A Method to Discriminate Pulmonary Contusion Severity Through Analysis of Hounsfield Unit Frequency

2007 ◽  
Author(s):  
F. Scott Gayzik ◽  
Melissa Daly ◽  
Joel Stitzel
Keyword(s):  
Three Dimensional ◽  
Hounsfield Unit ◽  
Pulmonary Contusion ◽  
Thoracic Injury ◽  
Strong Positive Correlation ◽  
X Rays ◽  
Novel Approach ◽  
Chest X Ray ◽  
Predicting Outcome

This study presents a novel approach for the quantification and classification of pulmonary contusion (PC). PC is a common thoracic injury, affecting up to 25% of patients sustaining blunt chest trauma. [1] Contusion volume at the time of hospitalization has been shown to be an independent predictor for the development of Acute Respiratory Distress Syndrome (ARDS), with the risk of ARDS increasing sharply with PC in excess of 20% by volume. [1] Despite the frequency of the injury and strong positive correlation between contusion volume and outcome, there are relatively few contusion quantification methods in the current literature. One such study utilized chest x-ray film to score PC according the amount of lung appearing to be damaged. [2] The study concluded that despite the limitations in using chest x-rays, a PC scoring system may be of value in determining the need for ventilator assistance and predicting outcome. A potentially more accurate approach to quantifying the severity of PC is through the use of computed tomography (CT) chest scans. CT is the preferred modality for obtaining volumetric pulmonary contusion data since the complete three-dimensional lung anatomy is captured. In this work a semi-automated approach is used to analyze PC in an isolated model of lung contusion in the rat. [3, 4] The CT-based approach enables the PC to be precisely quantified as the lesion progresses in time. The technique distinguishes the severity of the contusion by analyzing the composition of bands in the Hounsfield Unit (HU) range of lung image masks.

scholarly journals COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization

2020 ◽  
Author(s):  
Tahmina Zebin ◽  
Shahadate Rezvy ◽  
Wei Pang
Keyword(s):  
Disease Progression ◽  
Detection Accuracy ◽  
X Rays ◽  
Automated Classification ◽  
X Ray ◽  
Feature Extractor ◽  
Chest X Ray ◽  
Activation Mapping ◽  
Activation Map

Abstract Chest X-rays are playing an important role in the testing and diagnosis of COVID-19 disease in the recent pandemic. However, due to the limited amount of labelled medical images, automated classification of these images for positive and negative cases remains the biggest challenge in their reliable use in diagnosis and disease progression. We applied and implemented a transfer learning pipeline for classifying COVID-19 chest X-ray images from two publicly available chest X-ray datasets {https://github.com/ieee8023/covid-chestxray-dataset},{https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia}}. The classifier effectively distinguishes inflammation in lungs due to COVID-19 and pneumonia (viral and bacterial) from the ones with no infection (normal). We have used multiple pre-trained convolutional backbones as the feature extractor and achieved an overall detection accuracy of 91.2% , 95.3%, 96.7% for the VGG16, ResNet50 and EfficientNetB0 backbones respectively. Additionally, we trained a generative adversarial framework (a cycleGAN) to generate and augment the minority COVID-19 class in our approach. For visual explanations and interpretation purposes, we visualized the regions of input that are important for predictions and a gradient class activation mapping (Grad-CAM) technique is used in the pipeline to produce a coarse localization map of the highlighted regions in the image. This activation map can be used to monitor affected lung regions during disease progression and severity stages.

scholarly journals Optimizing Chest X-ray Indication in Blunt Trauma Patients using Clinical Criteria

2017 ◽  
Vol 6 (1) ◽  
pp. 30-34
Author(s):  
Luca GA Pivetta ◽  
Cristiano Below ◽  
Giovanna Z Rondini ◽  
Jacqueline AG Perlingero ◽  
José C Assef ◽  
...  
Keyword(s):  
Blunt Trauma ◽  
Thoracic Injury ◽  
Trauma Patients ◽  
X Rays ◽  
Chest Drainage ◽  
X Ray ◽  
Clinical Criteria ◽  
Chest X Ray ◽  
Low Probability ◽  
Excessive Number

ABSTRACT Background There is an excessive number of unnecessary chest X-rays (CXRs) in minor blunt trauma patients. Objective To identify, using routine clinical criteria, a subgroup of blunt trauma patients that do not require CXR for assessment. Materials and methods This was a retrospective analysis of trauma registry data collected over a 24-month period. Adult blunt trauma patients undergoing CXR on admission were analyzed. The following clinical criteria were assessed: Normal neurologic examination on admission (NNEx), hemodynamic stability (HS), normal physical examination of the chest on admission (NCEx), age ≤ 60 years, and absence of distracting injuries (Abbreviated Injury Scale >2 in head, abdomen, and extremities). These clinical criteria were progressively merged to select a group with lowest risk of exhibiting abnormal CXR on admission. Results Out of 4,647 patients submitted to CXR on admission, 268 (5.7%) had abnormal findings on scans. Of 2,897 patients admitted with NNEx, 116 (4.0%) had abnormal CXR. Of 2,426 patients with NNEx and HS, 74 (3.0%) had abnormal CXR. Of 1,698 patients with NNEx, HS, and NCEx, 24 (1.4%) had abnormal CXR. Of 1,347 patients with NNEx, HS, NCEx, and age < 60 years, 12 had thoracic injury (0.9% of total individuals receiving CXR). A total of 4 patients underwent chest drainage. Among 1,140 cases with all clinical criteria, 8 had confirmed thoracic injuries and 2 underwent chest drainage. Conclusion A subgroup of blunt trauma patients with low probability of exhibiting abnormalities on CXR at admission was identified. The need for CXR in this subgroup should be reviewed. How to cite this article Pivetta LGA, Parreira JG, Below C, Rondini GZ, Perlingero JAG, Assef JC. Optimizing Chest X-ray Indication in Blunt Trauma Patients using Clinical Criteria. Panam J Trauma Crit Care Emerg Surg 2017;6(1):30-34.

scholarly journals COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization

2020 ◽  
Author(s):  
Tahmina Zebin ◽  
Shahadate Rezvy
Keyword(s):  
Disease Progression ◽  
Input Image ◽  
Mapping Technique ◽  
Detection Accuracy ◽  
X Rays ◽  
Automated Classification ◽  
X Ray ◽  
Chest X Ray ◽  
Activation Mapping

Abstract Chest X-rays are playing an important role in the testing and diagnosis of COVID-19 disease in the recent pandemic. However, due to the limited amount of labelled medical images, automated classification of these images for positive and negative cases remains the biggest challenge in their reliable use in diagnosis and disease progression. We implemented a transfer learning pipeline for classifying COVID-19 chest X-ray images from two publicly available chest X-ray datasets1,2. The classifier effectively distinguishes inflammation in lungs due to COVID-19 and Pneumonia from the ones with no infection (normal). We have used multiple pre-trained convolutional backbones as the feature extractor and achieved an overall detection accuracy of 90%, 94.3%, and 96.8% for the VGG16, ResNet50, and EfficientNetB0 backbones respectively. Additionally, we trained a generative adversarial framework (a CycleGAN) to generate and augment the minority COVID-19 class in our approach. For visual explanations and interpretation purposes, we implemented a gradient class activation mapping technique to highlight the regions of the input image that are important for predictions. Additionally, these visualizations can be used to monitor the affected lung regions during disease progression and severity stages.

scholarly journals A Novel Approach for Multi-Label Chest X-Ray Classification of Common Thorax Diseases

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 64279-64288 ◽  
Author(s):  
Imane Allaouzi ◽  
Mohamed Ben Ahmed
Keyword(s):  
X Ray ◽  
Novel Approach ◽  
Chest X Ray

scholarly journals Effective Survey on Detection and Classification of COVID-19 Suspected Individual Using CT scan Images

2021 ◽  
pp. 294-299
Author(s):  
Snehal R. Sambhe ◽  
Dr. Kamlesh A. Waghmare
Keyword(s):  
Ct Scan ◽  
X Rays ◽  
Rt Pcr ◽  
X Ray ◽  
Feature Extractor ◽  
Chest X Ray ◽  
Coronavirus Infection ◽  
Aided Diagnosis ◽  
Excessive Risk

As insufficient testing kits are available, the development of new testing kits for detecting COVID remains an open vicinity of research. It’s impossible to test each and every patient suffering from coronavirus symptoms using the traditional method i.e. RT-PCR. This test requires more time to produce results and have less sensitivity. Detecting feasible coronavirus infection using chest X-Ray may also assist quarantine excessive risk sufferers while testing results are disclosed. A learning model can be built based on CT scan images or Chest X-rays of individuals with higher accuracy. This paper represents a computer-aided diagnosis of COVID 19 infection bases on a feature extractor by using CNN models.

scholarly journals Leadless pacemakers: where is the device?

EP Europace ◽  
2021 ◽  
Vol 23 (Supplement_3) ◽  
Author(s):  
M Elrefai ◽  
C Menexi ◽  
P Roberts
Keyword(s):  
Heart Diseases ◽  
Sinus Node ◽  
X Rays ◽  
Funding Sources ◽  
Safety And Reliability ◽  
Chest X Ray ◽  
Implantation Sites ◽  
The Right ◽  
Funding Acknowledgements

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Leadless pacemakers (LPs) were designed to avoid lead-related complications associated with transvenous pacing. To minimise the risk of complications, there is preference towards implanting LPs into the septal aspect of the right ventricle rather than the apex or free wall. The Transcatheter Pacing Study (TPS) and the international post-approval registry demonstrated the safety and reliability of the LP systems in real-world settings. The registry demonstrated that more than half of the LPs were implanted into the septum and most required &lt;2 attempts at deployment. We report a radiological method of defining LP position. Methods We reviewed the first 100 LPs implanted at our centre. Two independent observers who didn’t implant LPs reviewed the patients’ post-implant fluoroscopy images and/or post-implant CXRs when available. The reviewers assessed the devices’ positions in postero-anterior (PA) and/or right anterior oblique (RAO) views based on conventional fluoroscopic criteria for lead position. We used the proposed criteria interchangeably on fluoroscopic images and post implant CXRs (Figure). Differences in classification of device position were resolved by consensus. Results Three experienced operators implanted 100 LPs at our centre. Patients (61% male) 56.6 ± 22.2 years had normal hearts (74%), ischaemic cardiomyopathies (12%), congenital heart diseases (6%), valvular pathologies (5%) and dilated cardiomyopathies (3%). Indications for pacing were symptomatic sinus node dysfunction (36%), followed by high grade atrio-ventricular block (33%), bradyarrhythmia associated with atrial tachyarrhythmias (27%) and other indications for pacing (4%). We had a 100% successful implant rate, 88% required ≤2 attempts and 70% required one attempt. There were no major complications. We were able to classify the site of the LPs implants in a total of 90 patients who had fluoroscopic projections or chest x-rays that would allow us to classify the implant sites. A total of 32 implants were in the apex (35.6%). 28 were in mid-septum (31.1 %), 15 in the apical septum (16.7%), 14 on the septal aspect of the right ventricular inflow (15.5%) and 1 implant (1.1%) in the septum of the RV outflow tract. Conclusion Our proposed method of defining LP position demonstrated that the rate of implants into the true apex at our centre was highly comparable to that of the international registry. It also showed that we had lower rates of implants into the mid-septum in favour of apical septum. There were no pericardial effusions or cardiac perforations resulting from our implant procedures regardless of the site of the implant. We utilised widely used fluoroscopic and chest x-ray criteria for categorisation of the LPs implantation sites. However, a recognised limitation to our analysis is that our findings were not validated using other imaging modalities such as echocardiogram or cardiac computerised tomography (CT). Abstract Figure. Criteria to classify device position

scholarly journals Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset

2021 ◽  
Vol 11 (22) ◽  
pp. 10528
Author(s):  
Khin Yadanar Win ◽  
Noppadol Maneerat ◽  
Syna Sreng ◽  
Kazuhiko Hamamoto
Keyword(s):  
Deep Learning ◽  
Area Under Curve ◽  
Ensemble Classifier ◽  
Saliency Map ◽  
X Rays ◽  
Automated Classification ◽  
Deep Convolutional Neural Networks ◽  
X Ray ◽  
Chest X Ray

The ongoing COVID-19 pandemic has caused devastating effects on humanity worldwide. With practical advantages and wide accessibility, chest X-rays (CXRs) play vital roles in the diagnosis of COVID-19 and the evaluation of the extent of lung damages incurred by the virus. This study aimed to leverage deep-learning-based methods toward the automated classification of COVID-19 from normal and viral pneumonia on CXRs, and the identification of indicative regions of COVID-19 biomarkers. Initially, we preprocessed and segmented the lung regions usingDeepLabV3+ method, and subsequently cropped the lung regions. The cropped lung regions were used as inputs to several deep convolutional neural networks (CNNs) for the prediction of COVID-19. The dataset was highly unbalanced; the vast majority were normal images, with a small number of COVID-19 and pneumonia images. To remedy the unbalanced distribution and to avoid biased classification results, we applied five different approaches: (i) balancing the class using weighted loss; (ii) image augmentation to add more images to minority cases; (iii) the undersampling of majority classes; (iv) the oversampling of minority classes; and (v) a hybrid resampling approach of oversampling and undersampling. The best-performing methods from each approach were combined as the ensemble classifier using two voting strategies. Finally, we used the saliency map of CNNs to identify the indicative regions of COVID-19 biomarkers which are deemed useful for interpretability. The algorithms were evaluated using the largest publicly available COVID-19 dataset. An ensemble of the top five CNNs with image augmentation achieved the highest accuracy of 99.23% and area under curve (AUC) of 99.97%, surpassing the results of previous studies.

scholarly journals Chest X-ray classification using Deep learning for automated COVID-19 screening

2020 ◽  
Author(s):  
Ankita Shelke ◽  
Madhura Inamdar ◽  
Vruddhi Shah ◽  
Amanshu Tiwari ◽  
Aafiya Hussain ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Stage ◽  
Ground Glass Opacity ◽  
Classification Model ◽  
X Rays ◽  
X Ray ◽  
The People ◽  
History Of ◽  
Chest X Ray

AbstractIn today’s world, we find ourselves struggling to fight one of the worst pandemics in the history of humanity known as COVID-2019 caused by a coronavirus. If we detect the virus at an early stage (before it enters the lower respiratory tract), the patient can be treated quickly. Once the virus reaches the lungs, we observe ground-glass opacity in the chest X-ray due to fibrosis in the lungs. Due to the significant differences between X-ray images of an infected and non-infected person, artificial intelligence techniques can be used to identify the presence and severity of the infection. We propose a classification model that can analyze the chest X-rays and help in the accurate diagnosis of COVID-19. Our methodology classifies the chest X-rays into 4 classes viz. normal, pneumonia, tuberculosis (TB), and COVID-19. Further, the X-rays indicating COVID-19 are classified on severity-basis into mild, medium, and severe. The deep learning model used for the classification of pneumonia, TB, and normal is VGG16 with an accuracy of 95.9 %. For the segregation of normal pneumonia and COVID-19, the DenseNet-161 was used with an accuracy of 98.9 %. ResNet-18 worked best for severity classification achieving accuracy up to 76 %. Our approach allows mass screening of the people using X-rays as a primary validation for COVID-19.

scholarly journals STRUCTURAL CLASSIFICATION OF PROTEINS USING IMAGE BASED MACHINE LEARNING

2021 ◽  
Author(s):  
Daniel Franklin
Keyword(s):  
Machine Learning ◽  
Linear Chain ◽  
Three Dimensional ◽  
Structural Classification ◽  
Class Prediction ◽  
Novel Approach ◽  
Scop Classification ◽  
Structural Classification Of Proteins ◽  
Better Than

Classification of proteins is an important area of research that enables better grouping of proteins either by their function, evolutionary similarities or in their structural makeup. Structural classification is the area of research that this thesis focuses on. We use visualizations of proteins to build a machine learning class prediction model, that successfully classifies proteins using the Structural Classification of Proteins (SCOP) framework. SCOP is a well-researched classification with many approaches using a representation of a proteins secondary structure in a linear chain of structures. This thesis uses a novel approach of rendering a three dimensional visualization of the protein itself and then applying image based machine learning to determine a protein’s SCOP classification. The resulting convolutional neural network (CNN) method has achieved average accuracies in the range 78-87% on the 25PDB dataset, which is better than or equal to the existing methods.

The Clinical Significance of Occult Thoracic Injury in Blunt Trauma Patients

2010 ◽  
Vol 76 (10) ◽  
pp. 1063-1066 ◽  
Author(s):  
Meghann Kaiser ◽  
Matthew Whealon ◽  
Cristobal Barrios ◽  
Sarah Dobson ◽  
Darren Malinoski ◽  
...  
Keyword(s):  
Traumatic Injury ◽  
Rib Fractures ◽  
Pulmonary Contusion ◽  
Thoracic Injury ◽  
Trauma Patients ◽  
Included Patient ◽  
Clinical Consequences ◽  
Chest X Ray ◽  
Patient Groups ◽  
Level I

Increased use of thoracic CT (TCT) in diagnosis of blunt traumatic injury has identified many injuries previously undetected on screening chest x-ray (CXR), termed “occult injury.” The optimal management of occult rib fractures, pneumothoraces (PTX), hemothoraces (HTX), and pulmonary contusions is uncertain. Our objective was to determine the current management and clinical outcome of these occult blunt thoracic injuries. A retrospective review identified patients with blunt thoracic trauma who underwent both CXR and TCT over a 2-year period at a Level I urban trauma center. Patients with acute rib fractures, PTX, HTX, or pulmonary contusion on TCT were included. Patient groups analyzed included: 1) no injury (normal CXR, normal TCT, n = 1337); 2) occult injury (normal CXR, abnormal TCT, n = 205); and 3) overt injury (abnormal CXR, abnormal TCT, n = 227). Patients with overt injury required significantly more mechanical ventilation and had greater mortality than either occult or no injury patients. Occult and no injury patients had similar ventilator needs and mortality, but occult injury patients remained hospitalized longer. No patient with isolated occult thoracic injury required intubation or tube thoracostomy. Occult injuries, diagnosed by TCT only, have minimal clinical consequences but attract increased hospital resources.

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