients were identified as having delayed sputum smear conversion at the end of the intensive phase treatment. Factors of moderate (advanced odd ratio [aOR]: 1.7) and advanced (aOR: 2.7) chest X-ray findings at diagnosis, age range of >60 (aOR: 2.1), year of enrolment 2016 (aOR: 2.8), 2017 (aOR: 3.9), and 2018 (aOR: 2.8), smokers (aOR: 1.5), no directly observed treatment short-course (DOTS) supervisor (aOR: 6.9), non-Malaysian citizens (aOR: 1.5), and suburban home locations (aOR: 1.6) were associated with delayed sputum smear conversion at the end of the intensive phase of the treatment.To improve sputum smear conversion success rate, the early detection of PTB cases has to be fine-tuned so as to reduce late or severe case presentation during diagnosis. Efforts must also be in place to encourage PTB patients to quit smoking. The percentage of patients assigned with DOTS supervisors should be increased while at the same time ensuring that vulnerable groups such as those residing in suburban localities, the elderly and migrant TB patients are provided with proper follow-up treatment and management.


Assuntos
Antituberculosos/uso terapêutico , Tuberculose Latente , Mycobacterium tuberculosis , Escarro/microbiologia , Tuberculose Pulmonar , Assistência ao Convalescente/métodos , Assistência ao Convalescente/normas , Transmissão de Doença Infecciosa/prevenção & controle , Feminino , Humanos , Tuberculose Latente/diagnóstico , Tuberculose Latente/etiologia , Tuberculose Latente/prevenção & controle , Malásia/epidemiologia , Masculino , Pessoa de Meia-Idade , Mycobacterium tuberculosis/efeitos dos fármacos , Mycobacterium tuberculosis/isolamento & purificação , Determinação de Necessidades de Cuidados de Saúde , Radiografia Torácica/métodos , Radiografia Torácica/estatística & dados numéricos , Estudos Retrospectivos , Medição de Risco , Fatores de Risco , Tuberculose Pulmonar/epidemiologia , Tuberculose Pulmonar/microbiologia , Tuberculose Pulmonar/terapia , Tuberculose Pulmonar/transmissão
16.
Int J Med Sci ; 18(14): 3140-3149, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34400884

RESUMO

Background: Coronavirus disease 2019 (COVID-19) has caused over 3.8 million deaths globally. Up to date, the number of death in 2021 is more than that in 2020 globally. Here, we aimed to compare clinical characteristics of deceased patients and recovered patients, and analyze the risk factors of death to help reduce mortality of COVID-19. Methods: In this retrospective study, a total of 2719 COVID-19 patients were enrolled, including 109 deceased patients and 2610 recovered patients. Medical records of all patients were collected between February 4, 2020, and April 7, 2020. Clinical characteristics, laboratory indices, treatments, and deep-learning system- assessed lung lesion volumes were analyzed. The effect of different medications on survival time of fatal cases was also investigated. Results: The deceased patients were older (73 years versus 60 years) and had a male predominance. Nausea (10.1% versus 4.1%) and dyspnea (54.1% versus 39.2%) were more common in deceased patients. The proportion of patients with comorbidities in deceased patients was significantly higher than those in recovered patients. The median times from hospital admission to outcome in deceased patients and recovered patients were 9 days and 13 days, respectively. Patients with severe or critical COVID-19 were more frequent in deceased group. Leukocytosis (11.35×109/L versus 5.60×109/L) and lymphocytopenia (0.52×109/L versus 1.58×109/L) were shown in patients who died. The level of prothrombin time, activated partial prothrombin time, D-dimer, aspartate aminotransferase, alanine aminotransferase, urea, creatinine, creatine kinase, glucose, brain natriuretic peptide, and inflammatory indicators were significantly higher in deceased patients than in recovered patients. The volumes of ground-glass, consolidation, total lesions and total lung in all patients were quantified. Complications were more common in deceased patients than in recovered patients; respiratory failure (57.8%), septic shock (36.7%), and acute respiratory distress syndrome (26.6%) were the most common complications in patients who died. Many treatments were more frequent in deceased patients, such as antibiotic therapy (88.1% versus 53.7%), glucocorticoid treatment (70.6% versus 11.0%), intravenous immunoglobin treatment (36.6% versus 4.9%), invasive mechanical ventilation (62.3% versus 3.8%). Antivirals, antibiotics, traditional Chinese medicines and glucocorticoid treatment may significantly increase the survival time of fatal cases. Quantitative computed tomography imaging results were correlated with biochemical markers. Conclusions: Most patients with fatal outcomes were more likely to have common comorbidities. The leading causes of death were respiratory failure and multiple organ dysfunction syndrome. Acute respiratory distress syndrome, respiratory failure and septic shock were the most common serious complications. Antivirals, antibiotics, traditional Chinese medicines, and glucocorticoid treatment may prolong the survival time of deceased patients with COVID-19.


Assuntos
COVID-19/mortalidade , Idoso , Idoso de 80 Anos ou mais , COVID-19/sangue , COVID-19/complicações , COVID-19/terapia , China/epidemiologia , Feminino , Humanos , Pacientes Internados/estatística & dados numéricos , Masculino , Pessoa de Meia-Idade , Radiografia Torácica , Estudos Retrospectivos , Análise de Sobrevida
18.
Comput Math Methods Med ; 2021: 5528144, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34194535

RESUMO

Pneumonia is an infamous life-threatening lung bacterial or viral infection. The latest viral infection endangering the lives of many people worldwide is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19. This paper is aimed at detecting and differentiating viral pneumonia and COVID-19 disease using digital X-ray images. The current practices include tedious conventional processes that solely rely on the radiologist or medical consultant's technical expertise that are limited, time-consuming, inefficient, and outdated. The implementation is easily prone to human errors of being misdiagnosed. The development of deep learning and technology improvement allows medical scientists and researchers to venture into various neural networks and algorithms to develop applications, tools, and instruments that can further support medical radiologists. This paper presents an overview of deep learning techniques made in the chest radiography on COVID-19 and pneumonia cases.


Assuntos
Teste para COVID-19/métodos , COVID-19/diagnóstico por imagem , Aprendizado Profundo , SARS-CoV-2 , Algoritmos , COVID-19/diagnóstico , Teste para COVID-19/estatística & dados numéricos , Biologia Computacional , Diagnóstico Diferencial , Humanos , Conceitos Matemáticos , Redes Neurais de Computação , Pneumonia Viral/diagnóstico , Pneumonia Viral/diagnóstico por imagem , Radiografia Torácica/estatística & dados numéricos , Tomografia Computadorizada por Raios X/estatística & dados numéricos
19.
J Healthc Eng ; 2021: 5513679, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34194681

RESUMO

The world is experiencing an unprecedented crisis due to the coronavirus disease (COVID-19) outbreak that has affected nearly 216 countries and territories across the globe. Since the pandemic outbreak, there is a growing interest in computational model-based diagnostic technologies to support the screening and diagnosis of COVID-19 cases using medical imaging such as chest X-ray (CXR) scans. It is discovered in initial studies that patients infected with COVID-19 show abnormalities in their CXR images that represent specific radiological patterns. Still, detection of these patterns is challenging and time-consuming even for skilled radiologists. In this study, we propose a novel convolutional neural network- (CNN-) based deep learning fusion framework using the transfer learning concept where parameters (weights) from different models are combined into a single model to extract features from images which are then fed to a custom classifier for prediction. We use gradient-weighted class activation mapping to visualize the infected areas of CXR images. Furthermore, we provide feature representation through visualization to gain a deeper understanding of the class separability of the studied models with respect to COVID-19 detection. Cross-validation studies are used to assess the performance of the proposed models using open-access datasets containing healthy and both COVID-19 and other pneumonia infected CXR images. Evaluation results show that the best performing fusion model can attain a classification accuracy of 95.49% with a high level of sensitivity and specificity.


Assuntos
COVID-19/diagnóstico por imagem , Aprendizado Profundo , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Radiografia Torácica/métodos , Humanos , Pulmão/diagnóstico por imagem , SARS-CoV-2 , Sensibilidade e Especificidade
20.
Intern Med ; 60(18): 2911-2917, 2021 Sep 15.
Artigo em Inglês | MEDLINE | ID: mdl-34275978

RESUMO

Objective Severe acute respiratory syndrome coronavirus 2 has spread globally, and it is important to utilize medical resources properly, especially in critically ill patients. We investigated the validity of chest radiography as a tool for predicting aggravation in coronavirus disease (COVID-19) cases. Methods A total of 104 laboratory-confirmed COVID-19 cases were referred from the cruise ship "Diamond Princess" to the Self-Defense Forces Central Hospital in Japan from February 11 to 25, 2020. Fifty-nine symptomatic patients were selected. Chest radiography was performed upon hospitalization; subsequently, patients were categorized into the positive radiograph (Group A) and negative radiograph (Group B) groups. Radiographic findings were analyzed with a six-point semiquantitative score. Group A was further classified into two additional subgroups: patients who required oxygen therapy during their clinical courses (Group C) and patients who did not (Group D). Clinical records, laboratory data, and radiological findings were collected for an analysis. Results Among 59 patients, 34 were men with a median age of 60 years old. Groups A, B, C, and D consisted of 33, 26, 12, and 21 patients, respectively. The number of patients requiring oxygen administration was significantly larger in Group A than in Group B. The consolidation score on chest radiographs was significantly higher in Group C than in Group D. When chest radiographs showed consolidation in more than two lung fields, the positive likelihood ratio of deterioration was 10.6. Conclusions Chest radiography is a simple and easy-to-use clinic-level triage tool for predicting the severity of COVID-19 and may contribute to the allocation of medical resources.


Assuntos
COVID-19 , Triagem , Humanos , Masculino , Pessoa de Meia-Idade , Atenção Primária à Saúde , Radiografia , Radiografia Torácica , Estudos Retrospectivos , SARS-CoV-2
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