Development and prospective validation of COVID-19 chest X-ray screening model for patients attending emergency departments

AbstractChest X-rays (CXRs) are the first-line investigation in patients presenting to emergency departments (EDs) with dyspnoea and are a valuable adjunct to clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to facilitate rapid triage of CXRs for further patient testing and/or isolation. In this work we develop an AI algorithm, CovIx, to differentiate normal, abnormal, non-COVID-19 pneumonia, and COVID-19 CXRs using a multicentre cohort of 293,143 CXRs. The algorithm is prospectively validated in 3289 CXRs acquired from patients presenting to ED with symptoms of COVID-19 across four sites in NHS Greater Glasgow and Clyde. CovIx achieves area under receiver operating characteristic curve for COVID-19 of 0.86, with sensitivity and F1-score up to 0.83 and 0.71 respectively, and performs on-par with four board-certified radiologists. AI-based algorithms can identify CXRs with COVID-19 associated pneumonia, as well as distinguish non-COVID pneumonias in symptomatic patients presenting to ED. Pre-trained models and inference scripts are freely available at https://github.com/beringresearch/bravecx-covid.

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Simple method for detecting idiopathic interstitial pneumonias by measuring vertical lung length on chest X-ray

Scientific Reports ◽

10.1038/s41598-021-87452-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Masato Karayama ◽

Yoichiro Aoshima ◽

Hideki Yasui ◽

Hironao Hozumi ◽

Yuzo Suzuki ◽

...

Keyword(s):

Operating Characteristic ◽

Characteristic Curve ◽

Area Under The Curve ◽

X Rays ◽

Simple Method ◽

X Ray ◽

Idiopathic Interstitial Pneumonias ◽

Chest X Ray ◽

Costophrenic Angle ◽

Tracheal Carina

AbstractDetection of idiopathic interstitial pneumonias (IIPs) on chest X-ray is difficult for non-specialist physicians, especially in patients with mild IIPs. The current study aimed to evaluate the usefulness of a simple method for detecting IIPs by measuring vertical lung length (VLL) in chest X-rays to quantify decreased lung volume. A total of 280 consecutive patients with IIPs were randomly allocated to exploratory and validation cohorts, and 140 controls were selected for each cohort by propensity score-matching. Upper (uVLL; from apex to tracheal carina), lower (lVLL; from carina to costophrenic angle), and total VLL (tVLL; from apex to costophrenic angle), and the l/uVLL ratio were measured on chest X-rays. Patients in the exploratory cohort had significantly decreased uVLL, lVLL, tVLL, and l/uVLL ratio compared with controls (all p < 0.001). Receiver operating characteristic curve analyses demonstrated that lVLL (area under the curve [AUC] 0.86, sensitivity 0.65, specificity 0.92), tVLL (AUC 0.83, sensitivity 0.75, specificity 0.80), and l/uVLL ratio (AUC 0.80, sensitivity 0.72, specificity 0.79) had high diagnostic accuracies for IIPs. These results were reproduced in the validation cohort. IIP patients thus have decreased VLLs, and measurements of VLL may thus aid the accurate detection of IIPs.

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Deep Neural Network-Based Screening Model for COVID-19-Infected Patients Using Chest X-Ray Images

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001421510046 ◽

2020 ◽

pp. 2151004 ◽

Cited By ~ 5

Author(s):

Dilbag Singh ◽

Vijay Kumar ◽

Vaishali Yadav ◽

Manjit Kaur

Keyword(s):

Neural Network ◽

Performance Metrics ◽

Automated Analysis ◽

X Rays ◽

X Ray ◽

Screening Model ◽

X Ray Imaging ◽

Adaptive Differential Evolution ◽

Chest X Ray ◽

Almost All

There are limited coronavirus disease 2019 (COVID-19) testing kits, therefore, development of other diagnosis approaches is desirable. The doctors generally utilize chest X-rays and Computed Tomography (CT) scans to diagnose pneumonia, lung inflammation, abscesses, and/or enlarged lymph nodes. Since COVID-19 attacks the epithelial cells that line our respiratory tract, therefore, X-ray images are utilized in this paper, to classify the patients with infected (COVID-19 [Formula: see text]ve) and uninfected (COVID-19 [Formula: see text]ve) lungs. Almost all hospitals have X-ray imaging machines, therefore, the chest X-ray images can be used to test for COVID-19 without utilizing any kind of dedicated test kits. However, the chest X-ray-based COVID-19 classification requires a radiology expert and significant time, which is precious when COVID-19 infection is increasing at a rapid rate. Therefore, the development of an automated analysis approach is desirable to save the medical professionals’ valuable time. In this paper, a deep convolutional neural network (CNN) approach is designed and implemented. Besides, the hyper-parameters of CNN are tuned using Multi-objective Adaptive Differential Evolution (MADE). Extensive experiments are performed by considering the benchmark COVID-19 dataset. Comparative analysis reveals that the proposed technique outperforms the competitive machine learning models in terms of various performance metrics.

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Potential of deep learning in assessing pneumoconiosis depicted on digital chest radiography

Occupational and Environmental Medicine ◽

10.1136/oemed-2019-106386 ◽

2020 ◽

Vol 77 (9) ◽

pp. 597-602

Author(s):

Xiaohua Wang ◽

Juezhao Yu ◽

Qiao Zhu ◽

Shuqiang Li ◽

Zanmei Zhao ◽

...

Keyword(s):

Deep Learning ◽

Operating Characteristic ◽

Characteristic Curve ◽

Classification Performance ◽

X Ray ◽

Testing Dataset ◽

Learning Techniques ◽

History Of ◽

Digital Chest ◽

Chest X Ray

ObjectivesTo investigate the potential of deep learning in assessing pneumoconiosis depicted on digital chest radiographs and to compare its performance with certified radiologists.MethodsWe retrospectively collected a dataset consisting of 1881 chest X-ray images in the form of digital radiography. These images were acquired in a screening setting on subjects who had a history of working in an environment that exposed them to harmful dust. Among these subjects, 923 were diagnosed with pneumoconiosis, and 958 were normal. To identify the subjects with pneumoconiosis, we applied a classical deep convolutional neural network (CNN) called Inception-V3 to these image sets and validated the classification performance of the trained models using the area under the receiver operating characteristic curve (AUC). In addition, we asked two certified radiologists to independently interpret the images in the testing dataset and compared their performance with the computerised scheme.ResultsThe Inception-V3 CNN architecture, which was trained on the combination of the three image sets, achieved an AUC of 0.878 (95% CI 0.811 to 0.946). The performance of the two radiologists in terms of AUC was 0.668 (95% CI 0.555 to 0.782) and 0.772 (95% CI 0.677 to 0.866), respectively. The agreement between the two readers was moderate (kappa: 0.423, p<0.001).ConclusionOur experimental results demonstrated that the deep leaning solution could achieve a relatively better performance in classification as compared with other models and the certified radiologists, suggesting the feasibility of deep learning techniques in screening pneumoconiosis.

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Chest X-ray (mania) in pediatric emergency departments

Jornal de Pediatria ◽

10.2223/1158 ◽

2004 ◽

Vol 80 (2) ◽

pp. 163-166

Author(s):

Jayme Murahovschi

Keyword(s):

Emergency Departments ◽

Pediatric Emergency ◽

X Ray ◽

Chest X Ray

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

Journal of Neonatology ◽

10.1177/09732179211023763 ◽

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.

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Accessory pathway analysis using a multimodal deep learning model

Scientific Reports ◽

10.1038/s41598-021-87631-y ◽

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.

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TLCoV- An automated Covid-19 screening model using Transfer Learning from chest X-ray images

Chaos Solitons & Fractals ◽

10.1016/j.chaos.2021.110713 ◽

2021 ◽

Vol 144 ◽

pp. 110713

Author(s):

Ayan Kumar Das ◽

Sidra Kalam ◽

Chiranjeev Kumar ◽

Ditipriya Sinha

Keyword(s):

Transfer Learning ◽

X Ray ◽

Screening Model ◽

Chest X Ray

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

Case Reports in Radiology ◽

10.1155/2011/659586 ◽

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.

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

10.36106/ijsr/2506669 ◽

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.

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

Journal of Intensive Care Medicine ◽

10.1177/0885066618807859 ◽

2018 ◽

Vol 35 (10) ◽

pp. 1032-1038 ◽

Cited By ~ 1

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.

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