thoracic diseases
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2021 ◽  
Author(s):  
Rebeca Gregorio-Hernández ◽  
Alba Pérez-Pérez ◽  
Almudena Alonso-Ojembarrena ◽  
María Arriaga-Redondo ◽  
Cristina Ramos-Navarro ◽  
...  

Abstract Neonatal pneumothorax (NP) is a potentially life-threatening condition. Lung ultrasound (LU) has shown higher sensitivity and specificity in diagnosis compared to x-rays, but evidence regarding its usefulness in complex NP is lacking. We report four neonates suffering from cardiac or esophagueal malformations who developed lateral and/or posterior pneumothoraces, in which LU helped, making NP diagnosis and management easier and faster. In conclusion, LU is an easy-to-use, fast, simple and accurate tool when evaluating newborns with complex thoracic diseases.


2021 ◽  
Author(s):  
Hieu Huy Pham ◽  
Ha Q. Nguyen ◽  
Khanh Lam ◽  
Linh T. Le ◽  
Dung B. Nguyen ◽  
...  

Interpretation of chest radiographs (CXR) is a difficult but essential task for detecting thoracic abnormalities. Recent artificial intelligence (AI) algorithms have achieved radiologist-level performance on various medical classification tasks. However, only a few studies addressed the localization of abnormal findings from CXR scans, which is essential in explaining the image-level classification to radiologists. Additionally, the actual impact of AI algorithms on the diagnostic performance of radiologists in clinical practice remains relatively unclear. To bridge these gaps, we developed an explainable deep learning system called VinDr-CXR that can classify a CXR scan into multiple thoracic diseases and, at the same time, localize most types of critical findings on the image. VinDr-CXR was trained on 51,485 CXR scans with radiologist-provided bounding box annotations. It demonstrated a comparable performance to experienced radiologists in classifying 6 common thoracic diseases on a retrospective validation set of 3,000 CXR scans, with a mean area under the receiver operating characteristic curve (AUROC) of 0.967 (95% confidence interval [CI]: 0.958-0.975). The sensitivity, specificity, F1-score, false-positive rate (FPR), and false-negative rate (FNR) of the system at the optimal cutoff value were 0.933 (0.898-0.964), 0.900 (0.887-0.911), 0.631 (0.589-0.672), 0.101 (0.089-0.114) and 0.067 (0.057-0.102), respectively. For the localization task with 14 types of lesions, our free-response receiver operating characteristic (FROC) analysis showed that the VinDr-CXR achieved a sensitivity of 80.2% at the rate of 1.0 false-positive lesion identified per scan. A prospective study was also conducted to measure the clinical impact of the VinDr-CXR in assisting six experienced radiologists. The results indicated that the proposed system, when used as a diagnosis supporting tool, significantly improved the agreement between radiologists themselves with an increase of 1.5% in mean Fleiss' Kappa. We also observed that, after the radiologists consulted VinDr-CXR's suggestions, the agreement between each of them and the system was remarkably increased by 3.3% in mean Cohen's Kappa. Altogether, our results highlight the potentials of the proposed deep learning system as an effective assistant to radiologists in clinical practice. Part of the dataset used for developing the VinDr-CXR system has been made publicly available at https://physionet.org/content/vindr-cxr/1.0.0/.


2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Lara Walkoff ◽  
Marianna Zagurovskaya
Keyword(s):  

2021 ◽  
Author(s):  
Thanh T. Tran ◽  
Hieu H. Pham ◽  
Thang V. Nguyen ◽  
Tung T. Le ◽  
Hieu T. Nguyen ◽  
...  

Chest radiograph (CXR) interpretation is critical for the diagnosis of various thoracic diseases in pediatric patients. This task, however, is error-prone and requires a high level of understanding of radiologic expertise. Recently, deep convolutional neural networks (D-CNNs) have shown remarkable performance in interpreting CXR in adults. However, there is a lack of evidence indicating that D-CNNs can recognize accurately multiple lung pathologies from pediatric CXR scans. In particular, the development of diagnostic models for the detection of pediatric chest diseases faces significant challenges such as (i) lack of physician-annotated datasets and (ii) class imbalance problems. In this paper, we retrospectively collect a large dataset of 5,017 pediatric CXR scans, for which each is manually labeled by an experienced radiologist for the presence of 10 common pathologies. A D-CNN model is then trained on 3,550 annotated scans to classify multiple pediatric lung pathologies automatically. To address the high-class imbalance issue, we propose to modify and apply "Distribution-Balanced loss" for training D-CNNs which reshapes the standard Binary-Cross Entropy loss (BCE) to efficiently learn harder samples by down-weighting the loss assigned to the majority classes. On an independent test set of 777 studies, the proposed approach yields an area under the receiver operating characteristic (AUC) of 0.709 (95% CI, 0.690-0.729). The sensitivity, specificity, and F1-score at the cutoff value are 0.722 (0.694-0.750), 0.579 (0.563-0.595), and 0.389 (0.373-0.405), respectively. These results significantly outperform previous state-of-the-art methods on most of the target diseases. Moreover, our ablation studies validate the effectiveness of the proposed loss function compared to other standard losses, e.g., BCE and Focal Loss, for this learning task. Overall, we demonstrate the potential of D-CNNs in interpreting pediatric CXRs.


2021 ◽  
Vol 8 (8) ◽  
pp. 2272
Author(s):  
Mehmet Degirmenci ◽  
Celal Kus

Background: Tobacco can make thoracic diseases more complicated by affecting their respiratory functions. Smoking causes many diseases that require surgical treatment and affects surgical results. The aim of the study was to determine the relationship between tobacco use and post-operative complications in thoracic surgery patients and contribute to public health.Methods: In this study, 754 patients were evaluated retrospectively. Patient characteristics and tobacco use habits of the patients were determined. Postoperative complications, admission to the intensive therapy unit, intubation, death, and length of stay in hospital were defined as surgical outcomes. These results were compared and analyzed with tobacco use.Results: The patients consisted of 536 (71.1%) men and 218 (28.9%) women. Tobacco use was more common in men (X2=223.216, p<0.001) and younger ages (X2=45.342, p<0.001). Complications occurred in 96 patients, 76 (79.2%) of whom used tobacco. Tobacco use (p<0.001, OR=3.547), ASA score (p=0.029, OR=2.004), major surgeries (p<0.001, OR=4.458), and minimally invasive surgeries (p=0.027, OR=2.323) are associated with complications. Length of hospital stay is related to the amount of tobacco (p<0.001, OR=3.706), size of surgery (p<0.001, OR=14.797), over 65 years (p<0.001, OR=2.635), and infectious diseases (p=0.039, OR=1.939).Conclusions: Tobacco use is related to poor outcomes in thoracic surgery patients, and it is a severe health problem, especially at young ages. Tobacco control programs should be supported to prevent the effects of tobacco use on thoracic diseases and postoperative complications.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jiansheng Fang ◽  
Yanwu Xu ◽  
Yitian Zhao ◽  
Yuguang Yan ◽  
Junling Liu ◽  
...  

Abstract Background Chest X-rays are the most commonly available and affordable radiological examination for screening thoracic diseases. According to the domain knowledge of screening chest X-rays, the pathological information usually lay on the lung and heart regions. However, it is costly to acquire region-level annotation in practice, and model training mainly relies on image-level class labels in a weakly supervised manner, which is highly challenging for computer-aided chest X-ray screening. To address this issue, some methods have been proposed recently to identify local regions containing pathological information, which is vital for thoracic disease classification. Inspired by this, we propose a novel deep learning framework to explore discriminative information from lung and heart regions. Result We design a feature extractor equipped with a multi-scale attention module to learn global attention maps from global images. To exploit disease-specific cues effectively, we locate lung and heart regions containing pathological information by a well-trained pixel-wise segmentation model to generate binarization masks. By introducing element-wise logical AND operator on the learned global attention maps and the binarization masks, we obtain local attention maps in which pixels are are 1 for lung and heart region and 0 for other regions. By zeroing features of non-lung and heart regions in attention maps, we can effectively exploit their disease-specific cues in lung and heart regions. Compared to existing methods fusing global and local features, we adopt feature weighting to avoid weakening visual cues unique to lung and heart regions. Our method with pixel-wise segmentation can help overcome the deviation of locating local regions. Evaluated by the benchmark split on the publicly available chest X-ray14 dataset, the comprehensive experiments show that our method achieves superior performance compared to the state-of-the-art methods. Conclusion We propose a novel deep framework for the multi-label classification of thoracic diseases in chest X-ray images. The proposed network aims to effectively exploit pathological regions containing the main cues for chest X-ray screening. Our proposed network has been used in clinic screening to assist the radiologists. Chest X-ray accounts for a significant proportion of radiological examinations. It is valuable to explore more methods for improving performance.


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