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.

scholarly journals Automated segmentation and diagnosis of pneumothorax on chest X-rays with fully convolutional multi-scale ScSE-DenseNet: a retrospective study

2020 ◽  
Vol 20 (S14) ◽  
Author(s):  
Qingfeng Wang ◽  
Qiyu Liu ◽  
Guoting Luo ◽  
Zhiqin Liu ◽  
Jun Huang ◽  
...  
Keyword(s):  
Retrospective Study ◽  
Automatic Segmentation ◽  
Medical Emergency ◽  
Dice Similarity Coefficient ◽  
X Rays ◽  
X Ray ◽  
Learning Framework ◽  
Multi Scale ◽  
Life Threatening ◽  
Chest X Ray

Abstract Background Pneumothorax (PTX) may cause a life-threatening medical emergency with cardio-respiratory collapse that requires immediate intervention and rapid treatment. The screening and diagnosis of pneumothorax usually rely on chest radiographs. However, the pneumothoraces in chest X-rays may be very subtle with highly variable in shape and overlapped with the ribs or clavicles, which are often difficult to identify. Our objective was to create a large chest X-ray dataset for pneumothorax with pixel-level annotation and to train an automatic segmentation and diagnosis framework to assist radiologists to identify pneumothorax accurately and timely. Methods In this study, an end-to-end deep learning framework is proposed for the segmentation and diagnosis of pneumothorax on chest X-rays, which incorporates a fully convolutional DenseNet (FC-DenseNet) with multi-scale module and spatial and channel squeezes and excitation (scSE) modules. To further improve the precision of boundary segmentation, we propose a spatial weighted cross-entropy loss function to penalize the target, background and contour pixels with different weights. Results This retrospective study are conducted on a total of eligible 11,051 front-view chest X-ray images (5566 cases of PTX and 5485 cases of Non-PTX). The experimental results show that the proposed algorithm outperforms the five state-of-the-art segmentation algorithms in terms of mean pixel-wise accuracy (MPA) with $$0.93\pm 0.13$$ 0.93 ± 0.13 and dice similarity coefficient (DSC) with $$0.92\pm 0.14$$ 0.92 ± 0.14 , and achieves competitive performance on diagnostic accuracy with 93.45% and $$F_1$$ F 1 -score with 92.97%. Conclusion This framework provides substantial improvements for the automatic segmentation and diagnosis of pneumothorax and is expected to become a clinical application tool to help radiologists to identify pneumothorax on chest X-rays.

CHEST RADIOGRAPHS OF COVID-19 PATIENTS AFFECTED IN FIRST AND SECOND COVID WAVE IN INDIA: A COMPARATIVE STUDY

2021 ◽  
pp. 29-30
Author(s):  
Vicky Bakshi ◽  
Sheeba Rana
Keyword(s):  
The Elderly ◽  
X Rays ◽  
Age And Gender ◽  
X Ray ◽  
Classi Cation ◽  
Chest X Ray ◽  
Radiological Pattern ◽  
The Difference ◽  
And Gender ◽  
Second Wave

INTRODUCTION: The COVID-19 pandemic in India is part of the global coronavirus disease pandemic of 2019 (COVID-19), which is caused by the coronavirus that causes severe acute respiratory syndrome (SARS-CoV-2). India was the rst country to report over 400,000 new cases in a 24-hour period on April 30, 2021. The problems with the second wave were increasing manifolds as the symptoms of COVID-19 infections were strange and not common to the rst wave. The majority of those infected in the rst wave were the elderly with various comorbidities, but as the second wave began, the trend shifted, with younger people becoming infected. This study was conducted to evaluate the difference between chest X rays of the subjects affected in the rst and the second wave of COVID19 in India MATERIAL AND METHODS: This was a retrospective study in which chest X ray PA view of 40 COVID positive patients from rst wave of pandemic and 40 such patients from second wave of pandemic were selected. The age and gender of the patient were also noted. Chest X rays were evaluated and classied according to BSTI(11) and Brixia scoring system(12). RESULTS AND DISCUSSION: Out of total 40 patients in the rst wave 14 (35%) were female and 26 (65%) male, whereas in second wave subjects 22 (55%) were male and 18 (45%) female. BSTI classication revealed that classical features of COVID19 pneumonia were more common in the rst wave. Chest X-rays were also classied according to Brixia scoring. The average Brixia score in wave 1 and wave 2 subjects was 6.925 and 8.825 respectively. CONCLUSION: Mutations occurring within the coronavirus and vaccination against it may play a possible role in the difference of radiological pattern and extent of the disease in the consecutive waves.

scholarly journals An artificial intelligence system for predicting mortality in COVID-19 patients using chest X-rays: a retrospective study

2021 ◽  
Author(s):  
Avinash Nanivadekar ◽  
Kapil Zirpe ◽  
Ashutosh Dwivedi ◽  
Rajan Patel ◽  
Richa Pant ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Retrospective Study ◽  
Mortality Prediction ◽  
Clinical Parameters ◽  
X Rays ◽  
Operating Characteristics ◽  
Artificial Intelligence System ◽  
X Ray ◽  
Intelligence System ◽  
Chest X Ray

Background: Early prediction of disease severity in COVID-19 patients is essential. Chest X-ray (CXR) is a faster, widely available, and less expensive imaging modality that may be useful in predicting mortality in COVID-19 patients. Artificial Intelligence (AI) may help expedite CXR reading times, and improve mortality prediction. We sought to develop and assess an artificial intelligence system that used chest X-rays and clinical parameters to predict mortality in COVID-19 patients. Methods: A retrospective study was conducted in Ruby Hall Clinic, Pune, India. The study included patients who had a positive real-time reverse transcriptase-polymerase chain reaction (RT-PCR) test for COVID-19 and at least one available chest X-ray at the time of their initial presentation or admission. Features from CXR images and clinical parameters were used to train the Random Forest model. Results: Clinical data from a total of 201 patients was assessed retrospectively. The average age of the cohort was 51.4 ± 14.8 years, with 29.4% of the patients being over the age of 60. The model, which used CXRs and clinical parameters as inputs, had a sensitivity of 0.83 [95% CI: 0.7, 0.95] and a specificity of 0.7 [95% CI: 0.64, 0.77]. The area under the curve (AUC) on receiver operating characteristics (ROC) was increased from 0.74 [95% CI: 0.67, 0.8] to 0.79 [95% CI: 0.72, 0.85] when the model included features of CXRs in addition to clinical parameters. Conclusion: An Artificial Intelligence (AI) model based on CXRs and clinical parameters demonstrated high sensitivity and can be used as a rapid and reliable tool for COVID-19 mortality prediction.

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.

scholarly journals Correlation between chest x-ray findings and outcomes of patients with mechanical ventilation

2013 ◽  
Vol 53 (1) ◽  
pp. 6
Author(s):  
Indah Nurhayati ◽  
Muhammad Supriatna ◽  
Kamilah Budhi Raharjani ◽  
Eddy Sudijanto
Keyword(s):  
Mechanical Ventilation ◽  
Retrospective Study ◽  
Pleural Effusion ◽  
Respiratory Distress ◽  
Pulmonary Edema ◽  
Patient Outcomes ◽  
Morbidity And Mortality ◽  
Chi Square ◽  
X Ray ◽  
Chest X Ray

Background Most infants and children admitted to the pediatricintensive care unit (PICU) have respiratory distress and pulmonarydisease as underlying conditions. Mechanical ventilation may beused to limit morbidity and mortality in children with respiratoryfailure.Objective To assess a correlation between chest x-ray findingsand outcomes of patients with mechanical ventilation.Methods This retrospective study was held in Dr. KariadiHospital, Semarang, Indonesia. Data was collected from themedical records of children admitted to the PICU from Januaryto December 2010, who suffered from respiratory distress andused mechanical ventilation. We compared chest x-ray findings tothe outcomes of patients. Radiological expertise was provided byradiologists on duty at the time. Chi-square and logistic regressiontests were used for statistical analysis.Results There were 63 subjects in our study, consisting of 28 malesand 35 females. Patient outcomes were defined as survived or died,43 subjects ( 68%) and 20 subjects (3 2%), respectively. Chest x-rayfindings revealed the following conditions: bronchopneumonia48% (P=0.298; 95%CI 0.22 to 1.88), pleural effusion 43%(P=0.280; 95%CI 0.539 to 4.837) , pulmonary edema 6%(P=0.622; 95%CI 0.14 to 14.62) and atelectasis 3% (P=0.538;95%CI 0.03 to 7 .62). None of the chest x-ray findings significantlycorrelated to patient outcomes.Conclusion Chest x-ray findings do not correlate to patientoutcomes in pediatric subjects with mechanical ventilation inthe PICU of Dr. Kariadi Hospital, Semarang, Indonesia.

scholarly journals Chest x-ray findings and outcomes of children with suspected ventilator .. associated pneumonia

2012 ◽  
Vol 52 (4) ◽  
pp. 233
Author(s):  
Neni Sumarni ◽  
Muhammad Sholeh Kosim ◽  
Mohammad Supriatna ◽  
Eddy Sudijanto
Keyword(s):  
Retrospective Study ◽  
Patient Outcomes ◽  
Pulmonary Infection ◽  
Ventilator Associated Pneumonia ◽  
Chi Square ◽  
X Ray ◽  
Chest X Ray ◽  
Hospital Acquired ◽  
Pediatric Icu ◽  
The Relationship

Background Ventilator􀁖associated pneumonia (VAP) is anosocomial infection in patients who have received mechanicalventilation (MV), either by endotracheal intubation ortracheostomy, for more than 48 hours. YAP represents 80% ofall hospital􀁖acquired pneumonias. VAP incidence varies from5.1 %􀁖33.3%. The modified clinical pulmonary infection scoreis a criteria for diagnosing suspected YAP and typically includesradiographic evidence. YAP is associated with significantmorbidity and mortality.Objective To determine the relationship between chest x􀁖rayfindings and outcomes in children Mth suspected VAP.Methods This retrospective study was held in Dr. Kariadi Hospitalfrom January - December 2010. Data was collected from medicalrecords of pediatric ICU (PICU) patients with suspected VAP.Chest x􀁖ray findings and patient outcomes were recorded. X􀁖rayfindings were assessed by the on􀁖duty radiologist. Chi square testwas used for statistical analysis.Results Subjects were 30 children consisting of 14 males and 16females. Patient outcomes were 23 patients survived and 7 patientsdied. Chest x􀁖ray findings were categorized into the followinggroups and compared to patient survivability: diffuse infiltrates76.7% (OR􀁗0.694; P􀁗0.532; 95% CI 0.102 to 4.717), localhedinfiltrates 13.3% (OR􀁗4.200; P􀁗 0.225; 95% CI 0.470 t037.49),and no infiltrates 10% (OR􀁗 1.222; P􀁗 0.436; 95% CI 0.593 to0.926). None of the x􀁖ray findings had a significant correlationto patient outcomes.Conclusion There was no significant relationship between chestx􀁖ray findings and outcomes in children with suspected VAP.[Paediatr rndones. 2012;52:233-8].

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.

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