Enhanced pneumothorax visualization in ICU patients using portable chest radiography.
Ontology highlight
ABSTRACT:
SUBMITTER: Ley-Zaporozhan J
PROVIDER: S-EPMC6303023 | biostudies-literature | 2018
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundIn patients with multiple trauma, a supine chest radiography [chest X-ray (CXR)] is preferred over a erect CXR. However, this method has limitations in detecting post-traumatic pneumothorax. The use of chest computed tomography (CT) to detect traumatic pneumothorax is well known. However, pneumothorax that is not detected before a chest CT scan is known as an occult pneumothorax (OP), and it can cause serious complications in the patient. This study sought to evaluate the frequency and risk factors for OP in trauma patients.MethodsPatients who suffered thoracic trauma at the Level 1 Regional Trauma Center of Wonju Severance Christian Hospital between 2015 and 2022 were included in this study. All patients were at least 18 years old. The study reviewed all patients' supine CXR and chest CT images and classified them into five radiographic diagnoses: pneumothorax, rib fracture, subcutaneous emphysema, lung contusion, and pneumomediastinum.ResultsThe study included 1,284 patients, all with diagnoses of pneumothorax, rib fracture, subcutaneous emphysema, lung contusion, and pneumomediastinum following supine CXR and chest CT. The patient's average age was 58.3±15.2 years. Pneumothorax diagnosis on supine CXR had the lowest accuracy, at 46.7%, and the lowest sensitivity, at 12.7%. In univariate analysis, rib fracture, lung contusion, and subcutaneous emphysema on supine CXR were all found to be statistically significant regarding traumatic OP. In multivariate analysis, the risk factors for OP were lung contusion [odds ratio (OR), 1.440; 95% confidence interval (CI): 1.115-1.860; P=0.005] and subcutaneous emphysema (OR, 25.883; 95% CI: 13.155-50.928; P<0.001) on supine CXR.ConclusionsThe lung contusion and subcutaneous emphysema in supine CXR of trauma patients indicate the presence of OP. Therefore, if chest CT cannot be performed immediately due to unstable vital signs or other circumstances, recognizing the above radiological findings of traumatic pneumothorax may be necessary.
| S-EPMC10482646 | biostudies-literature
Project description:Dynamic digital radiography (DDR) is a high-resolution radiographic imaging technique using pulsed X-ray emission to acquire a multiframe cine-loop of the target anatomical area. The first DDR technology was orthostatic chest acquisitions, but new portable equipment that can be positioned at the patient's bedside was recently released, significantly expanding its potential applications, particularly in chest examination. It provides anatomical and functional information on the motion of different anatomical structures, such as the lungs, pleura, rib cage, and trachea. Native images can be further analyzed with dedicated post-processing software to extract quantitative parameters, including diaphragm motility, automatically projected lung area and area changing rate, a colorimetric map of the signal value change related to respiration and motility, and lung perfusion. The dynamic diagnostic information along with the significant advantages of this technique in terms of portability, versatility, and cost-effectiveness represents a potential game changer for radiological diagnosis and monitoring at the patient's bedside. DDR has several applications in daily clinical practice, and in this narrative review, we will focus on chest imaging, which is the main application explored to date in the literature. However, studies are still needed to understand deeply the clinical impact of this method.
| S-EPMC10885043 | biostudies-literature
Project description:IntroductionUltrasonography is being increasingly utilized in acute care settings with expanding applications. Pneumothorax evaluation by ultrasonography is a fast, safe, easy and inexpensive alternative to chest radiographs. In this review, we provide a comprehensive analysis of the current literature comparing ultrasonography and chest radiography for the diagnosis of pneumothorax.MethodsWe searched English-language articles in MEDLINE, EMBASE and Cochrane Library dealing with both ultrasonography and chest radiography for diagnosis of pneumothorax. In eligible studies that met strict inclusion criteria, we conducted a meta-analysis to evaluate the diagnostic accuracy of pleural ultrasonography in comparison with chest radiography for the diagnosis of pneumothorax.ResultsWe reviewed 601 articles and selected 25 original research articles for detailed review. Only 13 articles met all of our inclusion criteria and were included in the final analysis. One study used lung sliding sign alone, 12 studies used lung sliding and comet tail signs, and 6 studies searched for lung point in addition to the other two signs. Ultrasonography had a pooled sensitivity of 78.6% (95% CI, 68.1 to 98.1) and a specificity of 98.4% (95% CI, 97.3 to 99.5). Chest radiography had a pooled sensitivity of 39.8% (95% CI, 29.4 to 50.3) and a specificity of 99.3% (95% CI, 98.4 to 100). Our meta-regression and subgroup analyses indicate that consecutive sampling of patients compared to convenience sampling provided higher sensitivity results for both ultrasonography and chest radiography. Consecutive versus nonconsecutive sampling and trauma versus nontrauma settings were significant sources of heterogeneity. In addition, subgroup analysis showed significant variations related to operator and type of probe used.ConclusionsOur study indicates that ultrasonography is more accurate than chest radiography for detection of pneumothorax. The results support the previous investigations in this field, add new valuable information obtained from subgroup analysis, and provide accurate estimates for the performance parameters of both bedside ultrasonography and chest radiography for pneumothorax evaluation.
| S-EPMC4057340 | biostudies-literature
Project description:ObjectivesTo evaluate the ability of a commercially available comprehensive chest radiography deep convolutional neural network (DCNN) to detect simple and tension pneumothorax, as stratified by the following subgroups: the presence of an intercostal drain; rib, clavicular, scapular or humeral fractures or rib resections; subcutaneous emphysema and erect versus non-erect positioning. The hypothesis was that performance would not differ significantly in each of these subgroups when compared with the overall test dataset.DesignA retrospective case-control study was undertaken.SettingCommunity radiology clinics and hospitals in Australia and the USA.ParticipantsA test dataset of 2557 chest radiography studies was ground-truthed by three subspecialty thoracic radiologists for the presence of simple or tension pneumothorax as well as each subgroup other than positioning. Radiograph positioning was derived from radiographer annotations on the images.Outcome measuresDCNN performance for detecting simple and tension pneumothorax was evaluated over the entire test set, as well as within each subgroup, using the area under the receiver operating characteristic curve (AUC). A difference in AUC of more than 0.05 was considered clinically significant.ResultsWhen compared with the overall test set, performance of the DCNN for detecting simple and tension pneumothorax was statistically non-inferior in all subgroups. The DCNN had an AUC of 0.981 (0.976-0.986) for detecting simple pneumothorax and 0.997 (0.995-0.999) for detecting tension pneumothorax.ConclusionsHidden stratification has significant implications for potential failures of deep learning when applied in clinical practice. This study demonstrated that a comprehensively trained DCNN can be resilient to hidden stratification in several clinically meaningful subgroups in detecting pneumothorax.
| S-EPMC8655590 | biostudies-literature
Project description:BackgroundChest radiography (CXR) often is performed in the acute setting to help understand the extent of respiratory disease in patients with COVID-19, but a clearly defined role for negative chest radiograph results in assessing patients has not been described.Research questionIs portable CXR an effective exclusionary test for future adverse clinical outcomes in patients suspected of having COVID-19?Study design and methodsCharts of consecutive patients suspected of having COVID-19 at five EDs in New York City between March 19, 2020, and April 23, 2020, were reviewed. Patients were categorized based on absence of findings on initial CXR. The primary outcomes were hospital admission, mechanical ventilation, ARDS, and mortality.ResultsThree thousand two hundred forty-five adult patients, 474 (14.6%) with negative initial CXR results, were reviewed. Among all patients, negative initial CXR results were associated with a low probability of future adverse clinical outcomes, with negative likelihood ratios of 0.27 (95% CI, 0.23-0.31) for hospital admission, 0.24 (95% CI, 0.16-0.37) for mechanical ventilation, 0.19 (95% CI, 0.09-0.40) for ARDS, and 0.38 (95% CI, 0.29-0.51) for mortality. Among the subset of 955 patients younger than 65 years and with a duration of symptoms of at least 5 days, no patients with negative CXR results died, and the negative likelihood ratios were 0.17 (95% CI, 0.12-0.25) for hospital admission, 0.09 (95% CI, 0.02-0.36) for mechanical ventilation, and 0.09 (95% CI, 0.01-0.64) for ARDS.InterpretationInitial CXR in adult patients suspected of having COVID-19 is a strong exclusionary test for hospital admission, mechanical ventilation, ARDS, and mortality. The value of CXR as an exclusionary test for adverse clinical outcomes is highest among young adults, patients with few comorbidities, and those with a prolonged duration of symptoms.
| S-EPMC7844357 | biostudies-literature
Project description:ObjectivesPatients in ICUs often require neuroimaging to rule out a wide variety of intracranial problems. CT may be available in the ICU itself, but MRI has greater sensitivity for many conditions that affect the brain. However, transporting patients who are on ventilators and other life-sustaining devices is a labor-intensive process and involves placing the patient at risk for adverse events. This is a report of portable MRI in a clinical setting.DesignThis is a prospective, nonrandomized, observational study at one institution, utilizing a 0.064-T, self-shielding, portable MRI in ventilated patients in an ICU setting.SettingAcademic medical center.PatientsNineteen patients with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 infection. Patients selected for imaging had any of the following: 1) unexplained encephalopathy or coma, 2) seizures, 3) focal neurologic deficit, or 4) abnormal head CT. Imaging was performed in each patient's ICU room with a portable, self-shielding, 0.064-T MRI.InterventionsNone.Measurements and main resultsAmong 19 patients, 20 MRI scans in seven ICUs were acquired between April 13, 2020, and April 23, 2020. No adverse events to patients or staff from MRI acquisition were reported. In 12 patients, abnormal findings were seen, which included increased fluid attenuated inversion recovery signal (n = 12), hemorrhage (n = 3), and diffusion-weighted imaging positivity (n =3). Imaging led to changes in clinical management in five patients.ConclusionsIn this case series of patients, use of portable MRI has been found to be safe, feasible, and led to changes in clinical management based on imaging results. However, future studies comparing results with other imaging modalities are required to understand fully the extent of its clinical utility.
| S-EPMC7769347 | biostudies-literature
Project description:Respiratory X-ray imaging enhanced by phase contrast has shown improved airway visualization in animal models. Limitations in current X-ray technology have nevertheless hindered clinical translation, leaving the potential clinical impact an open question. Here, we explore phase-contrast chest radiography in a realistic in silico framework. Specifically, we use preprocessed virtual patients to generate in silico chest radiographs by Fresnel-diffraction simulations of X-ray wave propagation. Following a reader study conducted with clinical radiologists, we predict that phase-contrast edge enhancement will have a negligible impact on improving solitary pulmonary nodule detection (6 to 20 mm). However, edge enhancement of bronchial walls visualizes small airways (< 2 mm), which are invisible in conventional radiography. Our results show that phase-contrast chest radiography could play a future role in observing small-airway obstruction (e.g., relevant for asthma or early-stage chronic obstructive pulmonary disease), which cannot be directly visualized using current clinical methods, thereby motivating the experimental development needed for clinical translation. Finally, we discuss quantitative requirements on distances and X-ray source/detector specifications for clinical implementation of phase-contrast chest radiography.
| S-EPMC9910502 | biostudies-literature
Project description:BackgroundTo plan a surgical approach and predict the operative time or bleeding volume, it is important to determine the presence of pleural adhesions before surgery. Dynamic chest radiography (DCR) is a new modality that can dynamically capture X-rays, and we assessed the utility of DCR for detecting pleural adhesions preoperatively.MethodsThe subjects of this study were those who underwent DCR before surgery from January 2020 to May 2022. The preoperative evaluation was performed by three imaging analysis modes, and pleural adhesion was defined as the that spreading to more than 20% of the thoracic cavity and/or taking more than 5 minutes to dissect.ResultsOf the 120 total patients, DCR was performed properly for 119 (99.2%). Accurate preoperative evaluations of pleural adhesions were confirmed in 101 patients (84.9%), with a sensitivity of 64.5%, specificity of 91.0%, positive predictive value of 74.1%, and negative predictive value of 88.0%.ConclusionsDCR was very easy to perform in all preoperative patients with all manner of thoracic disease. We demonstrated the utility of DCR, showing its high specificity and negative predictive value. DCR has the potential to become a common preoperative examination for detecting pleural adhesions with further improvements in software programs.
| S-EPMC10089839 | biostudies-literature
Project description:BackgroundDynamic chest radiography (DCR) is a novel and supplementary examination in respiratory diseases. The investigation of other chest diseases using DCR has been explored, identifying a certain correlation of the pulmonary function test (PFT). However, there is a lack of research using DCR parameters to quantitatively evaluate chest disease. The purpose of this study was to investigate the diagnostic value of DCR for diaphragm paralysis (DP).MethodsThis retrospective study recruited 118 participants, which include 18 patients with DP, 48 healthy volunteers, and 52 patients with respiratory disease. Comparison of DCR parameters relationships among 3 groups was performed using one-way analysis of variance (ANOVA) and Kruskal-Wallis test. The receiver operating characteristic (ROC) curve was used to compare the value of the DCR parameters to diagnose DP.ResultsThe differences of excursion of diaphragm (ED) in normal (nb) and forced breathing (fb), ED(fb)-ED(nb), and the parameters of projected lung area (PLA) in inspiratory (ins) and expiratory phase (exp), PLA.exp(fb), PLA.ins(fb)-PLA.ins(nb), and PLA.exp(fb)-PLA.exp(nb) among the 3 groups were statistically significant. The highest area under the curve (AUC) of right-side parameter was the ED(fb)-ED(nb), for which the AUC was 0.8950 [95% confidence interval (CI): 0.7618-1.000], whereas that of the left-side parameter was ED(fb), for which the AUC was 0.9176 [95% confidence interval (CI): 0.8524-0.9829].ConclusionsThe parameters of DCR have good diagnostic value for DP. The highest diagnostic efficiency for DP on the right side is the ED(fb)-ED(nb), with a sensitivity of 95% and a specificity of 78.6%, whereas on the left side is ED(fb), with a sensitivity of 80% and a specificity of 88.2%.
| S-EPMC11320492 | biostudies-literature
Project description:BackgroundPneumothorax can lead to a life-threatening emergency. The experienced radiologists can offer precise diagnosis according to the chest radiographs. The localization of the pneumothorax lesions will help to quickly diagnose, which will be benefit for the patients in the underdevelopment areas lack of the experienced radiologists. In recent years, with the development of large neural network architectures and medical imaging datasets, deep learning methods have become a methodology of choice for analyzing medical images. The objective of this study was to the construct convolutional neural networks to localize the pneumothorax lesions in chest radiographs.Methods and findingsWe developed a convolutional neural network, called CheXLocNet, for the segmentation of pneumothorax lesions. The SIIM-ACR Pneumothorax Segmentation dataset was used to train and validate CheXLocNets. The training dataset contained 2079 radiographs with the annotated lesion areas. We trained six CheXLocNets with various hyperparameters. Another 300 annotated radiographs were used to select parameters of these CheXLocNets as the validation set. We determined the optimal parameters by the AP50 (average precision at the intersection over union (IoU) equal to 0.50), a segmentation evaluation metric used by several well-known competitions. Then CheXLocNets were evaluated by a test set (1082 normal radiographs and 290 disease radiographs), based on the classification metrics: area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive predictive value (PPV); segmentation metrics: IoU and Dice score. For the classification, CheXLocNet with best sensitivity produced an AUC of 0.87, sensitivity of 0.78 (95% CI 0.73-0.83), and specificity of 0.78 (95% CI 0.76-0.81). CheXLocNet with best specificity produced an AUC of 0.79, sensitivity of 0.46 (95% CI 0.40-0.52), and specificity of 0.92 (95% CI 0.90-0.94). For the segmentation, CheXLocNet with best sensitivity produced an IoU of 0.69 and Dice score of 0.72. CheXLocNet with best specificity produced an IoU of 0.77 and Dice score of 0.79. We combined them to form an ensemble CheXLocNet. The ensemble CheXLocNet produced an IoU of 0.81 and Dice score of 0.82. Our CheXLocNet succeeded in automatically detecting pneumothorax lesions, without any human guidance.ConclusionsIn this study, we proposed a deep learning network, called, CheXLocNet, for the automatic segmentation of chest radiographs to detect pneumothorax. Our CheXLocNets generated accurate classification results and high-quality segmentation masks for the pneumothorax at the same time. This technology has the potential to improve healthcare delivery and increase access to chest radiograph expertise for the detection of diseases. Furthermore, the segmentation results can offer comprehensive geometric information of lesions, which can benefit monitoring the sequential development of lesions with high accuracy. Thus, CheXLocNets can be further extended to be a reliable clinical decision support tool. Although we used transfer learning in training CheXLocNet, the parameters of CheXLocNet was still large for the radiograph dataset. Further work is necessary to prune CheXLocNet suitable for the radiograph dataset.
| S-EPMC7652331 | biostudies-literature