Does shifting to professional emergency department staffing affect the decision for chest radiography?

BACKGROUND: The study aims to determine whether shifting to professional emergency department(ED) teams leads to a higher rate of radiologic workup.METHODS: We retrospectively analyzed a total of 2,000 patients presenting to the ED of a tertiary teaching hospital in two time periods: group 1(G1) comprised 1,000 consecutive patients enrolled from December 21, 2012 to January 5, 2013(all patients were examined by an internal medicine specialist);group 2(G2) comprised 1,000 consecutive patients enrolled from December 21, 2018 to January 3, 2019(all patients were examined by an emergency physician).RESULTS: The chest X-ray(CXR) was performed in 40.6% of all patients. There was no difference in the frequency of CXR(38.9% in G1 vs. 42.3% in G2, P=0.152). More CXRs were performed in G2 patients older than 65 years, in female patients older than 65 years, in patients presenting during the evening and night shifts or off-hours, in patients with a history of malignancy, in patients with gastrointestinal bleeding, and in patients with bradycardia, but fewer in patients presenting with arrhythmia. No difference in the rates of pathological CXR was found(47.3% in G1 vs. 52.2% in G2, P=0.186). Compared with G2, higher sensitivity and specificity were obtained for the binary logistic regression model predicting pathological findings in G1.CONCLUSIONS: Shifting to professional ED teams does not increase radiologic workup. By implementing deliberate usage of ultrasound, some self-governing procedures, case-oriented investigations, and center-specific recommendations, unnecessary radiologic workup can be avoided. Professional ED teams could lead to a higher standard of emergency care.

Evaluation of an asymptomatic COVID-19 patient post-surgery with chest radiography: A surgeon’s dilemma

Routine chest radiography is not a requirement in post-surgery cardiac bypass patients.However,the safety of abandoning routine chest radiographs in critically ill patients remains uncertain.Surgery in an asymptomatic coronavirus disease 2019(COVID-19)patient presents additional challenges in postoperative management.Chest radiography remains a valuable tool for assessment of all patients,even a stable one.Management of surgical patients as an emergency in an asymptomatic COVID-19 case remains a surgeon’s dilemma.

Bedside chest radiography of novel influenza A （H7N9） virus infections and follow-up findings after short-time treatment

Background Influenza A （H7Ng） virus infections were first observed in China in March 2013.This type virus can cause severe illness and deaths,the situation raises many urgent questions and global public health concerns.Our purpose was to investigate bedside chest radiography findings for patients with novel influenza A （H7Ng） virus infections and the followup appearances after short-time treatment.Methods Eight hospitalized patients infected with the novel influenza A （H7Ng） virus were included in our study.All of the patients underwent bedside chest radiography after admission,and all had follow-up bedside chest radiography during their first ten days,using AXIOM Aristos MX and/or AMX-Ⅳ portable X-ray units.The exposure dose was generally 90 kV and 5 mAs,and was slightly adjusted according to the weight of the patients.The initial radiography data were evaluated for radiological patterns （ground glass opacity,consolidation,and reticulation）,distribution type （focal,multifocal,and diffuse）,lung zones involved,and appearance at follow-up while the patients underwent therapy.Results All patients presented with bilateral multiple lung involvement.Two patients had bilateral diffuse lesions,three patients had unilateral diffuse lesions of the right lobe with multifocal lesions of the left lobe,and the remaining three had bilateral multifocal lung lesions.The lesions were present throughout bilateral lung zones in three patients,the whole right lung zone in three patients with additional involvement in the left middle and/or lower lung zone（s）,both lower and middle lung zones in one patient,and the right middle and lower in combination with the left lower lung zones in one patient.The most common abnormal radiographic patterns were ground glass opacity （8/8）,and consolidation （8/8）.In three cases examined by CT we also found the pattern of reticulation in combination with CT images.Four patients had bilateral and four had unilateral pleural effusion.After a short period of treatment the pneumonia in one patient had significantly improved and three cases demonstrated disease progression.In four cases the severity of the pneumonia fluctuated.Conclusions In patients with influenza A （H7N9） virus infection,the distribution of the lung lesions are extensive,and the disease usually involves both lung zones.The most common imaging findings are a mixture of ground glass opacity and consolidation.Pleural effusion is common.Most cases have a poor short-time treatment response,and seem to have either rapid progressive radiographic deterioration or fluctuating radiographic changes.Chest radiography is helpful for evaluating patients with severe clinical symptoms and for follow-up evaluation.

Application of artificial intelligence in digital chest radiography reading for pulmonary tuberculosis screening

Currently, the diagnosis of tuberculosis (TB) is mainly based on the comprehensive consideration of the patient’s symptoms and signs, laboratory examinations and chest radiography (CXR). CXR plays a pivotal role to support the early diagnosis of TB, especially when used for TB screening and differential diagnosis. However, high cost of CXR hardware and shortage of certified radiologists poses a major challenge for CXR application in TB screening in resource limited settings. The latest development of artificial intelligence (AI) combined with the accumulation of a large number of medical images provides new opportunities for the establishment of computer-aided detection (CAD) systems in the medical applications, especially in the era of deep learning (DL) technology. Several CAD solutions are now commercially available and there is growing evidence demonstrate their value in imaging diagnosis. Recently, WHO published a rapid communication which stated that CAD may be used as an alternative to human reader interpretation of plain digital CXRs for screening and triage of TB.

Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 diagnosis.However,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning techniques.This study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal cases.Texture information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering filters.Machine learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding diagnosis.The ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different pathologies.Furthermore,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased analysis.This analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered images.Notably,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.

COVID-DeepNet: Hybrid Multimodal Deep Learning System for Improving COVID-19 Pneumonia Detection in Chest X-ray Images

Coronavirus(COVID-19)epidemic outbreak has devastating effects on daily lives and healthcare systems worldwide.This newly recognized virus is highly transmissible,and no clinically approved vaccine or antiviral medicine is currently available.Early diagnosis of infected patients through effective screening is needed to control the rapid spread of this virus.Chest radiography imaging is an effective diagnosis tool for COVID-19 virus and followup.Here,a novel hybrid multimodal deep learning system for identifying COVID-19 virus in chest X-ray(CX-R)images is developed and termed as the COVID-DeepNet system to aid expert radiologists in rapid and accurate image interpretation.First,Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Butterworth bandpass filter were applied to enhance the contrast and eliminate the noise in CX-R images,respectively.Results from two different deep learning approaches based on the incorporation of a deep belief network and a convolutional deep belief network trained from scratch using a large-scale dataset were then fused.Parallel architecture,which provides radiologists a high degree of confidence to distinguish healthy and COVID-19 infected people,was considered.The proposed COVID-DeepNet system can correctly and accurately diagnose patients with COVID-19 with a detection accuracy rate of 99.93%,sensitivity of 99.90%,specificity of 100%,precision of 100%,F1-score of 99.93%,MSE of 0.021%,and RMSE of 0.016%in a large-scale dataset.This system shows efficiency and accuracy and can be used in a real clinical center for the early diagnosis of COVID-19 virus and treatment follow-up with less than 3 s per image to make the final decision.

Accuracy of Patient Age Estimation from Frontal Chest Radiographs of Adult Patients

Objective: The objective of this study is to evaluate the accuracy of patient age estimation from frontal chest radiographs of adult patients. Methods: 195 posterior-anterior chest radiographs without significant abnormalities were shown to 5 staff radiologists and 6 radiology residents, who were asked to provide their estimates of patient age to the nearest decade. Real patient age distribution ranged from 16 to 91 years of age. Results: On average, correct estimate of patient age decade was made in 22% of cases. Staff radiologists were overall more accurate in their estimations compared to residents. Best accuracy was achieved by the radiologist with the most years of clinical experience, however overall accuracy did not tend to correlate with number of years in practice for staff, nor years of post-graduate training for residents. Overall, patient age was most often overestimated. The least accurate estimates were made for patients younger than 20 years and older than 90. Best accuracy was seen for patients between 50 and 70 years of age. For patients between 20 and 90 years of age, overall estimates were within 11 - 15 years of their true age. There was no significant difference in accuracy of age estimation between radiographs of women and men. Conclusions: Average rate of correct age estimation to the nearest decade from normal frontal chest radiographs in our study was 22%. Staff radiologists were more accurate than radiology residents. Best estimates were made for middle-aged patients, and worst for extremes of age.

Results of the Evaluation of the Respiratory Condition of Permanent Workers in an Oil Mill in Benin

Introduction: The process of extracting oil from cotton seeds can create dusty work atmospheres that can cause respiratory problems. The main objective of this study was to determine the prevalence of respiratory problems among permanent workers in an oil mill in Benin. Methods: This cross-sectional study of 52 workers in an oil mill took place in January 2017 as part of the annual medical check-ups of workers. A questionnaire was administered and spirometry using Spirobank II and chest radiography were performed. The spirometry results were interpreted by an occupational physician and a pulmonologist. Data were entered and analyzed using Epidata software. Results: The mean age was 42.7 ± 6.4 years, and 43 of the 52 workers were men. Of these, 58% were in technical production positions and 42% in administrative positions. Most of them had more than 10 years of seniority. The prevalence of respiratory symptoms among production workers was 4 (13%) versus 2 (9%) among administrative workers. A total of 8 (15.4%) abnormal spirometry was identified with 4 obstructive syndrome, 3 restrictive syndrome, 1 a mixed pattern. There were 5 (16.6%) workers in production versus 3 (13.6%) in administration who had abnormal spirometry. The means 25/75 forced expiratory flow (FEF) value of production workers was significantly lower than that of administration workers. Abnormal chest radiographs were 5 (17%) in production workers compared to 3 (14%) in administration workers. Conclusion: Oil mill workers had few respiratory symptoms. However, production workers had more ventilatory disorders than administrative workers. A spirometric follow-up of this group of workers is therefore necessary.