scholarly journals Family medicine residents’ skill levels in emergency chest X-ray interpretation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Malak Al Shammari ◽  
Ali Hassan ◽  
Nouf AlShamlan ◽  
Sarah Alotaibi ◽  
Manar Bamashmoos ◽  
...  
Keyword(s):  
Family Medicine ◽  
Diagnostic Accuracy ◽  
General Practitioners ◽  
Diagnostic Radiology ◽  
X Rays ◽  
Diagnostic Confidence ◽  
X Ray ◽  
Chest X Ray ◽  
Radiology Residents ◽  
Family Medicine Residents

Abstract Background Family medicine physicians may encounter a wide variety of conditions, including acute and urgent cases. Considering the limited access to diagnostic investigations in primary care practice, chest X-ray remains the imaging modality of choice. The current study assessed the competency of family medicine residents in the interpretation of chest X-rays for emergency conditions and to compare it with that of diagnostic radiology residents, general practitioners, and medical interns. Methods An online survey was distributed to 600 physicians, including family medicine residents, medical interns, general practitioners, and diagnostic radiology residents. The study included some background information such as gender, years in practice, training type, interest in pulmonary medicine and diagnostic radiology, and having adequate training on the interpretation of chest X-rays. The survey had 10 chest X-ray cases with brief clinical information. Participants were asked to choose the most likely diagnosis and to rate their degree of confidence in the interpretation of the chest X-ray for each case. Results The survey was completed by 205 physicians (response rate = 34.2%). The overall diagnostic accuracy was 63.1% with a significant difference between family medicine and radiology residents (58.0% vs. 90.5%; P < 0.001). The COVID-19 pneumonia (85.4%) and pneumoperitoneum (80.5%) cases had the highest diagnostic accuracy scores. There was a significant correlation between the diagnostic confidence and accuracy (rs = 0.39; P < 0.001). Multivariable regression analysis revealed that being diagnostic radiology residents (odds ratio [OR]: 13.0; 95% confidence interval [CI]: 2.5–67.7) and having higher diagnostic confidence (OR: 2.2; 95% CI: 1.3–3.8) were the only independent predictors of achieving high diagnostic accuracy. Conclusion The competency of family medicine residents in the interpretation of chest X-ray for emergency conditions was far from optimal. The introduction of radiology training courses on emergency conditions seems imperative. Alternatively, the use of tele-radiology in primary healthcare centers should be considered.

Effects of Practice on Detection of Abnormalities in Chest X-Rays

1986 ◽  
Vol 30 (3) ◽  
pp. 309-311
Author(s):  
Raja Parasuraman
Keyword(s):  
Diagnostic Accuracy ◽  
X Rays ◽  
X Ray ◽  
Disease Identification ◽  
Optimal Criterion ◽  
Chest X Ray ◽  
Radiology Residents ◽  
Diagnostic Setting

The performance of radiology residents and staff radiologists in detecting and identifying abnormalities in chest x-rays was examined. 120 chest films were each read five times on five different sessions by each radiologist. Half of the films contained up to eight possible types of abnormality. Practice improved the detectability of abnormalities, as indexed by ROC-based measures, in both groups of readers. However, greater benefits of practice were noted for the residents than for the staff. Staff radiologists, but not the residents, had reporting criteria that were close to the optimal criterion for the diagnostic setting. Accuracy of disease identification also improved with practice in both groups. The results indicate that training and practice can lead to the stabilization of reporting criteria and improvement of diagnostic accuracy in chest x-ray diagnosis.

scholarly journals Higher Accuracy of Lung Ultrasound over Chest X-ray for Early Diagnosis of COVID-19 Pneumonia

2021 ◽  
Vol 18 (7) ◽  
pp. 3481
Author(s):  
Javier Martínez Redondo ◽  
Carles Comas Rodríguez ◽  
Jesús Pujol Salud ◽  
Montserrat Crespo Pons ◽  
Cristina García Serrano ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Lung Ultrasound ◽  
Ultrasound Images ◽  
X Rays ◽  
Rt Pcr ◽  
X Ray ◽  
Lower Specificity ◽  
Chest X Ray ◽  
Interstitial Disease ◽  
Higher Sensitivity

Background: The COVID-19 pandemic rapidly strained healthcare systems worldwide. The reference standard for diagnosis is a positive reverse transcription polymerase chain reaction (RT-PCR) test, but results are not immediate and sensibility is variable. Aim: To evaluate the diagnostic accuracy of lung ultrasound compared to chest X-ray for COVID-19 pneumonia. Design and Setting: A retrospective analysis of symptomatic patients admitted into one primary care centre in Spain between March and September 2020. Method: Patients’ chest X-rays and lung ultrasounds were categorized as normal or pathologic. RT-PCR confirmed COVID-19 infection. Pathologic lung ultrasound images were further categorized as showing either local or diffuse interstitial disease. McNemar and Fisher tests were used to compare diagnostic accuracy. Results: Most of the 212 patients presented fever at admission, either as a standalone symptom (37.74% of patients) or together with others (72.17% of patients). The positive predictive value of the lung ultrasound was 90% for the diffuse interstitial pattern and 46.92% for local pattern. The lung ultrasound had a significantly higher sensitivity (82.75%) (p < 0.001), but lower specificity (71%) than the chest X-ray (54.02% and 86%, respectively) (p = 0.008) for identifying interstitial lung disease. Moreover, sensitivity of the lung ultrasound for severe interstitial disease was 100%, and was significantly higher than the chest X-ray (58.33%) (p = 0.002). Conclusion: The lung ultrasound is more accurate than the chest X-ray for identifying patients with COVID-19 pneumonia and it is especially useful for those presenting diffuse interstitial disease.

Spontaneous Pneumomediastinum: Uncommon Cause of Air Leak in Late Preterm Neonate

2021 ◽  
Vol 35 (2) ◽  
pp. 93-94
Author(s):  
Jyotsna Bhushan ◽  
Shagufta Iqbal ◽  
Abhishek Chopra
Keyword(s):  
Respiratory Distress ◽  
Conservative Management ◽  
Preterm Neonate ◽  
Late Preterm ◽  
Intravenous Fluids ◽  
X Rays ◽  
Blood Gas ◽  
X Ray ◽  
Spontaneous Pneumomediastinum ◽  
Chest X Ray

A clinical case report of spontaneous pneumomediastinum in a late-preterm neonate, chest x-ray showing classical “spinnaker sail sign,” which was managed conservatively and had excellent prognosis on conservative management. Respiratory distress in a preterm neonate is a common clinical finding. Common causes include respiratory distress syndrome, transient tachypnea of the newborn, pneumonia, and pneumothorax. Pneumomediastinum is not very common cause of respiratory distress and more so spontaneous pneumomediastinum. We report here a preterm neonate with spontaneous pneumomediastinum who had excellent clinical recovery with conservative management. A male baby was delivered to G3P1A1 mother at 34 + 6 weeks through caesarean section done due to abruptio placenta. Apgar scores were 8 and 9. Maternal antenatal history was uneventful and there were no risk factors for early onset sepsis. Baby had respiratory distress soon after birth with Silverman score being 2/10. Baby was started on oxygen (O2) by nasal prongs through blender 0.5 l/min, FiO2 25%, and intravenous fluids. Blood gas done was normal. Possibility of transient tachypnea of newborn or mild hyaline membrane disease was kept. Respiratory distress increased at 20 h of life (Silverman score: 5), urgent chest x-ray done revealed “spinnaker sign” suggestive of pneumomediastinum, so baby was shifted to O2 by hood with FiO2 being 70%. Blood gas repeated was normal. Baby was managed conservatively on intravenous fluids and O2 by hood. Baby was gradually weaned off from O2 over next 5 days. As respiratory distress decreased, baby was started on orogastric feed, which baby tolerated well and then was switched to oral feeds. Serial x-rays showed resolution of pneumomediastinum. Baby was discharged on day 7 of life in stable condition on breast feeds and room air.

scholarly journals Accessory pathway analysis using a multimodal deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Makoto Nishimori ◽  
Kunihiko Kiuchi ◽  
Kunihiro Nishimura ◽  
Kengo Kusano ◽  
Akihiro Yoshida ◽  
...  
Keyword(s):  
Deep Learning ◽  
Electrophysiological Study ◽  
Accessory Pathway ◽  
Learning Model ◽  
X Rays ◽  
X Ray ◽  
Wpw Syndrome ◽  
Chest X Ray ◽  
Deep Learning Model ◽  
Ecg Data

AbstractCardiac accessory pathways (APs) in Wolff–Parkinson–White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model.

scholarly journals 3D Imaging in Unilateral Primary Pulmonary Hypoplasia in an Adult: A Case Report

2011 ◽  
Vol 2011 ◽  
pp. 1-6
Author(s):  
Aristida Georgescu ◽  
Crinu Nuta ◽  
Simona Bondari
Keyword(s):  
3D Imaging ◽  
Pulmonary Hypoplasia ◽  
Left Lung ◽  
Left Lobe ◽  
X Rays ◽  
X Ray ◽  
Female Smoker ◽  
Multidetector Computer Tomography ◽  
Biological Tests ◽  
Chest X Ray

Unilateral primary pulmonary hypoplasia is rare in adulthood (UPHA); it is characterized by a decreased number of bronchial segmentation and decreased/absent alveolar air space. Classical chest X-ray may be confusing, and the biological tests are unspecific. We present a case of UPHA in a 60-year-old female, smoker, with 3 term normal deliveries, who presented with late recurrent pneumonias and bronchiectasis-type symptomathology, arterial hypertension, and obesity. Chest X-rays revealed opacity in the left lower pulmonary zone, an apparent hypoaerated upper left lobe and left deviation of the mediastinum. Preoperatory multidetector computer tomography (MDCT) presented a small retrocardiac left lung with 5-6 bronchial segmentation range and cystic appearance. After pneumonectomy the gross specimen showed a small lung with multiple bronchiectasis and small cysts, lined by hyperplasic epithelium, surrounded by stromal fibrosclerosis. We concluded that this UPHA occurred in the 4–7 embryonic weeks, and the 3D MDCT reconstructions offered the best noninvasive diagnosis.

CHEST X RAY SCORE AND CLINICAL SEVERITY IN COVID-19 PATIENTS

2021 ◽  
pp. 31-32
Author(s):  
Sheeba Rana ◽  
Vicky Bakshi ◽  
Yavini Rawat ◽  
Zaid Bin Afroz
Keyword(s):  
Retrospective Study ◽  
Medical Records ◽  
Scoring Systems ◽  
Clinical Severity ◽  
X Rays ◽  
Antigen Test ◽  
X Ray ◽  
Classi Cation ◽  
Chest X Ray ◽  
Male Subjects

INTRODUCTION: Various chest X-ray scoring systems have been discovered and are employed to correlate with clinical severity, outcome and progression of diseases. With, the coronavirus outbreak, few chest radiograph classication were formulated, like the BSTI classication and the Brixia chest X-ray score. Brixia CXR scoring is used for assessing the clinical severity and outcome of COVID-19. This study aims to compare the Brixia CXR score with clinical severity of COVID-19 patients. MATERIAL& METHODS:This was a retrospective study in which medical records of patients aged 18 years or above, who tested for RTPCR or st st Rapid Antigen Test (RAT) for COVID positive from 1 February 2021 to 31 July 2021 (6 months) were taken. These subjects were stratied into mild, moderate and severe patients according to the ICMR guidelines. Chest X Rays were obtained and lesions were classied according to Brixia scoring system. RESULTS: Out of these 375 patients, 123 (32.8%) were female and 252 (67.2%) were male subjects. The average brixia score was 11.12. Average Brixia CXR score for mild, moderate and severe diseased subjects were 5.23, 11.20, and 14.43 respectively. DISCUSSION:The extent of chest x-ray involvement is proportional to the clinical severity of the patient. Although, a perplexing nding was that the average Brixia score of the female subjects were slightly higher than their male counterparts in the same clinical groups. CONCLUSION: Brixia CXR score correlates well with the clinical severity of the COVID-19.

Portable Ventilation/Perfusion Scanning is Useful for Evaluating Clinically Significant Pulmonary Embolism in the ICU Despite Abnormal Chest Radiography

2018 ◽  
Vol 35 (10) ◽  
pp. 1032-1038 ◽  
Author(s):  
Aaron S. Weinberg ◽  
William Chang ◽  
Grace Ih ◽  
Alan Waxman ◽  
Victor F. Tapson
Keyword(s):  
Pulmonary Embolism ◽  
Chest Radiograph ◽  
High Probability ◽  
Hemodynamic Instability ◽  
X Rays ◽  
X Ray ◽  
Abnormal Chest ◽  
Chest X Ray ◽  
Low Probability ◽  
Clinically Significant

Objective: Computed tomography angiography is limited in the intensive care unit (ICU) due to renal insufficiency, hemodynamic instability, and difficulty transporting unstable patients. A portable ventilation/perfusion (V/Q) scan can be used. However, it is commonly believed that an abnormal chest radiograph can result in a nondiagnostic scan. In this retrospective study, we demonstrate that portable V/Q scans can be helpful in ruling in or out clinically significant pulmonary embolism (PE) despite an abnormal chest x-ray in the ICU. Design: Two physicians conducted chart reviews and original V/Q reports. A staff radiologist, with 40 years of experience, rated chest x-ray abnormalities using predetermined criteria. Setting: The study was conducted in the ICU. Patients: The first 100 consecutive patients with suspected PE who underwent a portable V/Q scan. Interventions: Those with a portable V/Q scan. Results: A normal baseline chest radiograph was found in only 6% of patients. Fifty-three percent had moderate, 24% had severe, and 10% had very-severe radiographic abnormalities. Despite the abnormal x-rays, 88% of the V/Q scans were low probability for a PE despite an average abnormal radiograph rating of moderate. A high-probability V/Q for PE was diagnosed in 3% of the population despite chest x-ray ratings of moderate to severe. Six patients had their empiric anticoagulation discontinued after obtaining the results of the V/Q scan, and no anticoagulation was started for PE after a low-probability V/Q scan. Conclusion: Despite the large percentage of moderate-to-severe x-ray abnormalities, PE can still be diagnosed (high-probability scan) in the ICU with a portable V/Q scan. Although low-probability scans do not rule out acute PE, it appeared less likely that any patient with a low-probability V/Q scan had severe hypoxemia or hemodynamic instability due to a significant PE, which was useful to clinicians and allowed them to either stop or not start anticoagulation.

scholarly journals Objective Evaluation of Deep Uncertainty Predictions for COVID-19 Detection

2021 ◽  
Author(s):  
Hamzeh Asgharnezhad ◽  
Afshar Shamsi ◽  
Roohallah Alizadehsani ◽  
Abbas Khosravi ◽  
Saeid Nahavandi ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
Performance Metrics ◽  
Confusion Matrix ◽  
Objective Evaluation ◽  
X Rays ◽  
The Novel ◽  
X Ray ◽  
Uncertainty Estimates ◽  
Chest X Ray ◽  
Novel Concept

Abstract Deep neural networks (DNNs) have been widely applied for detecting COVID-19 in medical images. Existing studies mainly apply transfer learning and other data representation strategies to generate accurate point estimates. The generalization power of these networks is always questionable due to being developed using small datasets and failing to report their predictive confidence. Quantifying uncertainties associated with DNN predictions is a prerequisite for their trusted deployment in medical settings. Here we apply and evaluate three uncertainty quantification techniques for COVID-19 detection using chest X-Ray (CXR) images. The novel concept of uncertainty confusion matrix is proposed and new performance metrics for the objective evaluation of uncertainty estimates are introduced. Through comprehensive experiments, it is shown that networks pertained on CXR images outperform networks pretrained on natural image datasets such as ImageNet. Qualitatively and quantitatively evaluations also reveal that the predictive uncertainty estimates are statistically higher for erroneous predictions than correct predictions. Accordingly, uncertainty quantification methods are capable of flagging risky predictions with high uncertainty estimates. We also observe that ensemble methods more reliably capture uncertainties during the inference. DNN-based solutions for COVID-19 detection have been mainly proposed without any principled mechanism for risk mitigation. Previous studies have mainly focused on on generating single-valued predictions using pretrained DNNs. In this paper, we comprehensively apply and comparatively evaluate three uncertainty quantification techniques for COVID-19 detection using chest X-Ray images. The novel concept of uncertainty confusion matrix is proposed and new performance metrics for the objective evaluation of uncertainty estimates are introduced for the first time. Using these new uncertainty performance metrics, we quantitatively demonstrate where and when we could trust DNN predictions for COVID-19 detection from chest X-rays. It is important to note the proposed novel uncertainty evaluation metrics are generic and could be applied for evaluation of probabilistic forecasts in all classification problems.

scholarly journals Role of Chest X-Ray in Coronavirus Disease and Correlation of Radiological Features with Clinical Outcomes in Indian Patients

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Satyanand Sathi ◽  
Richa Tiwari ◽  
Savita Verma ◽  
Anil Kumar Garg ◽  
Virendra Singh Saini ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Hospital Stay ◽  
Rank Correlation ◽  
X Rays ◽  
X Ray ◽  
Radiological Features ◽  
Spearman’S Rank Correlation ◽  
Spearman’S Rank Correlation Coefficient ◽  
Discharged Patients ◽  
Chest X Ray

Recent literature has reported that radiological features of coronavirus disease (COVID-19) patients are influenced by computed tomography. This study aimed to assess the characteristic chest X-ray features of COVID-19 and correlate them with clinical outcomes of patients. This retrospective study included 120 COVID-19 patients. Baseline chest X-rays and serial chest X-rays were reviewed. A severity index in the form of maximum radiological assessment of lung edema (RALE) score was calculated for each lung, and scores of both the lungs were summed to obtain a final score. The mean ± standard deviation (SD) and frequency (%) were determined, and an unpaired t test, Spearman’s rank correlation coefficient, and logistic regression analyses were performed for statistical analyses. Among 120 COVID-19 patients, 74 (61.67%) and 46 (38.33%) were males and females, respectively; 64 patients (53.33%) had ground-glass opacities (GGO), 55 (45.83%) had consolidation, and 38 (31.67%) had reticular-nodular opacities, with lower zone distribution (50%) and peripheral distribution (41.67%). Baseline chest X-ray showed a sensitivity of 63.3% in diagnosing typical findings of SARS-CoV-2 pneumonia. The maximum RALE score was 2.13 ± 1.9 in hospitalized patients and 0.57 ± 0.77 in discharged patients ( p value <0.0001). Spearman’s rank correlation coefficient between maximum RALE score and clinical outcome parameters was as follows: age, 0.721 ( p value <0.00001); >10 days of hospital stay, 0.5478 ( p value <0.05); ≤10 days of hospital stay, 0.5384 ( p value <0.0001); discharged patients, 0.5433 ( p value <0.0001); and death, 0.6182 ( p value = 0.0568). The logistic regression analysis revealed that maximum RALE scores (0.0932 [0.024–0.367]), (10.730 [2.727–42.206]), (1.258 [0.990–1.598]), and (0.794 [0.625–1.009]) predicted discharge, death, >10 days of hospital stay, and ≤10 days of hospital stay, respectively. The study findings suggested that the RALE score can quantify the extent of COVID-19 and can predict the prognosis of patients.

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