Role of CXR and HRCT in diagnosing COVID-19, a descriptive cross-sectional study, at a tertiary care hospital in Pakistan

ABSTRACT Objectives: Objectives of this study are to do the analysis of chest X-ray and High-resolution CT scan findings in patients who are clinical suspects of COVID-19 infection. The other objective is to classify the radiological findings in mild, moderate or severe diseases according to BSTI criteria for chest X-ray and CTSS for high-resolution CT scan. Methods: This is a cross-sectional descriptive study. A group of 50 patients who were clinically suspected cases of COVID-19 infection, presented to Corona flu filter clinic of Holy Family Hospital (HFH) or admitted to corona isolation wards were included. The time duration of the study was from 15 May 2020 to 15 June 2020. Patients labelled as clinically suspected cases were having positive contact with confirmed positive (based upon positive PCR) patients. Recent travel history from the area having an outbreak. They were having clinical signs/symptoms of fever, cough, and shortness of breath, lethargy and loss of sense of smell or taste. CXR and HRCT was the investigation of choice for all the 50 patients.  I also did PCR to make a correlation with the other two tests. All radiological findings were analyzed based upon Fleischner society glossary of terms for thoracic imaging. Two radiologists then assessed CXRs findings based upon BSTI criteria. They marked those CXR findings as low, moderate and high probability for COVID-19 infection. HRCT findings were analyzed using CT-SS, and researchers labelled outcomes as mild, moderate and severe disease.  Results: Out of 50 patients, 33(66%) were males, and 17(34%) were females. Mean age was 51 with ages ranging from 30-72 years. Presenting complaints were fever in 42(84%) patients, cough in 37(74%), lethargy in 33(66%), shortness of breath in 41(82%) and loss of sense of smell and taste in 21(42%) patients. Out of these 50, 32(72%) were having positive PCR for COVID-19 infection. On CXR 5(10%) patients showed classic findings which were highly probable for COVID-19. 19(38%) patients showed intermediate results for COVID-19, 7(14%) patients had a low probability of COVID-19 infection on CXRS. Out of 50, 19(38%), patients showed normal CXR with no evidence of COVID-19 infection. We did HRCT of the same patients on the same day; it showed 21(42%)patients with mild disease,23(46%)patients with moderate disease and 6(12%)patients with the severe disease according to CTSI.HRCT of 3(6%)patients was ok with no evidence of illness in bilateral lungs.    Conclusion: The role of radiology is crucial in the diagnosis of this viral illness. CXR, with its ability to detect changes of COVID-19 in lungs, should be used as a first-line imaging modality in clinically suspected patients. Moreover, it should also be used for follow up of patients with COVID-19. HRCT is very sensitive in the diagnosis of COVID-19 infection in its milder forms. Due to lack of its widespread availability in countries with inadequate medical facilities, it was not the primary imaging tool/screening tool. Due to risk of infection to radiological staff as well as non-covid-19 patients due to surface contact, due to reduced infection control issues, due to increased burden of ionizing radiations in patients. All these factors limit the role of HRCT as a primary imaging modality for COVID-19 infectio

A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)

Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and has become one of the most acute and severe ailments in the past hundred years. The prevalence rate of COVID-19 is rapidly rising every day throughout the globe. Although no vaccines for this pandemic have been discovered yet, deep learning techniques proved themselves to be a powerful tool in the arsenal used by clinicians for the automatic diagnosis of COVID-19. This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities like Computer Tomography (CT) and X-ray. This review specifically discusses the systems developed for COVID-19 diagnosis using deep learning techniques and provides insights on well-known data sets used to train these networks. It also highlights the data partitioning techniques and various performance measures developed by researchers in this field. A taxonomy is drawn to categorize the recent works for proper insight. Finally, we conclude by addressing the challenges associated with the use of deep learning methods for COVID-19 detection and probable future trends in this research area. This paper is intended to provide experts (medical or otherwise) and technicians with new insights into the ways deep learning techniques are used in this regard and how they potentially further works in combatting the outbreak of COVID-19.Comment: 18 pages, 2 figures, 4 Table

Adversarial Machine Learning For Advanced Medical Imaging Systems

Although deep neural networks (DNNs) have achieved significant advancement in various challenging tasks of computer vision, they are also known to be vulnerable to so-called adversarial attacks. With only imperceptibly small perturbations added to a clean image, adversarial samples can drastically change models’ prediction, resulting in a significant drop in DNN’s performance. This phenomenon poses a serious threat to security-critical applications of DNNs, such as medical imaging, autonomous driving, and surveillance systems. In this dissertation, we present adversarial machine learning approaches for natural image classification and advanced medical imaging systems. We start by describing our advanced medical imaging systems to tackle the major challenges of on-device deployment: automation, uncertainty, and resource constraint. It is followed by novel unsupervised and semi-supervised robust training schemes to enhance the adversarial robustness of these medical imaging systems. These methods are designed to tackle the unique challenges of defending against adversarial attacks on medical imaging systems and are sufficiently flexible to generalize to various medical imaging modalities and problems. We continue on developing novel training scheme to enhance adversarial robustness of the general DNN based natural image classification models. Based on a unique insight into the predictive behavior of DNNs that they tend to misclassify adversarial samples into the most probable false classes, we propose a new loss function as a drop-in replacement for the cross-entropy loss to improve DNN\u27s adversarial robustness. Specifically, it enlarges the probability gaps between true class and false classes and prevents them from being melted by small perturbations. Finally, we conclude the dissertation by summarizing original contributions and discussing our future work that leverages DNN interpretability constraint on adversarial training to tackle the central machine learning problem of generalization gap