scholarly journals Deep convolutional approaches for the analysis of Covid-19 using chest X-Ray images from portable devices

2020 ◽  
Author(s):  
Joaquim de Moura ◽  
Lucía Ramos ◽  
Plácido L. Vidal ◽  
Milena Cruz ◽  
Laura Abelairas ◽  
...  
Keyword(s):  
Network Architecture ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
World Health ◽  
Portable Devices ◽  
Learning Approaches ◽  
Portable Equipment ◽  
Convolutional Network ◽  
X Ray ◽  
Chest X Ray

The recent human coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been declared as a global pandemic on 11 March 2020 by the World Health Organization. Given the effects of COVID-19 in pulmonary tissues, chest radiography imaging plays an important role for the screening, early detection and monitoring of the suspected individuals. Hence, as the pandemic of COVID-19 progresses, there will be a greater reliance on the use of portable equipment for the acquisition of chest X-Ray images due to its accessibility, widespread availability and benefits regarding to infection control issues, minimizing the risk of cross contamination. This work presents novel fully automatic approaches specifically tailored for the classification of chest X-Ray images acquired by portable equipment into 3 different clinical categories: normal, pathological and COVID-19. For this purpose, two complementary deep learning approaches based on a densely convolutional network architecture are herein presented. The joint response of both approaches allows to enhance the differentiation between patients infected with COVID-19, patients with other diseases that manifest characteristics similar to COVID-19 and normal cases. The proposed approaches were validated over a dataset provided by the Radiology Service of the Complexo Hospitalario Universitario A Coruña (CHUAC) specifically retrieved for this research. Despite the poor quality of chest X-Ray images that is inherent to the nature of the portable equipment, the proposed approaches provided satisfactory results, allowing a reliable analysis of portable radiographs, to support the clinical decision-making process.

scholarly journals Fully automatic deep convolutional approaches for the analysis of Covid-19 using chest X-ray images

2020 ◽  
Author(s):  
Joaquim de Moura ◽  
Jorge Novo ◽  
Marcos Ortega
Keyword(s):  
Infectious Disease ◽  
Network Architecture ◽  
World Health ◽  
Convolutional Network ◽  
Diagnostic Screening ◽  
X Ray ◽  
The World ◽  
Chest X Ray ◽  
Fully Automatic ◽  
Early Diagnostic

AbstractCovid-19 is a new infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the seriousness of the situation, the World Health Organization declared a global pandemic as the Covid-19 rapidly around the world. Among its applications, chest X-ray images are frequently used for an early diagnostic/screening of Covid-19 disease, given the frequent pulmonary impact in the patients, critical issue to prevent further complications caused by this highly infectious disease.In this work, we propose complementary fully automatic approaches for the classification of chest X-ray images under the analysis of 3 different categories: Covid-19, pneumonia and healthy cases. Given the similarity between the pathological impact in the lungs between Covid-19 and pneumonia, mainly during the initial stages of both lung diseases, we performed an exhaustive study of differentiation considering different pathological scenarios. To face these classification tasks, we exploited and adapted to this topic a densely convolutional network architecture, which connects each layer to every other layer in a feed-forward fashion. To validate the designed approaches, several representative experiments were performed using images retrieved from different public chest X-ray images datasets. overall, satisfactory results were obtained from the designed experiments, facilitating the doctors’ work and allowing better an early diagnostic/screening and treatment of this relevant pandemic pathology.

scholarly journals LUNG ULTRASOUND DURING COVID-19 PANDEMICS: WHY, HOW AND WHEN?

2021 ◽  
Vol 74 (8) ◽  
pp. 1783-1788
Author(s):  
Khrystyna O. Pronyuk ◽  
Liudmyla O. Kondratiuk ◽  
Andrii D. Vysotskyi ◽  
Olga A. Golubovska ◽  
Iryna M. Nikitina
Keyword(s):  
Hospital Stay ◽  
Clinical Decision Making ◽  
Lung Ultrasound ◽  
Limited Resource ◽  
Clinical Decision ◽  
Lung Damage ◽  
Dynamic Monitoring ◽  
Initial Assessment ◽  
X Ray ◽  
Chest X Ray

The aim: To optimize diagnostic of pathological processes in lungs affected by COVID-19, dynamic monitoring and clinical decision making using lung ultrasound in limited resources settings. Materials and methods: Between the onset of pandemics and January 2021, approximately 9000 patients have been treated for confirmed COVID-19 in the Olexandrivska Clinical Hospital. Assessment of all hospitalized patients included hematology, chemistries and proinflammatory cytokines – IL-6, CRP, procalcitonin, ferritin. Diagnosis was confirmed by PCR for SARS-CoV-2 RNA. Chest X-ray was performed in all hospitalized cases, while CT was available approximately in 30% of cases during hospital stay. Lung ultrasound was proactively utilized to assess the type and extent of lung damage and to monitor the progress of disease in patients hospitalized into the ICU and Infection Unit (n=135). Ultrasound findings were recorded numerically based on scales. Results: In the setting of СOVID-19, bedside lung ultrasound has been promptly recognized as a tool to diagnose and monitor the nature and extent of lung injury. Lung ultrasound is a real time assessment, which helps determine the nature of a pathologic process affecting lungs. In this paper the accuracy of bedside LUS, chest X-ray and computer tomography are compared based on clinical cases, typical for COVID-19 lung ultrasound appearance is evaluated. Described in article data is collected in one of the biggest facility that deals with COVID-19. Chest X-ray was performed in all hospitalized cases, while CT was available approximately in 30% of cases during hospital stay. The cases presented in the paper indicate potential advantages to the use of ultrasound in limited resource healthcare settings, especially when the risk of transportation to CT outweighs the value of information obtained. Conclusions: Grading of ultrasonographic findings in the lungs was sufficient for both initial assessment with identification of high risk patients, and routine daily monitoring. Hence, lung ultrsound may be used to predict deterioration, stratify risks and make clinical decisions.

scholarly journals Slumped posture on chest X-ray—association with mortality and length of hospital stay in older hospitalised patients

2019 ◽  
Vol 48 (4) ◽  
pp. 588-591
Author(s):  
Aled Lloyd ◽  
Elin Thomas ◽  
Hasan Haboubi
Keyword(s):  
Length Of Stay ◽  
Hospital Stay ◽  
Clinical Decision Making ◽  
Length Of Hospital Stay ◽  
Clinical Decision ◽  
Health Board ◽  
X Ray ◽  
Shortest Distance ◽  
Hospitalised Patients ◽  
Chest X Ray

Abstract Introduction frail, older patients are occasionally incapable of keeping their head out of the field of view of a chest radiograph (CXR) resulting in a ‘slumpogram’. This study aims to explore a possible link between a slumped appearance on a CXR; mortality and length of hospital stay. Methods the CXRs of patients aged over 65 admitted to a Health Board with a catchment area of approximately 300,000 were investigated in a retrospective analysis of all CXRs taken during the first week of January 2015. Slumped patients were compared to age matched controls. The degree of slumping was measured by the number of ribs covered and the MA/C factor (the shortest distance between the angle of the mandible and a line drawn between the heads of both acromion divided by the length of the patient’s clavicle). Outcomes investigated included length of hospital stay and 18-month mortality. Results 806 CXRs were viewed with 53 slumped patients and 53 matched controls identified. In all patients aged over 65 there was a statistically significant correlation between the length of stay and the number of ribs covered by the patients’ head (P = 0.038). The MA/C factor was also associated with length of stay (P = 0.025). In patients over 80 there was a significant association between the number of ribs covered and death (P = 0.015). Conclusion a slumped CXR may be associated with longer hospitalisation or death. The results of this small study require further revalidation but if true could help inform clinical decision making.

scholarly journals COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images

Diagnostics ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 25
Author(s):  
Hossein Aboutalebi ◽  
Maya Pavlova ◽  
Mohammad Javad Shafiee ◽  
Ali Sabri ◽  
Amer Alaref ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Clinical Decision ◽  
Severity Assessment ◽  
Clinical Workflow ◽  
X Ray ◽  
Number Of Patients ◽  
The World ◽  
Chest X Ray

The world is still struggling in controlling and containing the spread of the COVID-19 pandemic caused by the SARS-CoV-2 virus. The medical conditions associated with SARS-CoV-2 infections have resulted in a surge in the number of patients at clinics and hospitals, leading to a significantly increased strain on healthcare resources. As such, an important part of managing and handling patients with SARS-CoV-2 infections within the clinical workflow is severity assessment, which is often conducted with the use of chest X-ray (CXR) images. In this work, we introduce COVID-Net CXR-S, a convolutional neural network for predicting the airspace severity of a SARS-CoV-2 positive patient based on a CXR image of the patient’s chest. More specifically, we leveraged transfer learning to transfer representational knowledge gained from over 16,000 CXR images from a multinational cohort of over 15,000 SARS-CoV-2 positive and negative patient cases into a custom network architecture for severity assessment. Experimental results using the RSNA RICORD dataset showed that the proposed COVID-Net CXR-S has potential to be a powerful tool for computer-aided severity assessment of CXR images of COVID-19 positive patients. Furthermore, radiologist validation on select cases by two board-certified radiologists with over 10 and 19 years of experience, respectively, showed consistency between radiologist interpretation and critical factors leveraged by COVID-Net CXR-S for severity assessment. While not a production-ready solution, the ultimate goal for the open source release of COVID-Net CXR-S is to act as a catalyst for clinical scientists, machine learning researchers, as well as citizen scientists to develop innovative new clinical decision support solutions for helping clinicians around the world manage the continuing pandemic.

scholarly journals Lung Ultrasound in COVID-19 pandemic: Why? How? And When?

2020 ◽  
pp. 3-12
Author(s):  
Khrystyna Pronyuk ◽  
Andriy Vysotskyi
Keyword(s):  
Critically Ill ◽  
Computer Tomography ◽  
Critically Ill Patients ◽  
Clinical Decision Making ◽  
Lung Ultrasound ◽  
Clinical Decision ◽  
X Rays ◽  
Lung Involvement ◽  
X Ray ◽  
Chest X Ray

During COVID-19 pandemic Lung ultrasound has rapidly become a tool for diagnosis and monitoring of lung involvement and it’s severity. Accurate evaluation of lung pathologic entities at the bedside, especially in critically ill patients, and those on mechanical ventilation, remains problematic. CT should not be frequently repeated and is not available everywhere, especially for critically ill patients. Limitations of bedside chest X-ray have been well described and lead to poor-quality X-ray films with low sensitivity.The lung ultrasound has been shown to be a useful tool in intensive care patients with adult respiratory distress syndrome (ARDS) and can be used forassessing severity of lung involvement in COVID-19. In this paper the accuracy of bedside LUS, chest X-ray and computer tomography are compared based on clinical cases, typical for COVID-19 lung ultrasound appearance is evaluated. There have been shown that lung ultrasound can predict the deterioration of the patient's conditionand can be used for risk stratification and clinical decision making, reducethe use of both chest x‐rays and computer tomography, what is very important especially in limited resources settings.

scholarly journals COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images

2021 ◽  
Author(s):  
Hossein Aboutalebi ◽  
Maya Pavlova ◽  
Mohammad Javad Shafiee ◽  
Ali Sabri ◽  
Amer Alaref ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Clinical Decision ◽  
Severity Assessment ◽  
Clinical Workflow ◽  
X Ray ◽  
Number Of Patients ◽  
The World ◽  
Chest X Ray

Abstract The world is still struggling in controlling and containing the spread of the COVID-19 pandemic caused by the SARS-CoV-2 virus. The medical conditions associated with SARS-CoV-2 infections have resulted in a surge in the number of patients at clinics and hospitals, leading to a significantly increased strain on healthcare resources. As such, an important part of managing patients with SARS-CoV-2 infections within the clinical workflow is severity assessment, which is often conducted with the use of chest x-ray (CXR) images. In this work, we introduce COVID-Net CXR-S, a convolutional neural network for predicting the airspace severity of a SARS-CoV-2 positive patient based on a CXR image of the patient's chest. More specifically, we leveraged transfer learning to transfer representational knowledge gained from over 16,000 CXR images from a multinational cohort of over 15,000 patient cases into a custom network architecture for severity assessment. Experimental results with a multi-national patient cohort curated by the Radiological Society of North America (RSNA) RICORD initiative showed that the proposed COVID-Net CXR-S has potential to be a powerful tool for computer-aided severity assessment of CXR images of COVID-19 positive patients. Furthermore, radiologist validation on select cases by two board-certified radiologists with over 10 and 19 years of experience, respectively, showed consistency between radiologist interpretation and critical factors leveraged by COVID-Net CXR-S for severity assessment. While not a production-ready solution, the ultimate goal for the open source release of COVID-Net CXR-S is to act as a catalyst for clinical scientists, machine learning researchers, as well as citizen scientists to develop innovative new clinical decision support solutions for helping clinicians around the world manage the continuing pandemic.

Cost-effective clinical decision making

PEDIATRICS ◽  
1977 ◽  
Vol 60 (5) ◽  
pp. 756-759
Author(s):  
Duncan Neuhauser
Keyword(s):  
Decision Making ◽  
Hospital Stay ◽  
Clinical Decision Making ◽  
Cost Effective ◽  
Clinical Decision ◽  
Inappropriate Therapy ◽  
X Ray ◽  
Scarce Resources ◽  
Chest X Ray ◽  
The Cost

Are pediatric preoperative chest x-ray examinations worth doing? Sane et al. (p. 669) conclude that they are "medically and economically justified and essential." To answer this question requires at least the following information. What is the cost of the examination (dollars, radiation exposure, cost of further testing and perhaps inappropriate therapy resulting therefrom, and possible prolongation of hospital stay)? What is the benefit? What percent of patients have unsuspected findings; of these, the percent with modified treatment; and of these, what percent benefited therefrom? What was the cost per unit of benefit received? Is there some other use of these scarce resources that would have yielded greater benefits?

scholarly journals The role of chest X-ray in the diagnosis of bacteriologically confirmed pulmonary tuberculosis in hospitalised Xpert MTB/RIF-negative patients

2020 ◽  
pp. 00708-2020
Author(s):  
Wakjira Kebede ◽  
Gemeda Abebe ◽  
Esayas Kebede Gudina ◽  
Elias Kedir ◽  
Thuy Ngan Tran ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Clinical Symptoms ◽  
Medical Center ◽  
Strong Association ◽  
Clinical Decision ◽  
Prediction Tool ◽  
X Ray ◽  
Hospitalised Patients ◽  
Chest X Ray

IntroductionThe role of a chest X-ray (CXR) to diagnose active tuberculosis (TB) in symptomatic patients who have a negative Xpert MTB/RIF (Xpert) test result is unclear. This study aimed to assess the performance of CXR and the value of CXR findings for a prediction tool to identify cases of active pulmonary TB among symptomatic, Xpert-negative hospitalised patients.MethodsXpert-negative patients hospitalised between January and July 2019 at Jimma University Medical Center in Ethiopia were assessed by mycobacterial culture and CXR. CXR was interpreted by a clinician for clinical decision making and by a radiologist for research purposes. Using bacteriological confirmation as the reference standard, the performance of CXR to diagnose active TB was assessed by the area under the receiver operating characteristic (AUC) curve, predictors of active TB were identified using bivariate and multivariate logistic regression analyses.ResultsOf 247 Xpert-negative patients, 38% and 40% were classified as suggestive of TB by clinician and radiologist, respectively. Of the 39 (15.8%) bacteriologically confirmed cases, 69% and 79% were classified as having CXR findings suggestive of TB by clinician or radiologist, respectively. While there was a strong association between bacteriologically confirmed TB and CXR classified by clinician as suggestive of TB (aOR 2.7, 95% CI: 1.2–6.6), CXR with signs typical of TB (aOR 5.3, 95% CI: 2.1–14.4) or compatible with TB (aOR 5.1, 95% CI: 1.3–20.0), the positive predictive value of the CXR was low (27% and 34% for classification by clinician and radiologist, respectively). The addition of CXR findings by clinician or radiologist to clinical characteristics did not improve the performance of the prediction tool, with similar risk classification distribution, AUCs and negative and positive prediction values.ConclusionDespite the strong association between CXR findings and active TB among hospitalised Xpert negative individuals, CXR findings did not improve the performance of a risk prediction tool based solely on clinical symptoms. Countries with a high TB/HIV burden should urgently replace Xpert by the more sensitive Xpert ultra assay to improve the diagnosis of active TB.

scholarly journals A Prospective Observational Study to Investigate Performance of a Chest X-ray Artificial Intelligence Diagnostic Support Tool Across 12 U.S. Hospitals

2021 ◽  
Author(s):  
Ju Sun ◽  
Le Peng ◽  
Taihui Li ◽  
Dyah Adila ◽  
Zach Zaiman ◽  
...  
Keyword(s):  
Real Time ◽  
Clinical Decision Making ◽  
External Validation ◽  
Model Performance ◽  
Signs And Symptoms ◽  
Clinical Decision ◽  
Indiana University ◽  
X Ray ◽  
Emory University ◽  
Chest X Ray

Importance: An artificial intelligence (AI)-based model to predict COVID-19 likelihood from chest x-ray (CXR) findings can serve as an important adjunct to accelerate immediate clinical decision making and improve clinical decision making. Despite significant efforts, many limitations and biases exist in previously developed AI diagnostic models for COVID-19. Utilizing a large set of local and international CXR images, we developed an AI model with high performance on temporal and external validation. Objective: Investigate real-time performance of an AI-enabled COVID-19 diagnostic support system across a 12-hospital system. Design: Prospective observational study. Setting: Labeled frontal CXR images (samples of COVID-19 and non-COVID-19) from the M Health Fairview (Minnesota, USA), Valencian Region Medical ImageBank (Spain), MIMIC-CXR, Open-I 2013 Chest X-ray Collection, GitHub COVID-19 Image Data Collection (International), Indiana University (Indiana, USA), and Emory University (Georgia, USA) Participants: Internal (training, temporal, and real-time validation): 51,592 CXRs; Public: 27,424 CXRs; External (Indiana University): 10,002 CXRs; External (Emory University): 2002 CXRs Main Outcome and Measure: Model performance assessed via receiver operating characteristic (ROC), Precision-Recall curves, and F1 score. Results: Patients that were COVID-19 positive had significantly higher COVID-19 Diagnostic Scores (median .1 [IQR: 0.0-0.8] vs median 0.0 [IQR: 0.0-0.1], p < 0.001) than patients that were COVID-19 negative. Pre-implementation the AI-model performed well on temporal validation (AUROC 0.8) and external validation (AUROC 0.76 at Indiana U, AUROC 0.72 at Emory U). The model was noted to have unrealistic performance (AUROC > 0.95) using publicly available databases. Real-time model performance was unchanged over 19 weeks of implementation (AUROC 0.70). On subgroup analysis, the model had improved discrimination for patients with severe as compared to mild or moderate disease, p < 0.001. Model performance was highest in Asians and lowest in whites and similar between males and females. Conclusions and Relevance: AI-based diagnostic tools may serve as an adjunct, but not replacement, for clinical decision support of COVID-19 diagnosis, which largely hinges on exposure history, signs, and symptoms. While AI-based tools have not yet reached full diagnostic potential in COVID-19, they may still offer valuable information to clinicians taken into consideration along with clinical signs and symptoms.

scholarly journals Does imbalance in chest X-ray datasets produce biased deep learning approaches for COVID-19 screening?

2021 ◽  
Author(s):  
Lorena Alvarez-Rodríguez ◽  
Joaquim de Moura ◽  
Jorge Novo ◽  
Marcos Ortega
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Clinical Decision Making ◽  
Patient Characteristics ◽  
Clinical Decision ◽  
Decision Making Process ◽  
Health Crisis ◽  
X Ray ◽  
Reliable Analysis ◽  
Chest X Ray

Abstract Background: The health crisis resulting from the global COVID-19 pandemic highlighted more than ever the need for rapid, reliable and safe methods of diagnosis and monitoring of respiratory diseases. To study pulmonary involvement in detail, one of the most common resources is the use different lung imaging modalities (like chest radiography) to explore the possible affected areas. Methods: In this work, we performed a comprehensive analysis of sex and age factors in chest X-ray images. The study of these recurrent patient characteristics in pathologies of this type is crucial, since there is a clear scarcity of data that may lead to biases when trying to develop systems that are as representative as possible, as well as to gain knowledge of the disease itself. To identify possible biases, we analyzed 3 different computational approaches for automatic COVID-19 screening: Normal vs COVID-19, Pneumonia vs COVID-19 and Non-COVID-19 vs COVID-19. The presented study was validated using two public chest X-ray datasets, allowing a reliable analysis to support the clinical decision-making process in the context of this dramatic global pandemic. Results: The obtained results for the sex-related imbalance analysis indicate that this factor slightly affects the system performance in the Normal VS COVID-19 and Pneumonia VS COVID-19 approaches, although the identified differences are not relevant enough to worsen considerably the system’s response. Regarding the age-related imbalance analysis, this factor was observed to be again influencing the system in a more consistent way than the sex factor, as it was present in all the approaches. Once again, this worsening is not a major problem for our data and system, as it is not of great magnitude. Conclusions: Multiple studies have been conducted in other fields in order to determine if certain patient characteristics such as sex or age influenced these deep learning systems. However, to the best of our knowledge, this study has not been done for COVID-19 despite the urgency and lack of COVID-19 chest x-ray images. The presented results evidenced that the proposed methodology and tested approaches allow a robust and reliable analysis to support the clinical decision-making process in this pandemic scenario.

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