![]() the chin should not be superimposing any structures.The entire lung fields should be visible superior from the apices inferior to the posterior costophrenic angle anteroposterior to the level of the acromioclavicular joints.inferior to the inferior border of the 12 th rib.superiorly 5 cm above the shoulder joint to allow proper visualization of the upper airways.the midcoronal plane of the level of the 7 th thoracic vertebra, approximately the inferior angle of the scapulae.using the paravertebral gutter technique (see Figure 1) right side rotated 5-10° anterior.midsagittal plane must be perpendicular to the divergent beam, therefore:.arms can be placed on the head or holding onto handles, if available.both arms raised above the head, preventing superimposition over the chest.left shoulder placed firmly against the image receptor.left side of the thorax adjacent to the image receptor.Otherwise, a left lateral view is the default and preferred side as it demonstrates better anatomical detail of the heart. This will allow radiographers/imaging technologists to image with the side of interest against the image receptor, hence reducing any magnification from an increased SID. If locating a specific pulmonary opacity within the chest cavity, it would be useful for requesting doctors to ensure that the side of the opacity is mentioned in their clinical notes. The lateral chest view can be particularly useful in assessing the retrosternal and retrocardiac airspaces. I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.This orthogonal view to a frontal chest radiograph may be performed as an adjunct in cases where there is diagnostic uncertainty. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as. The details of the IRB/oversight body that provided approval or exemption for the research described are given below:Īll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. This research is a part of an academic project. The authors have declared no competing interest. Taken together, here we introduce a state-of-the-art artificial intelligence-based system for efficient COVID-19 detection and a user-friendly application that has the capacity to become a rapid COVID-19 diagnosis method in the near future. Given the practicality of acquiring chest X-ray images by patients, we also developed a web application (link: ) based on our model to directly enable users to upload chest X-ray images and detect the presence of COVID-19 within a few seconds. Our final DLH-COVID model yielded the highest accuracy of 96% in detection of COVID-19 from chest X-ray images when compared to images of both pneumonia-affected and healthy individuals. We used publicly available resources of 6,432 images and further strengthened our model by tuning hyperparameters to provide better generalization during the model validation phase. All these CNN models cater to image classification exercise. Towards this purpose, here we implemented a set of deep learning pre-trained models such as ResNet, VGG, Inception and EfficientNet in conjunction with developing a computer vision AI system based on our own convolutional neural network (CNN) model: Deep Learning in Healthcare (DLH)-COVID. Thus, artificial intelligence techniques powered by deep learning algorithms, which learn from radiography images and predict presence of COVID-19 have potential to enhance current diagnosis process. However, manual detection of COVID-19 from a set of chest X-ray images comprising both COVID-19 and pneumonia cases is cumbersome and prone to human error. Therefore, chest X-ray image-based disease classification has emerged as an alternative to aid medical diagnosis. Early studies identified abnormalities in chest X-ray images of COVID-19 infected patients that could be beneficial for disease diagnosis. Since these symptoms also appear in pneumonia patients, this creates complications in COVID-19 detection especially during the flu season. While COVID-19 patients are known to exhibit a variety of symptoms, the major symptoms include fever, cough, and fatigue. The rise of the coronavirus disease 2019 (COVID-19) pandemic has made it necessary to improve existing medical screening and clinical management of this disease.
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