Comparison of Baseline, Bone-Subtracted, and Enhanced Chest Radiographs for Detection of Pneumothorax

2020 ◽  
pp. 084653712090885
Author(s):  
Fatemeh Homayounieh ◽  
Subba R. Digumarthy ◽  
Jennifer A. Febbo ◽  
Sherief Garrana ◽  
Chayanin Nitiwarangkul ◽  
...  
Keyword(s):  
False Negative ◽  
Tension Pneumothorax ◽  
Area Under The Curve ◽  
Ground Truth ◽  
Chest Radiographs ◽  
Detection Rates ◽  
X Ray ◽  
Institutional Review ◽  
Type Area ◽  
Chest X Ray

Purpose: To assess and compare detectability of pneumothorax on unprocessed baseline, single-energy, bone-subtracted, and enhanced frontal chest radiographs (chest X-ray, CXR). Method and Materials: Our retrospective institutional review board–approved study included 202 patients (mean age 53 ± 24 years; 132 men, 70 women) who underwent frontal CXR and had trace, moderate, large, or tension pneumothorax. All patients (except those with tension pneumothorax) had concurrent chest computed tomography (CT). Two radiologists reviewed the CXR and chest CT for pneumothorax on baseline CXR (ground truth). All baseline CXR were processed to generate bone-subtracted and enhanced images (ClearRead X-ray). Four radiologists (R1-R4) assessed the baseline, bone-subtracted, and enhanced images and recorded the presence of pneumothorax (side, size, and confidence for detection) for each image type. Area under the curve (AUC) was calculated with receiver operating characteristic analyses to determine the accuracy of pneumothorax detection. Results: Bone-subtracted images (AUC: 0.89-0.97) had the lowest accuracy for detection of pneumothorax compared to the baseline (AUC: 0.94-0.97) and enhanced (AUC: 0.96-0.99) radiographs ( P < .01). Most false-positive and false-negative pneumothoraces were detected on the bone-subtracted images and the least numbers on the enhanced radiographs. Highest detection rates and confidence were noted for the enhanced images (empiric AUC for R1-R4 0.96-0.99). Conclusion: Enhanced CXRs are superior to bone-subtracted and unprocessed radiographs for detection of pneumothorax. Clinical Relevance/Application: Enhanced CXRs improve detection of pneumothorax over unprocessed images; bone-subtracted images must be cautiously reviewed to avoid false negatives.

scholarly journals COVID-19 infection map generation and detection from chest X-ray images

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Aysen Degerli ◽  
Mete Ahishali ◽  
Mehmet Yamac ◽  
Serkan Kiranyaz ◽  
Muhammad E. H. Chowdhury ◽  
...  
Keyword(s):  
State Of The Art ◽  
Ground Truth ◽  
Clinical Use ◽  
X Ray ◽  
Learning Techniques ◽  
Map Generation ◽  
Severity Grading ◽  
Chest X Ray ◽  
Novel Method ◽  
Aided Diagnosis

AbstractComputer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus. Numerous studies have proposed to use Deep Learning techniques for COVID-19 diagnosis. However, they have used very limited chest X-ray (CXR) image repositories for evaluation with a small number, a few hundreds, of COVID-19 samples. Moreover, these methods can neither localize nor grade the severity of COVID-19 infection. For this purpose, recent studies proposed to explore the activation maps of deep networks. However, they remain inaccurate for localizing the actual infestation making them unreliable for clinical use. This study proposes a novel method for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating the so-called infection maps. To accomplish this, we have compiled the largest dataset with 119,316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human–machine approach. Furthermore, we publicly release the first CXR dataset with the ground-truth segmentation masks of the COVID-19 infected regions. A detailed set of experiments show that state-of-the-art segmentation networks can learn to localize COVID-19 infection with an F1-score of 83.20%, which is significantly superior to the activation maps created by the previous methods. Finally, the proposed approach achieved a COVID-19 detection performance with 94.96% sensitivity and 99.88% specificity.

scholarly journals Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Isabella Castiglioni ◽  
Davide Ippolito ◽  
Matteo Interlenghi ◽  
Caterina Beatrice Monti ◽  
Christian Salvatore ◽  
...  
Keyword(s):  
Deep Learning ◽  
Area Under The Curve ◽  
Clinical Information ◽  
Training Dataset ◽  
Independent Dataset ◽  
X Ray ◽  
Learning Classifier ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
Deep Learning Model

Abstract Background We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in Lombardy, Italy. Methods We used for training and validation an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals (Centres 1 and 2). We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as the reference standard. Results At 10-fold cross-validation, our deep learning model classified COVID-19 and non-COVID-19 patients with 0.78 sensitivity (95% confidence interval [CI] 0.74–0.81), 0.82 specificity (95% CI 0.78–0.85), and 0.89 area under the curve (AUC) (95% CI 0.86–0.91). For the independent dataset, deep learning showed 0.80 sensitivity (95% CI 0.72–0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73–0.87), and 0.81 AUC (95% CI 0.73–0.87). Radiologists’ reading obtained 0.63 sensitivity (95% CI 0.52–0.74) and 0.78 specificity (95% CI 0.61–0.90) in Centre 1 and 0.64 sensitivity (95% CI 0.52–0.74) and 0.86 specificity (95% CI 0.71–0.95) in Centre 2. Conclusions This preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of deep learning for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.

Is a routine chest X-ray indicated before discharge following paediatric cardiac surgery?

2009 ◽  
Vol 19 (4) ◽  
pp. 370-371 ◽  
Author(s):  
Kerstin Bosse ◽  
Thomas Krasemann
Keyword(s):  
Cardiac Surgery ◽  
Heart Surgery ◽  
Clinical Impact ◽  
Chest Radiographs ◽  
X Ray ◽  
Paediatric Cardiac Surgery ◽  
Routine Basis ◽  
Chest X Ray ◽  
Routine Chest

AbstractIn many paediatric cardiosurgical units, a chest X-ray is routinely performed before discharge. We sought to evaluate the clinical impact of such routine radiographs in the management of children after cardiac surgery.Of 100 consecutive children, a chest X-ray was performed in 71 prior to discharge. Of these, 38 were clinically indicated, while 33 were performed as a routine. Therapeutic changes were instituted on the basis of the X-ray in 4 patients, in all of whom the imaging had been clinically indicated. No therapeutic changes followed those radiographs performed on a routine basis.Conclusion: Routine chest radiographs can be omitted prior to discharging patients after paediatric heart surgery.

Interpretation of chest radiographs

2018 ◽  
Vol 11 (3) ◽  
pp. 155-161
Author(s):  
Ramona-Rita Barbara ◽  
Eryl A Thomas
Keyword(s):  
General Practice ◽  
Junior Doctor ◽  
Chest Radiographs ◽  
Patient Treatment ◽  
X Ray ◽  
Chest X Ray

It is vital that every junior doctor has a thorough knowledge of the fundamentals of interpreting chest radiographs. Frequently, hospital-based trainees in general practice need to make a decision regarding patient treatment on an unreported chest X-ray. This article covers the basic interpretation of chest radiographs and the most common pathologies encountered.

Can We Screen Febrile Children for Pneumonia and/or Bacteremia Without Chest X-ray Films and Blood Cultures?

PEDIATRICS ◽  
1978 ◽  
Vol 61 (2) ◽  
pp. 332-333
Author(s):  
Henry M. Feder
Keyword(s):  
Young Children ◽  
Prospective Study ◽  
False Negative ◽  
Blood Cultures ◽  
Chest Roentgenogram ◽  
Negative Group ◽  
X Ray ◽  
Chest X Ray ◽  
Wbc Count ◽  
A Prospective Study

McCarthy et al. in their article "Temperature Greater Than or Equal to 40 C in Children Less Than 24 Months of Age: A Prospective Study" (Pediatrics 59:663, May 1977) recommend using both WBC count (≥ 15,000/cu mm) and ESR (≥ 30 mm/hr) for screening febrile young children for pneumonia or bacteremia. If either is elevated they suggest doing blood cultures and taking a chest roentgenogram. However, in 25% of their patients with bacteremia and 42% of their patients with pneumonia neither WBC count nor ESR was elevated, leaving a sizable false-negative group.

scholarly journals A gyermekkori alsó légúti idegentest-aspiráció diagnosztikája a klinikai gyakorlatban

2018 ◽  
Vol 159 (51) ◽  
pp. 2162-2166
Author(s):  
Dániel Hajnal ◽  
Tamás Kovács
Keyword(s):  
Foreign Body ◽  
Standard Procedure ◽  
False Negative ◽  
Radiological Finding ◽  
Paediatric Population ◽  
Rigid Bronchoscopy ◽  
Foreign Body Aspiration ◽  
X Ray ◽  
Co Morbidity ◽  
Chest X Ray

Abstract: Introduction and aim: Rigid bronchoscopic foreign body removal is the gold standard procedure for foreign body aspiration. We have analysed our results of bronchoscopies and the accuracy of diagnosis among the paediatric population in Southeast Hungary. Method: A retrospective study of children admitted because of suspected solid foreign body aspiration between 2006 and 2017 was performed. Results: From among 220 admitted patients, 86 were suspected of solid particle aspiration. Presenting history was certain in 68.6% (n = 59/86). Sudden choking-like symptoms were present in 61/86 patients (70.9%), coughing in 81/86 patients (94.2%). Thoracic auscultation was positive in 67/86 cases (77.9%), chest X-ray in 75/86 patients (87.2%), while fluoroscopy only in 12/75 cases (16%). 92 bronchoscopies in 86 patients were performed. In 57 bronchoscopies, solid foreign body was found (66.2%) and the removal was successful in 56 cases. Thoracic auscultation was negative in patients with foreign body only in 6/57 cases (10.5%). In the same group, chest X-ray was negative in 33/57 cases (57.9%) and fluoroscopy was positive only in 12/57 patients (21.1%). Pneumonia or prolonged bronchitis was present in 4/86 patients (4.6%). Severe bronchial bleeding occurred in 2/86 cases (2.3%). Mortality was 1.2%, a child with severe co-morbidity and chronic aspiration passed away. Bronchoscopy was negative in 29/86 patients (33.7%). Complications were significantly higher in chronic cases than in the acute ones. Conclusion: Rigid bronchoscopy is indicated if solid foreign body aspiration is suspected and positive anamnesis, typical symptoms (coughing, choking) or positive chest auscultations are present. Diagnosis predominantly based on radiological finding is controversial due to the high possibility of false negative results. Early intervention within the first 24 hours is recommended to avoid complications. Orv Hetil. 2018; 159(51): 2162–2166.

scholarly journals Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks

2020 ◽  
Vol 25 (6) ◽  
pp. 553-565 ◽  
Author(s):  
Boran Sekeroglu ◽  
Ilker Ozsahin
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Statistical Data ◽  
Area Under The Curve ◽  
Operating Characteristics ◽  
X Ray ◽  
Life Saving ◽  
Chest X Ray

The detection of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), which is responsible for coronavirus disease 2019 (COVID-19), using chest X-ray images has life-saving importance for both patients and doctors. In addition, in countries that are unable to purchase laboratory kits for testing, this becomes even more vital. In this study, we aimed to present the use of deep learning for the high-accuracy detection of COVID-19 using chest X-ray images. Publicly available X-ray images (1583 healthy, 4292 pneumonia, and 225 confirmed COVID-19) were used in the experiments, which involved the training of deep learning and machine learning classifiers. Thirty-eight experiments were performed using convolutional neural networks, 10 experiments were performed using five machine learning models, and 14 experiments were performed using the state-of-the-art pre-trained networks for transfer learning. Images and statistical data were considered separately in the experiments to evaluate the performances of models, and eightfold cross-validation was used. A mean sensitivity of 93.84%, mean specificity of 99.18%, mean accuracy of 98.50%, and mean receiver operating characteristics–area under the curve scores of 96.51% are achieved. A convolutional neural network without pre-processing and with minimized layers is capable of detecting COVID-19 in a limited number of, and in imbalanced, chest X-ray images.

The intracavitary electrocardiography method for positioning the tip of epicutaneous cava catheter in neonates: Pilot study

2018 ◽  
Vol 19 (6) ◽  
pp. 542-547 ◽  
Author(s):  
Antonella Capasso ◽  
Rossella Mastroianni ◽  
Annalisa Passariello ◽  
Marta Palma ◽  
Francesco Messina ◽  
...  
Keyword(s):  
Pilot Study ◽  
Superior Vena ◽  
False Negative ◽  
Vena Cava ◽  
P Wave ◽  
Target Zone ◽  
X Ray ◽  
Catheter Tip ◽  
Chest X Ray ◽  
Head Neck

Purpose: The neonatologists of Sant’Anna and San Sebastiano Hospital of Caserta have carried out a pilot study investigating the safety, feasibility, and accuracy of intracavitary electrocardiography for neonatal epicutaneous cava catheter tip positioning. Patients and methods: We enrolled 39 neonates (1–28 days of postnatal age or correct age lower than 41 weeks) requiring epicutaneous cava catheter in the district of superior vena cava (head–neck or upper limbs). Intracavitary electrocardiography was applicable in 38 neonates. Results: No significant complications related to intracavitary electrocardiography occurred in the studied neonates. The increase in P wave on intracavitary electrocardiography was detected in 30 cases. Of the remaining eight cases, six malpositioned catheters tipped out of cavoatrial junction–target zone (chest x-ray and echocardiographical control) and two were false negative (tip located in target zone). The match between intracavitary electrocardiography and x-ray was observed in 29/38 cases, and the same ratio between intracavitary electrocardiography and echocardiography was detected. Conclusion: We conclude that the intracavitary electrocardiography method is safe and accurate in neonates as demonstrated in pediatric and adult patients. The applicability of the method is 97% and its feasibility is 79%. The overall accuracy is 76% but it rises to 97% if “peak” P wave is detected.

scholarly journals COVIDPEN: A Novel COVID-19 Detection Model using Chest X-Rays and CT Scans

2020 ◽  
Author(s):  
Amit Kumar Jaiswal ◽  
Prayag Tiwari ◽  
Vipin Kumar Rathi ◽  
Jia Qian ◽  
Hari Mohan Pandey ◽  
...  
Keyword(s):  
False Negative ◽  
Healthcare Providers ◽  
False Negative Rate ◽  
X Rays ◽  
X Ray ◽  
Detection Model ◽  
Global Pandemic ◽  
High False Negative Rate ◽  
Proposed Model ◽  
Chest X Ray

The trending global pandemic of COVID-19 is the fastest ever impact which caused people worldwide by severe acute respiratory syndrome~(SARS)-driven coronavirus. However, several countries suffer from the shortage of test kits and high false negative rate in PCR test. Enhancing the chest X-ray or CT detection rate becomes critical. The patient triage is of utmost importance and the use of machine learning can drive the diagnosis of chest X-ray or CT image by identifying COVID-19 cases. To tackle this problem, we propose~COVIDPEN~-~a transfer learning approach on Pruned EfficientNet-based model for the detection of COVID-19 cases. The proposed model is further interpolated by post-hoc analysis for the explainability of the predictions. The effectiveness of our proposed model is demonstrated on two systematic datasets of chest radiographs and computed tomography scans. Experimental results with several baseline comparisons show that our method is on par and confers clinically explicable instances, which are meant for healthcare providers.

scholarly journals Deep Learning Based Hybrid Models for Prediction of COVID-19 using Chest X-Ray

2020 ◽  
Author(s):  
Shree Charran R ◽  
Rahul Kumar Dubey
Keyword(s):  
Data Augmentation ◽  
False Negative ◽  
X Ray ◽  
Infected Case ◽  
X Ray Imaging ◽  
Normal Chest ◽  
Chest X Ray ◽  
To Come ◽  
Spread Of Infection

COVID-19 has ended up being the greatest pandemic to come to pass for on humanity in the last century. It has influenced all parts of present day life. The best way to confine its spread is the early and exact finding of infected patients. Clinical imaging strategies like Chest X-ray imaging helps specialists to assess the degree of spread of infection. In any case, the way that COVID-19 side effects imitate those of conventional Pneumonia brings few issues utilizing of Chest Xrays for its prediction accurately. In this investigation, we attempt to assemble 4 ways to deal with characterize between COVID-19 Pneumonia, NON-COVID-19 Pneumonia, and an Healthy- Normal Chest X-Ray images. Considering the low accessibility of genuine named Chest X-Ray images, we incorporated combinations of pre-trained models and data augmentation methods to improve the quality of predictions. Our best model has achieved an accuracy of 99.5216%. More importantly, the hybrid did not predict a False Negative Normal (i.e. infected case predicted as normal) making it the most attractive feature of the study.

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