scholarly journals Rama Co-RADS: Cutting-edge tool for improved communication in management and treatment of COVID-19 patients in Thailand

2021 ◽  
Vol 22 (2) ◽  
pp. 29-49
Author(s):  
Thitiporn Suwatanapongched ◽  
Chayanin Nitiwarangkul ◽  
Vanlapa Arnuntasupakul ◽  
Sasisopin Kiertiburanakul
Keyword(s):  
Life Support ◽  
Time Lapse ◽  
Third Wave ◽  
Healthcare Facilities ◽  
X Ray ◽  
Vulnerable Patients ◽  
Number Of Patients ◽  
Patients At Risk ◽  
Chest X Ray ◽  
Constrained Conditions

In Thailand, the rapid and constant rise in the number of confirmed COVID-19 cases, together with the increasing number of patients requiring respiratory and other medical life support during the third wave of COVID-19, has drastically overwhelmed the existing country’s healthcare facilities, physicians, and other healthcare workers. Hence, early identification of vulnerable patients at risk and early COVID-19 pneumonia is crucial for timely management and treatment by antivirals or corticosteroids to prevent them from developing severe COVID-19 pneumonia. A prompt chest X-ray report with clear and concise information at baseline screening in alternative healthcare facilities, especially in resource-constrained conditions, is essential. The article presents the incorporation of Rama Co-RADS (a categorical assessment scheme for chest X-ray findings for diagnosing pneumonia in patients with confirmed COVID-19). Its use facilitates a rapid, clear, and concise X-ray report despite the various levels of radiologists’ experience. Comprehensibleand consistent chest X-ray information successfully reduces the time lapse and communication gap among medical staff and assists on-duty, frontline physicians to make prompt and more accurate decisions regarding the management and treatment of COVID-19 patients in accordance with the current national guideline.  

scholarly journals 1184. Making Pneumonia Surveillance Easy: Automation of Pneumonia Case Detection

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S424-S425
Author(s):  
Dan Ding ◽  
Anna Stachel ◽  
Eduardo Iturrate ◽  
Michael Phillips
Keyword(s):  
Language Processing ◽  
Cost Effective ◽  
The United States ◽  
Surveillance Data ◽  
X Rays ◽  
Healthcare Facilities ◽  
X Ray ◽  
Chest X Ray ◽  
Sensitivity Specificity ◽  
Time Critical

Abstract Background Pneumonia (PNU) is the second most common nosocomial infection in the United States and is associated with substantial morbidity and mortality. While definitions from CDC were developed to increase the reliability of surveillance data, reduce the burden of surveillance in healthcare facilities, and enhance the utility of surveillance data for improving patient safety - the algorithm is still laborious. We propose an implementation of a refined algorithm script which combines two CDC definitions with the use of natural language processing (NLP), a tool which relies on pattern matching to determine whether a condition of interest is reported as present or absent in a report, to automate PNU surveillance. Methods Using SAS v9.4 to write a query, we used a combination of National Healthcare Safety Network’s (NHSN) PNU and ventilator-associated event (VAE) definitions that use discrete fields found in electronic medical records (EMR) and trained an NLP tool to determine whether chest x-ray report was indicative of PNU (Fig1). To validate, we assessed sensitivity/specificity of NLP tool results compared with clinicians’ interpretations. Results The NLP tool was highly accurate in classifying the presence of PNU in chest x-rays. After training the NLP tool, there were only 4% discrepancies between NLP tool and clinicians interpretations of 223 x-ray reports - sensitivity 92.2% (81.1–97.8), specificity 97.1% (93.4–99.1), PPV 90.4% (79.0–96.8), NPV 97.7% (94.1–99.4). Combining the automated use of discrete EMR fields with NLP tool significantly reduces the time spent manually reviewing EMRs. A manual review for PNU without automation requires approximately 10 minutes each day per admission. With a monthly average of 2,350 adult admissions at our hospital and 16,170 patient-days for admissions with at least 2 days, the algorithm saves approximately 2,695 review hours. Conclusion The use of discrete EMR fields with an NLP tool proves to be a timelier, cost-effective yet accurate alternative to manual PNU surveillance review. By allowing an automated algorithm to review PNU, timely reports can be sent to units about individual cases. Compared with traditional CDC surveillance definitions, an automated tool allows real-time critical review for infection and prevention activities. Disclosures All authors: No reported disclosures.

scholarly journals COVID-19 Symptoms Detection Based on NasNetMobile with Explainable AI Using Various Imaging Modalities

2020 ◽  
Vol 2 (4) ◽  
pp. 490-504
Author(s):  
Md Manjurul Ahsan ◽  
Kishor Datta Gupta ◽  
Mohammad Maminur Islam ◽  
Sajib Sen ◽  
Md. Lutfar Rahman ◽  
...  
Keyword(s):  
Ct Scan ◽  
Initial Screening ◽  
X Ray ◽  
Learning Techniques ◽  
Alternative Approach ◽  
Number Of Patients ◽  
The Usa ◽  
Chest X Ray ◽  
Interpretable Model ◽  
Potential Opportunity

The outbreak of COVID-19 has caused more than 200,000 deaths so far in the USA alone, which instigates the necessity of initial screening to control the spread of the onset of COVID-19. However, screening for the disease becomes laborious with the available testing kits as the number of patients increases rapidly. Therefore, to reduce the dependency on the limited test kits, many studies suggested a computed tomography (CT) scan or chest radiograph (X-ray) based screening system as an alternative approach. Thereby, to reinforce these approaches, models using both CT scan and chest X-ray images need to develop to conduct a large number of tests simultaneously to detect patients with COVID-19 symptoms. In this work, patients with COVID-19 symptoms have been detected using eight distinct deep learning techniques, which are VGG16, InceptionResNetV2, ResNet50, DenseNet201, VGG19, MobilenetV2, NasNetMobile, and ResNet15V2, using two datasets: one dataset includes 400 CT scan and another 400 chest X-ray images. Results show that NasNetMobile outperformed all other models by achieving an accuracy of 82.94% in CT scan and 93.94% in chest X-ray datasets. Besides, Local Interpretable Model-agnostic Explanations (LIME) is used. Results demonstrate that the proposed models can identify the infectious regions and top features; ultimately, it provides a potential opportunity to distinguish between COVID-19 patients with others.

scholarly journals Simple predictive models identify patients with COVID-19 pneumonia and poor prognosis

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244627
Author(s):  
Mar Riveiro-Barciela ◽  
Moisés Labrador-Horrillo ◽  
Laura Camps-Relats ◽  
Didac González-Sans ◽  
Meritxell Ventura-Cots ◽  
...  
Keyword(s):  
Predictive Models ◽  
Poor Prognosis ◽  
High Flow ◽  
X Ray ◽  
Inflammatory Parameters ◽  
High Flow Oxygen ◽  
Patients At Risk ◽  
Chest X Ray ◽  
Bootstrap Replication

Background and aims Identification of SARS-CoV-2-infected patients at high-risk of poor prognosis is crucial. We aimed to establish predictive models for COVID-19 pneumonia severity in hospitalized patients. Methods Retrospective study of 430 patients admitted in Vall d’Hebron Hospital (Barcelona) between 03-12-2020 and 04-28-2020 due to COVID-19 pneumonia. Two models to identify the patients who required high-flow-oxygen-support were generated, one using baseline data and another with also follow-up analytical results. Calibration was performed by a 1000-bootstrap replication model. Results 249 were male, mean age 57.9 years. Overall, 135 (31.4%) required high-flow-oxygen-support. The baseline predictive model showed a ROC of 0.800 based on: SpO2/FiO2 (adjusted Hazard Ratio-aHR = 8), chest x-ray (aHR = 4), prior immunosuppressive therapy (aHR = 4), obesity (aHR = 2), IL-6 (aHR = 2), platelets (aHR = 0.5). The cut-off of 11 presented a specificity of 94.8%. The second model included changes on the analytical parameters: ferritin (aHR = 7.5 if ≥200ng/mL) and IL-6 (aHR = 18 if ≥64pg/mL) plus chest x-ray (aHR = 2) showing a ROC of 0.877. The cut-off of 12 exhibited a negative predictive value of 92%. Conclusions SpO2/FiO2 and chest x-ray on admission or changes on inflammatory parameters as IL-6 and ferritin allow us early identification of COVID-19 patients at risk of high-flow-oxygen-support that may benefit from a more intensive disease management.

scholarly journals Comparative outcomes of inpatients with lung collapse managed by bronchoscopic or conservative means

2019 ◽  
Vol 6 (1) ◽  
pp. e000427 ◽  
Author(s):  
Omesh Gopal Toolsie ◽  
Muhammad Adrish ◽  
Syed Arsalan Akhter Zaidi ◽  
Gilda Diaz-Fuentes
Keyword(s):  
Primary Outcome ◽  
Fibreoptic Bronchoscopy ◽  
Inclusion Criteria ◽  
Lung Collapse ◽  
X Ray ◽  
Conservative Group ◽  
Number Of Patients ◽  
Significant Pathology ◽  
Chest X Ray

BackgroundAlthough the incidence and prevalence of atelectatic lung collapse is unknown, such events are common among inpatients, and there are no guidelines for optimally instituting bronchoscopic techniques. The aim of this study was to evaluate the outcomes of patients with complete or near-complete lung collapse managed via interventional flexible fibreoptic bronchoscopy or a conservative approach.MethodsRetrospective analysis of all adult patients admitted to BronxCare Health System between January 2011 and October 2017 with a diagnosis of lung collapse/atelectasis. The primary outcome was radiological resolution. Timing of bronchoscopy relative to radiological resolution and mortality served as secondary outcomes.ResultsOf the 177 patients meeting inclusion criteria, 149 (84%) underwent bronchoscopy and 28 (16%) were managed through conservative measures only. A significantly greater number of patients in the bronchoscopy group achieved complete or near-complete resolution on chest X-ray, compared with the conservative group (p=0.007). Timing of bronchoscopy had no impact on the rate of radiological resolution, and mortality in the two groups was similar. New endobronchial malignancies were identified in 21 patients (14%).ConclusionsOur data support the central role of bronchoscopy in instances of complete or near-complete lung collapse, ensuring better radiological outcomes. Judicious use of conservative management is warranted to avoid missing significant pathology. A prime consideration in this setting is obstructive malignancy.

scholarly journals Immediate chest X-ray for patients at risk of lung cancer presenting in primary care: randomised controlled feasibility trial

2017 ◽  
Vol 116 (3) ◽  
pp. 293-302 ◽  
Author(s):  
Richard D Neal ◽  
Allan Barham ◽  
Emily Bongard ◽  
Rhiannon Tudor Edwards ◽  
Jim Fitzgibbon ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Primary Care ◽  
At Risk ◽  
Feasibility Trial ◽  
X Ray ◽  
Patients At Risk ◽  
Chest X Ray ◽  
Randomised Controlled

scholarly journals Comparison of Chest Ultrasound and Standard X-Ray Imaging in COVID-19 Patients

2020 ◽  
Vol 06 (02) ◽  
pp. E36-E40
Author(s):  
Evgenii Shumilov ◽  
Ali Seif Amir Hosseini ◽  
Golo Petzold ◽  
Hannes Treiber ◽  
Joachim Lotz ◽  
...  
Keyword(s):  
Medical Community ◽  
X Ray ◽  
Chest Ultrasound ◽  
Pleural Line ◽  
X Ray Imaging ◽  
Number Of Patients ◽  
Lung Abnormalities ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
B Lines

AbstractThe COVID-19 pandemic poses new challenges for the medical community due to its large number of patients presenting with varying symptoms. Chest ultrasound (ChUS) may be particularly useful in the early clinical management in suspected COVID-19 patients due to its broad availability and rapid application. We aimed to investigate patterns of ChUS in COVID-19 patients and compare the findings with results from chest X-ray (CRX).24 patients (18 symptomatic, 6 asymptomatic) with confirmed SARS-CoV-2 by polymerase chain reaction underwent bedside ChUS in addition to CRX following admission. Subsequently, the results of ChUS and CRX were compared.94% (n=17/18) of patients with respiratory symptoms demonstrated lung abnormalities on ChUS. ChUS was especially useful to detect interstitial syndrome compared to CXR in COVID-19 patients (17/18 vs. 11/18; p<0.02). Of note, ChUS also detected lung consolidations very effectively (14/18 for ChUS vs. 7/18 cases for CXR; p<0.02). Besides pathological B-lines and subpleural consolidations, pleural line abnormality (89%; n=16/18) was the third most common feature in patients with respiratory manifestations of COVID-19 detected by ChUS.Our findings support the high value of ChUS in the management of COVID-19 patients.

scholarly journals COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images

Diagnostics ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 25
Author(s):  
Hossein Aboutalebi ◽  
Maya Pavlova ◽  
Mohammad Javad Shafiee ◽  
Ali Sabri ◽  
Amer Alaref ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Clinical Decision ◽  
Severity Assessment ◽  
Clinical Workflow ◽  
X Ray ◽  
Number Of Patients ◽  
The World ◽  
Chest X Ray

The world is still struggling in controlling and containing the spread of the COVID-19 pandemic caused by the SARS-CoV-2 virus. The medical conditions associated with SARS-CoV-2 infections have resulted in a surge in the number of patients at clinics and hospitals, leading to a significantly increased strain on healthcare resources. As such, an important part of managing and handling patients with SARS-CoV-2 infections within the clinical workflow is severity assessment, which is often conducted with the use of chest X-ray (CXR) images. In this work, we introduce COVID-Net CXR-S, a convolutional neural network for predicting the airspace severity of a SARS-CoV-2 positive patient based on a CXR image of the patient’s chest. More specifically, we leveraged transfer learning to transfer representational knowledge gained from over 16,000 CXR images from a multinational cohort of over 15,000 SARS-CoV-2 positive and negative patient cases into a custom network architecture for severity assessment. Experimental results using the RSNA RICORD dataset showed that the proposed COVID-Net CXR-S has potential to be a powerful tool for computer-aided severity assessment of CXR images of COVID-19 positive patients. Furthermore, radiologist validation on select cases by two board-certified radiologists with over 10 and 19 years of experience, respectively, showed consistency between radiologist interpretation and critical factors leveraged by COVID-Net CXR-S for severity assessment. While not a production-ready solution, the ultimate goal for the open source release of COVID-Net CXR-S is to act as a catalyst for clinical scientists, machine learning researchers, as well as citizen scientists to develop innovative new clinical decision support solutions for helping clinicians around the world manage the continuing pandemic.

scholarly journals COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images

2021 ◽  
Author(s):  
Hossein Aboutalebi ◽  
Maya Pavlova ◽  
Mohammad Javad Shafiee ◽  
Ali Sabri ◽  
Amer Alaref ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Clinical Decision ◽  
Severity Assessment ◽  
Clinical Workflow ◽  
X Ray ◽  
Number Of Patients ◽  
The World ◽  
Chest X Ray

Abstract The world is still struggling in controlling and containing the spread of the COVID-19 pandemic caused by the SARS-CoV-2 virus. The medical conditions associated with SARS-CoV-2 infections have resulted in a surge in the number of patients at clinics and hospitals, leading to a significantly increased strain on healthcare resources. As such, an important part of managing patients with SARS-CoV-2 infections within the clinical workflow is severity assessment, which is often conducted with the use of chest x-ray (CXR) images. In this work, we introduce COVID-Net CXR-S, a convolutional neural network for predicting the airspace severity of a SARS-CoV-2 positive patient based on a CXR image of the patient's chest. More specifically, we leveraged transfer learning to transfer representational knowledge gained from over 16,000 CXR images from a multinational cohort of over 15,000 patient cases into a custom network architecture for severity assessment. Experimental results with a multi-national patient cohort curated by the Radiological Society of North America (RSNA) RICORD initiative showed that the proposed COVID-Net CXR-S has potential to be a powerful tool for computer-aided severity assessment of CXR images of COVID-19 positive patients. Furthermore, radiologist validation on select cases by two board-certified radiologists with over 10 and 19 years of experience, respectively, showed consistency between radiologist interpretation and critical factors leveraged by COVID-Net CXR-S for severity assessment. While not a production-ready solution, the ultimate goal for the open source release of COVID-Net CXR-S is to act as a catalyst for clinical scientists, machine learning researchers, as well as citizen scientists to develop innovative new clinical decision support solutions for helping clinicians around the world manage the continuing pandemic.

scholarly journals Clinicoepidemiological profile and outcome in patients of covid 19 with normal chest x-ray: study of 51 patients

2020 ◽  
Vol 9 (1) ◽  
pp. 181
Author(s):  
Huma Firdaus ◽  
Nafees Ahmad Khan ◽  
Maqsumi Reza ◽  
Mansoor Ahmad Khan ◽  
Gishnu Krishnan ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Age Group ◽  
Rt Pcr ◽  
X Ray ◽  
Good Outcome ◽  
Number Of Patients ◽  
History Of ◽  
Normal Chest ◽  
Chest X Ray ◽  
Time Of Presentation

Background: Covid 19 was declared a pandemic by WHO on 11 March 2020. Patients usually have pneumonia on chest x-ray at time of presentation however many patients also do not develop pneumonia and have normal chest x-ray.Methods: A total of 51 patients above the age of 15 years diagnosed with covid 19 by RT PCR of nasopharngeal/oropharyngeal samples were included in the study. History of symptoms onset was recorded, chest x-ray and haematological investigations were done of all patients.Results: A total of 51 patients >15 years of age were included in the study. 28 were male and 23 were female patients. Maximum number of patients were in age group 15-30 years. Most common presenting complain was fever (49% patients). Most common comorbidity was diabetes mellitus. There was no mortality reported in patients with normal chest x-ray.Conclusions: We conclude from the current study that patients with normal chest x-ray at the time of presentation have a very good outcome.

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