Searching for pneumothorax in x-ray images using autoencoded deep features

A deep learning-based algorithm for pulmonary tuberculosis detection in chest radiography

In tuberculosis (TB), chest radiography (CXR) patterns are highly variable, mimicking pneumonia and many other diseases. This study aims to evaluate the efficacy of Google teachable machine, a deep neural network-based image classification tool, to develop algorithm for predicting TB probability of CXRs. The training dataset included 348 TB CXRs and 3806 normal CXRs for training TB detection. We also collected 1150 abnormal CXRs and 627 normal CXRs for training abnormality detection. For external validation, we collected 250 CXRs from our hospital. We also compared the accuracy of the algorithm to five pulmonologists and radiological reports. In external validation, the AI algorithm showed areas under the curve (AUC) of 0.951 and 0.975 in validation dataset 1 and 2. The accuracy of the pulmonologists on validation dataset 2 showed AUC range of 0.936-0.995. When abnormal CXRs other than TB were added, AUC decreased in both human readers (0.843-0.888) and AI algorithm (0.828). When combine human readers with AI algorithm, the AUC further increased to 0.862-0.885. The TB CXR AI algorithm developed by using Google teachable machine in this study is effective, with the accuracy close to experienced clinical physicians, and may be helpful for detecting tuberculosis by CXR.

“Quo Vadis Diagnosis”: Application of Informatics in Early Detection of Pneumothorax

A pneumothorax is a condition that occurs in the lung region when air enters the pleural space—the area between the lung and chest wall—causing the lung to collapse and making it difficult to breathe. This can happen spontaneously or as a result of an injury. The symptoms of a pneumothorax may include chest pain, shortness of breath, and rapid breathing. Although chest X-rays are commonly used to detect a pneumothorax, locating the affected area visually in X-ray images can be time-consuming and prone to errors. Existing computer technology for detecting this disease from X-rays is limited by three major issues, including class disparity, which causes overfitting, difficulty in detecting dark portions of the images, and vanishing gradient. To address these issues, we propose an ensemble deep learning model called PneumoNet, which uses synthetic images from data augmentation to address the class disparity issue and a segmentation system to identify dark areas. Finally, the issue of the vanishing gradient, which becomes very small during back propagation, can be addressed by hyperparameter optimization techniques that prevent the model from slowly converging and poorly performing. Our model achieved an accuracy of 98.41% on the Society for Imaging Informatics in Medicine pneumothorax dataset, outperforming other deep learning models and reducing the computation complexities in detecting the disease.

Detection of acute thoracic aortic dissection based on plain chest radiography and a residual neural network (Resnet)

Acute thoracic aortic dissection is a life-threatening disease, in which blood leaking from the damaged inner layer of the aorta causes dissection between the intimal and adventitial layers. The diagnosis of this disease is challenging. Chest x-rays are usually performed for initial screening or diagnosis, but the diagnostic accuracy of this method is not high. Recently, deep learning has been successfully applied in multiple medical image analysis tasks. In this paper, we attempt to increase the accuracy of diagnosis of acute thoracic aortic dissection based on chest x-rays by applying deep learning techniques. In aggregate, 3,331 images, comprising 716 positive images and 2615 negative images, were collected from 3,331 patients. Residual neural network 18 was used to detect acute thoracic aortic dissection. The diagnostic accuracy of the ResNet18 was observed to be 90.20% with a precision of 75.00%, recall of 94.44%, and F1-score of 83.61%. Further research is required to improve diagnostic accuracy based on aorta segmentation.

A case report of secondary spontaneous pneumothorax in acute exacerbation of COPD managed with improvised chest tube drain

Introduction

and importance: We reported a case of secondary spontaneous pneumothorax (SSP) in a 70-years-old male with acute exacerbation of COPD (AE COPD) managed with improvised chest tube drain (ICD).

Case presentation

He presented with sudden onset breathlessness and oxygen saturation of 78%. With prolonged expiration on auscultation, he was treated as AE COPD with oxygen therapy, nebulization with albuterol/ipratropium, and injectable antibiotics and steroids. The patient was not improving with treatment on third day, and non-critical respiratory distress continued. Considering the alternative diagnosis, the chest X-ray was done which revealed right sided spontaneous pneumothorax and COPD. Due to his reluctancy to go to higher center for chest tube insertion during ongoing COVID-19 pandemic, we inserted ICD (intravenous set put in saline bottle) at our primary care. Following drainage, breathlessness improved and saturation increased. Then inpatient symptomatic treatment for COPD was continued for three more days. He was discharged on inhalers after fifth day and asked for follow up after 10 days. He came after 1 month and on repeat chest X-ray, his right sided pneumothorax resolved completely and COPD was in control with inhaled medications. There was no recurrence of pneumothorax in five months follow up.

Clinical discussion

ICD is a safe, and an alternative option in resource limited setting. However, the guidelines recommend chest tube insertion as appropriate treatment.

Conclusion

This would remind the physicians to anticipate the alternative possibility, and to re-examine those with AE COPD who are not improving as expected with oxygen and nebulization therapy.

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High level of clinical acumen is required to early anticipate the spontaneous pneumothorax in acute exacerbation of COPD.

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We should be vigilant and re-examine the cases of acute exacerbation of COPD, not improving with oxygen and nebulization.

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Improvised chest drain is an alternative option for treating secondary spontaneous pneumothorax.

Deep multi-instance transfer learning for pneumothorax classification in chest X-ray images

PURPOSE

Pneumothorax is a life-threatening emergency that requires immediate treatment. Frontal-view chest X-ray images are typically used for pneumothorax detection in clinical practice. However, manual review of radiographs is time-consuming, labor-intensive, and highly dependent on the experience of radiologists, which may lead to misdiagnosis. Here, we aim to develop a reliable automatic classification method to assist radiologists in rapidly and accurately diagnosing pneumothorax in frontal chest radiographs.

METHODS

A novel residual neural network (ResNet)-based two-stage deep-learning strategy is proposed for pneumothorax identification: local feature learning (LFL) followed by global multi-instance learning (GMIL). Most of the nonlesion regions in the images are removed for learning discriminative features. Two datasets are used for large-scale validation: a private dataset (27 955 frontal-view chest X-ray images) and a public dataset (the National Institutes of Health [NIH] ChestX-ray14; 112 120 frontal-view X-ray images). The model performance of the identification was evaluated using the accuracy, precision, recall, specificity, F1-score, receiver operating characteristic (ROC), and area under ROC curve (AUC). Fivefold cross-validation is conducted on the datasets, and then the mean and standard deviation of the above-mentioned metrics are calculated to assess the overall performance of the model.

RESULTS

The experimental results demonstrate that the proposed learning strategy can achieve state-of-the-art performance on the NIH dataset with an accuracy, AUC, precision, recall, specificity, and F1-score of 94.4% ± 0.7%, 97.3% ± 0.5%, 94.2% ± 0.3%, 94.6% ± 1.5%, 94.2% ± 0.4%, and 94.4% ± 0.7%, respectively.

CONCLUSIONS

The experimental results demonstrate that our proposed CAD system is an efficient assistive tool in the identification of pneumothorax.