scholarly journals Simple, fast and affordable triaging pathway for COVID-19

2020 ◽  
pp. postgradmedj-2020-138029
Author(s):  
Elizabeth Jane Eggleton

Coronavirus disease 2019 has caused a global pandemic. The majority of patients will experience mild disease, but others will develop a severe respiratory infection that requires hospitalisation. This is causing a significant strain on health services. Patients are presenting at emergency departments with symptoms of dyspnoea, dry cough and fever with varying severity. The appropriate triaging of patients will assist in preventing health services becoming overwhelmed during the pandemic. This is assisted through clinical assessment and various imaging and laboratory investigations, including chest X-ray, blood analysis and identification of viral infection with SARS-CoV-2. Here, a succinct triaging pathway that aims to be fast, reliable and affordable is presented. The hope is that such a pathway will assist health services in appropriately combating the pandemic.

ESC CardioMed ◽  
2018 ◽  
pp. 411-412
Author(s):  
Nicola Sverzellati ◽  
Gianluca Milanese ◽  
Mario Silva

Both the detection and interpretation of focal abnormalities on chest X-ray (CXR) are challenging tasks. CXR accuracy depends on the view (e.g. the supine view has limited sensitivity) and technological equipment. The detection of small focal abnormalities (e.g. lung nodules) varies between anatomical regions according to the presence of dense anatomic structures, such as the bones and the hila. The interpretation of focal abnormalities on CXR is paramount within the whole clinical assessment, because CXR findings can guide the patient’s management, or warrant further investigations, such as computed tomography. Focal lung abnormalities on CXR are still a cornerstone of diagnostic algorithms; however, the radiological approach has progressively changed in the last decade because of the progressive development of both hardware and software applications that enable sensitive detection and accurate characterization.


2020 ◽  
Vol 7 (1) ◽  
pp. e000646 ◽  
Author(s):  
Bruce Kirenga ◽  
Winters Muttamba ◽  
Alex Kayongo ◽  
Christopher Nsereko ◽  
Trishul Siddharthan ◽  
...  

RationaleDetailed data on the characteristics and outcomes of patients with COVID-19 in sub-Saharan Africa are limited.ObjectiveWe determined the clinical characteristics and treatment outcomes of patients diagnosed with COVID-19 in Uganda.MeasurementsAs of the 16 May 2020, a total of 203 cases had been confirmed. We report on the first 56 patients; 29 received hydroxychloroquine (HCQ) and 27 did not. Endpoints included admission to intensive care, mechanical ventilation or death during hospitalisation.Main resultsThe median age was 34.2 years; 67.9% were male; and 14.6% were <18 years. Up 57.1% of the patients were asymptomatic. The most common symptoms were fever (21.4%), cough (19.6%), rhinorrhea (16.1%), headache (12.5%), muscle ache (7.1%) and fatigue (7.1%). Rates of comorbidities were 10.7% (pre-existing hypertension), 10.7% (diabetes) and 7.1% (HIV), Body Mass Index (BMI) of ≥30 36.6%. 37.0% had a blood pressure (BP) of >130/90 mm Hg, and 27.8% had BP of >140/90 mm Hg. Laboratory derangements were leucopenia (10.6%), lymphopenia (11.1%) and thrombocytopenia (26.3%). Abnormal chest X-ray was observed in 14.3%. No patients reached the primary endpoint. Time to clinical recovery was shorter among patients who received HCQ, but this difference did not reach statistical significance.ConclusionMost of the patients with COVID-19 presented with mild disease and exhibited a clinical trajectory not similar to other countries. Outcomes did not differ by HCQ treatment status in line with other concluded studies on the benefit of using HCQ in the treatment of COVID-19.


2020 ◽  
Author(s):  
Amit Kumar Jaiswal ◽  
Prayag Tiwari ◽  
Vipin Kumar Rathi ◽  
Jia Qian ◽  
Hari Mohan Pandey ◽  
...  

The trending global pandemic of COVID-19 is the fastest ever impact which caused people worldwide by severe acute respiratory syndrome~(SARS)-driven coronavirus. However, several countries suffer from the shortage of test kits and high false negative rate in PCR test. Enhancing the chest X-ray or CT detection rate becomes critical. The patient triage is of utmost importance and the use of machine learning can drive the diagnosis of chest X-ray or CT image by identifying COVID-19 cases. To tackle this problem, we propose~COVIDPEN~-~a transfer learning approach on Pruned EfficientNet-based model for the detection of COVID-19 cases. The proposed model is further interpolated by post-hoc analysis for the explainability of the predictions. The effectiveness of our proposed model is demonstrated on two systematic datasets of chest radiographs and computed tomography scans. Experimental results with several baseline comparisons show that our method is on par and confers clinically explicable instances, which are meant for healthcare providers.


AI ◽  
2020 ◽  
Vol 1 (3) ◽  
pp. 418-435
Author(s):  
Khandaker Haque ◽  
Ahmed Abdelgawad

Deep Learning has improved multi-fold in recent years and it has been playing a great role in image classification which also includes medical imaging. Convolutional Neural Networks (CNNs) have been performing well in detecting many diseases including coronary artery disease, malaria, Alzheimer’s disease, different dental diseases, and Parkinson’s disease. Like other cases, CNN has a substantial prospect in detecting COVID-19 patients with medical images like chest X-rays and CTs. Coronavirus or COVID-19 has been declared a global pandemic by the World Health Organization (WHO). As of 8 August 2020, the total COVID-19 confirmed cases are 19.18 M and deaths are 0.716 M worldwide. Detecting Coronavirus positive patients is very important in preventing the spread of this virus. On this conquest, a CNN model is proposed to detect COVID-19 patients from chest X-ray images. Two more CNN models with different number of convolution layers and three other models based on pretrained ResNet50, VGG-16 and VGG-19 are evaluated with comparative analytical analysis. All six models are trained and validated with Dataset 1 and Dataset 2. Dataset 1 has 201 normal and 201 COVID-19 chest X-rays whereas Dataset 2 is comparatively larger with 659 normal and 295 COVID-19 chest X-ray images. The proposed model performs with an accuracy of 98.3% and a precision of 96.72% with Dataset 2. This model gives the Receiver Operating Characteristic (ROC) curve area of 0.983 and F1-score of 98.3 with Dataset 2. Moreover, this work shows a comparative analysis of how change in convolutional layers and increase in dataset affect classifying performances.


2017 ◽  
Vol 34 (12) ◽  
pp. A878.2-A879
Author(s):  
Steve Goodacre ◽  
Kimberley Horspool ◽  
Catherine Nelson-piercy ◽  
Marian Knight ◽  
Neil Shephard ◽  
...  

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Prashant Kumar Shukla ◽  
Jasminder Kaur Sandhu ◽  
Anamika Ahirwar ◽  
Deepika Ghai ◽  
Priti Maheshwary ◽  
...  

COVID-19 is a new disease, caused by the novel coronavirus SARS-CoV-2, that was firstly delineated in humans in 2019.Coronaviruses cause a range of illness in patients varying from common cold to advanced respiratory syndromes such as Severe Acute Respiratory Syndrome (SARS-CoV) and Middle East Respiratory Syndrome (MERS-CoV). The SARS-CoV-2 outbreak has resulted in a global pandemic, and its transmission is increasing at a rapid rate. Diagnostic testing and approaches provide a valuable tool for doctors and support them with the screening process. Automatic COVID-19 identification in chest X-ray images can be useful to test for COVID-19 infection at a good speed. Therefore, in this paper, a framework is designed by using Convolutional Neural Networks (CNN) to diagnose COVID-19 patients using chest X-ray images. A pretrained GoogLeNet is utilized for implementing the transfer learning (i.e., by replacing some sets of final network CNN layers). 20-fold cross-validation is considered to overcome the overfitting quandary. Finally, the multiobjective genetic algorithm is considered to tune the hyperparameters of the proposed COVID-19 identification in chest X-ray images. Extensive experiments show that the proposed COVID-19 identification model obtains remarkably better results and may be utilized for real-time testing of patients.


Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 3550-3550
Author(s):  
Ruochen Li ◽  
Cara Doyle ◽  
Lara N Roberts ◽  
Kathryn Jane Lang ◽  
Emma Gee ◽  
...  

Abstract There are clearly demonstrated links between unprovoked venous thromboembolism (VTE) and underlying malignancy. Previous studies have shown an incidence between 3 and 13% of subsequent cancer diagnoses in patients with unprovoked VTE. National guidance issued in the United Kingdom, 2012 recommend that all patients with a first unprovoked VTE are investigated for occult malignancy with a thorough history and examination, full blood count, liver function tests, calcium, chest X-ray and urinalysis with directed investigation of any positive findings. Additionally, abdomino-pelvic CT scans (and mammography in women) should be considered for all patients over 40 years with first unprovoked VTE without positive findings on basic investigations. We retrospectively reviewed all patients diagnosed with unprovoked VTE at King's College Hospital between April 2014 and March 2015, and results were followed up to July 2015. We excluded as provoked VTEs all cases associated with trauma, known malignancy, recent surgery or hospitalisation, prolonged immobilisation, long-haul travel, hormonal therapy, intravenous drug use, pregnancy or the puerperium. We examined extent of investigations performed and reviewed the incidence of occult malignancy in those with a first unprovoked VTE. Over the period of study, 544 patients were objectively diagnosed with pulmonary embolism (PE) or deep vein thrombosis (DVT). Of these, 140 cases were unprovoked in nature. 75/140 (53.6%) were male, with a median age of 56 years (range 22-97). All 140 patients had initial clinical assessment and bloods tests. 113 (80.7%) patients also had chest X-ray screening performed. Of the remaining 27 patients, 4 were not followed up in our centre. 75 (53.6%) patients had tumour markers taken, 74 (52.9%) patients had abdominal imaging (of which 61, 82.4% had CT abdomen and pelvis, remainder ultrasound) and 3 women had mammography. Tumour markers were abnormal in 26/75 (34.7%). Abdominal imaging was abnormal in 33/66 (50.0%) patients without a subsequent diagnosis of malignancy, with 18/66 (27.3%) requiring additional investigation to definitively exclude malignancy. 8/136 (5.9%) cases of occult malignancy were identified (see Table for characteristics). The majority of patients found to have occult malignancy were diagnosed at an advanced stage, with high subsequent mortality rates and minimal opportunity for intervention. Our findings compare favourably with the findings of the SOME trial with a low incidence of occult malignancy and questionable value of routine abdomino-pelvic imaging for otherwise asymptomatic patients with first unprovoked VTE. Such screening is likely to incur anxiety for patients, incidental findings and higher costs without demonstrable patient benefit. Abnormal tumour markers were common and non-specific and should not be performed routinely following unprovoked VTE. Targeted investigation for individuals with suggestive clinical features or abnormalities on baseline bloods, chest X-ray or urinalysis should be considered. Table 1. Characteristics of patients identified with occult malignancy, time to cancer diagnosis, staging of cancer, treatment received, and mortality Cancer Age/Gender VTE Abnormal basic screen# Tumour markers Time to cancer diagnosis (days) Stage/treatment Time to death* (days) Endometrial 52F Distal DVT No Not done 131 T1aM0N0 ¨C surgery (TAH + BSO) N/A Endometrial 57F Distal DVT Yes CA125 3383 0 No formal staging, metastatic disease, no treatment 45 Pancreatic 52M Proximal DVT Yes CA125 2832 17 No formal staging, metastatic disease, no treatment 19 Pancreatic 57M PE Yes CA125 583, CEA 96 20 No formal staging, metastatic disease, no treatment 65 Lung 85F PE Yes Not done 85 T3N1M1a ¨C chemotherapy N/A Lung 81M PE Yes Not done 0 T4N3M1b ¨C for palliation only 49 Ovarian 69F PE Yes CA125 1224, CEA 6 0 No formal staging, metastatic disease, no treatment 16 Unknown primary 97F Proximal DVT Yes AFP 29, CEA 626, CA125 316 1 No formal staging, metastatic disease, for palliation 6 #basic screen includes clinical assessment, renal/liver function, calcium, chest X-ray and urinalysis; *from time of VTE diagnosis Disclosures Arya: Bayer plc: Research Funding.


2020 ◽  
Vol 66 (8) ◽  
pp. 1157-1163
Author(s):  
Sergio Henrique Loss ◽  
Diego Leite Nunes ◽  
Oellen Stuani Franzosi ◽  
Cassiano Teixeira

SUMMARY There is a new global pandemic that emerged in China in 2019 that is threatening different populations with severe acute respiratory failure. The disease has enormous potential for transmissibility and requires drastic governmental measures, guided by social distancing and the use of protective devices (gloves, masks, and facial shields). Once the need for admission to the ICU is characterized, a set of essentially supportive therapies are adopted in order to offer multi-organic support and allow time for healing. Typically, patients who require ventilatory support have bilateral infiltrates in the chest X-ray and chest computed tomography showing ground-glass pulmonary opacities and subsegmental consolidations. Invasive ventilatory support should not be postponed in a scenario of intense ventilatory distress. The treatment is, in essence, supportive.


Author(s):  
YULI SUN HARIYANI ◽  
SUGONDO HADIYOSO ◽  
THOMHERT SUPRAPTO SIADARI

ABSTRAKPenyakit Coronavirus-2019 atau Covid-19 telah menjadi pandemi global dan menjadi masalah utama yang harus segera dikendalikan. Salah satu cara yang dapat dilakukan adalah memutus rantai penyebaran virus tersebut dengan melakukan deteksi dan melalukan karantina. Pencitraan X-Ray dapat dijadikan alternatif dalam mempelajari Covid-19. X-Ray dianggap mampu menggambarkan kondisi paru-paru pada pasien Covid-19 dan dapat menjadi alat bantu diagnosa klinis. Pada penelitian ini, kami mengusulkan pendekatan deep learning berbasis residual deep network untuk deteksi Covid-19 melalui citra chest X-Ray. Evaluasi yang dilakukan untuk mengetahui performa metode yang diusulkan berupa precision, recall, F1, dan accuracy. Hasil eksperimen menunjukkan bahwa usulan metode ini memberikan precision, recall, F1 dan accuracy masing-masing 0,98, 0,95, 0,97 dan 99%. Pada masa mendatang, studi ini diharapkan dapat divalidasi dan kemudian digunakan untuk melengkapi diagnosa klinis oleh dokter.Kata kunci: Coronavirus-2019, Covid-19, chest X-Ray, deep learning, residual network ABSTRACTCoronavirus-2019 or Covid-19 disease has become a global pandemic and is a major problem that must be stopped immediately. One of the ways that can be done to stop its spreading is to break the spreading chain of the virus by detecting and doing quarantine. X-Ray imaging can be used as an alternative in detecting Covid-19. X-Ray is considered able to describe the condition of the lungs for Covid-19 suspected patients and can be a supporting tool for clinical diagnosis. In this study, we propose a residual based deep learning approach for Covid-19 detection using chest X-Ray images. Evaluation is carried out to determine the performance of the proposed method in the form of precision, recall, F1 and accuracy. Experiments results show that our proposed method provides precision, recall, F1 and accuracy respectively 0.98, 0.95, 0.97 and 99%. In the future, this study is expected to be validated and then used to support clinical diagnoses by doctors.Keywords: Coronavirus-2019, Covid-19, chest X-Ray, deep learning, residual network


Author(s):  
Debaraj Rana ◽  
Swarna Prabha Jena ◽  
Subrat Kumar Pradhan

The Global pandemic declared Corona Virus Disease (COVID 19) has affected severely tothe health of human being over the globe. More than 15 crore around worldwide have been affected by the Novel Corona virus and it is progressing rapidly. Mainly in the health sector, the hospitals are not properly equipped with proper diagnosis system which can detect the disease accurately with less time consumption. The Chest X Ray image are taking less time and cost effective which can be used for detection of COVID 19 even the severity can also be determine form the CXR images. In the current research many researchers are focusing on implementation of Deep learning method for accurate and quick detection of COVID 19 which can help the radiologist for evaluation of the disease. In this review, proposed Deep learning methodology from the literature have been discussed with their experimental data set. This review could help to develop modified architecture which gives more improvement in the diagnosis in term of computational complexity and time consumption


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