Chest X-ray and CT findings of early H7N9 avian influenza cases

Lin, Zhi Qian; Xu, Xue Qin; Zhang, Ke Bei; Zhuang, Zhi Guo; Liu, Xiao Sheng; Li, Zhao; Lin, Chang Yang; Yang, Li; Hua, Xiao Lan; Zhao, Hui; Xu, Jian

doi:10.1177/0284185114535209

Cited by 13 publications

(7 citation statements)

References 9 publications

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“…In other viral diseases that affects breathing, such as influenza or SARS, the damage produced to the lungs can be observed using pulmonary X-ray images. The works presented in Reference [11,12] detailed these effects in influenza, while other works like Reference [13,14] explain the effects in SARS patients.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning System for COVID-19 Diagnosis Aid Using X-ray Pulmonary Images

et al. 2020

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The spread of the SARS-CoV-2 virus has made the COVID-19 disease a worldwide epidemic. The most common tests to identify COVID-19 are invasive, time consuming and limited in resources. Imaging is a non-invasive technique to identify if individuals have symptoms of disease in their lungs. However, the diagnosis by this method needs to be made by a specialist doctor, which limits the mass diagnosis of the population. Image processing tools to support diagnosis reduce the load by ruling out negative cases. Advanced artificial intelligence techniques such as Deep Learning have shown high effectiveness in identifying patterns such as those that can be found in diseased tissue. This study analyzes the effectiveness of a VGG16-based Deep Learning model for the identification of pneumonia and COVID-19 using torso radiographs. Results show a high sensitivity in the identification of COVID-19, around 100%, and with a high degree of specificity, which indicates that it can be used as a screening test. AUCs on ROC curves are greater than 0.9 for all classes considered.

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Section: Introductionmentioning

confidence: 99%

Deep Learning System for COVID-19 Diagnosis Aid Using X-ray Pulmonary Images

et al. 2020

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“…In a study that involved 16 severe H7N9 infection cases, nine patients showed a low level of pleural effusion. Five suffered from moderate pleural effusion, four had mild seroperitoneum, and seven experienced mild pericardial effusion (7). In critical cases of influenza A virus subtype H1N1 (A/H1N1) infection, the imaging features include GGO, plaque-like shadows, massive consolidation of the lungs, pleural effusion, and pneumothorax (for patients who require mechanical ventilation) (8).…”

Section: Discussionmentioning

confidence: 99%

COVID-19 with pleural effusion as the initial symptom: a case study analysis

Zhang¹,

Chai²,

Li³

et al. 2020

Ann Palliat Med

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a newly identified strain of coronavirus in the human body and was reported in Wuhan at the end of 2019. So far, the epidemic is continuing and very serious, with the number of infections and deaths increasing. Despite active investigations around the world to better understand the dynamics of transmission and the scope of clinical disease, COVID-19 continues to spread rapidly from person to person. The common signs and symptoms of SARS-CoV-2 infection include fever, fatigue, dry cough, and dyspnea; in severe cases, patients may have acute respiratory distress syndrome, septic shock, metabolic acidosis difficult to treat and coagulation disorder. However, some patients who test positive for SARS-CoV-2 in their respiratory tract may not have such clinical signs and symptoms. This report presents a case study analysis of a patient admitted in the Fourth Taiyuan People's Hospital, who had suffered traumatic injuries from a car accident and survived COVID-19, with pleural effusion as the initial symptom. We report a case of 2019-NCOV with pleural effusion as the first symptom. Describe in detail the differential diagnosis, diagnosis, clinical management, and cure of this case. In order to combat the novel CoronaviruscoVID-19 in the process to provide lessons and help.

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“…This difficulty may impair screening efficacy and work-up efforts. Chest X-rays (CXRs) are often considered as the first step in medical examinations of organs in the chest, as it's quickly and easily obtained besides lower dose of radiation exposure [12]. The presentation of PH in CXRs include enlargement of the central pulmonary arteries, with or without rapid tapering (pruning), and right heart chamber enlargement [13,14].…”

Section: Introductionmentioning

confidence: 99%

A promising approach for screening pulmonary hypertension based on frontal chest radiographs using deep learning: A retrospective study

Zou

Ren²,

Feng

et al. 2020

PLoS ONE

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Background To date, the missed diagnosis rate of pulmonary hypertension (PH) was high, and there has been limited development of a rapid, simple, and effective way to screen the disease. The purpose of this study is to develop a deep learning approach to achieve rapid detection of possible abnormalities in chest radiographs suggesting PH for screening patients suspected of PH. Methods We retrospectively collected frontal chest radiographs and the pulmonary artery systolic pressure (PASP) value measured by Doppler transthoracic echocardiography from 762 patients (357 healthy controls and 405 with PH) from three institutes in China from January 2013 to May 2019. The wohle sample comprised 762 images (641 for training, 80 for internal test, and 41 for external test). We firstly performed a 8-fold cross-validation on the 641 images selected for training (561 for pre-training, 80 for validation), then decided to tune learning rate to 0.0008 according to the best score on validation data. Finally, we used all the pre-training and validation data (561+80 = 641) to train our models (Resnet50, Xception, and Inception V3), evaluated them on internal and external test dataset to classify the images as having manifestations of PH or healthy according to the area under the receiver operating characteristic curve (AUC/ROC). After that, the three deep learning models were further used for prediction of PASP using regression algorithm. Moreover, we invited an experienced chest radiologist to classify the images in the test dataset as having PH or not, and compared the prediction accuracy performed by deep learing models with that of manual classification.

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Chest X-ray and CT findings of early H7N9 avian influenza cases

Cited by 13 publications

References 9 publications

Deep Learning System for COVID-19 Diagnosis Aid Using X-ray Pulmonary Images

Deep Learning System for COVID-19 Diagnosis Aid Using X-ray Pulmonary Images

COVID-19 with pleural effusion as the initial symptom: a case study analysis

A promising approach for screening pulmonary hypertension based on frontal chest radiographs using deep learning: A retrospective study

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