Chest X ray image enhancement using deep contrast diffusion learning

The chest x-ray (CXR) is a diagnostic imaging tool that aids in the early detection and diagnosis of lung abnormalities. Due to scattering radiation, the CXR would have poor contrast, and the diagnosis would be difficult. Although many methods exist, deep learning (DL)-based CXR image enhancement remains difficult due to the amount of contrast that needs to be enhanced and the locations where acceptable contrast must be extracted. In order to improve CXR images, a contrast diffusion network is introduced in this paper. The input image is initially placed through a multi-level contrast-limited adaptive histogram Equalization (CLAHE) process, from which the necessary contrast is extracted and sent into the convolutional neural network (CNN)-based residual learning network together with low contrast CXR. To create the enhanced CXR images, the learned contrast features were diffused over the input image. The amount of contrast to be diffused is determined by multiple levels of CLAHE. Various metrics are used to evaluate the enhanced image's quality. Additionally, the enhanced images are submitted to computer-assisted diagnosis, which improves overall classification efficiency. All of the results are based on the Shenzhen, COVID-CXR, and PadChest datasets. © 2023 Elsevier GmbH

Diagnostic tools and automated decision support systems for COVID-19

This chapter explores the primary and auxiliary diagnostic tools for COVID-19 including molecular, serology, and medical imaging-based techniques, and highlights some Artificial Intelligence (AI)-based systems for automated diagnosis. As a molecular testing method, real-time reverse transcription-polymerase chain reaction can be used for the qualitative detection of RNA of the pathogen in the specimens of the suspected individuals. This viral RNA identification test is explained with necessary details about the assay procedure, along with its sensitivity to the early diagnosis and some associated challenges regarding the stability of its detection with clinically confirmed cases. On the other hand, serology tests are blood-based tests that are used to identify the exposure of a particular pathogen in the specimen by examining the immune response. For serology-based diagnosis, a general description of different assay procedures, with their uses and benefits, is discussed. Medical imaging is another well-practiced diagnostic technique. Specifically, chest X-ray and CT scan are two popular noninvasive imaging modalities that can be used not only for diagnosis and monitoring of the disease progression but also in confirming the clinical tests and observations, and suggesting treatment and post-hospitalization patient management procedures. The current advancements in AI make the development of computer-aided automated diagnosis (CAD) methods remarkably easier, flexible, and accurate through exploiting all the latest technologies. These methods could utilize the available diagnostic images with clinical features even for early-stage diagnosis. Using such CAD systems can largely expedite the medical image screening process, which subsequently could reduce the burden on the clinicians at the outbreak sites. This chapter discusses the current research efforts for AI-based automated diagnosis system developments, along with analyzing the uses and benefits of these methods. © 2021 Elsevier Inc. All rights reserved.

Covid-19 Imaging Tools: How Big Data is Big?

In this paper, considering year 2020 and Covid-19, we analyze medical imaging tools and their performance scores in accordance with the dataset size and their complexity. For this, we mainly consider AI-driven tools that employ two different types of image data, namely chest Computed Tomography (CT) and X-ray. We elaborate on their strengths and weaknesses by taking the following important factors into account: i) dataset size; ii) model fitting criteria (over-fitting and under-fitting); iii) transfer learning in the deep learning era; and iv) data augmentation. Medical imaging tools do not explicitly analyze model fitting. Also, using transfer learning, with fewer data, one could possibly build Covid-19 deep learning model but they are limited to education and training. We observe that, in both image modalities, neither the dataset size nor does data augmentation work well for Covid-19 screening purposes because a large dataset does not guarantee all possible Covid-19 manifestations and data augmentation does not create new Covid-19 cases.

Bidirectional link between diabetes mellitus and coronavirus disease 2019 leading to cardiovascular disease: A narrative review.

Coronavirus disease 2019 (COVID-19) is a global pandemic where several comorbidities have been shown to have a significant effect on mortality. Patients with diabetes mellitus (DM) have a higher mortality rate than non-DM patients if they get COVID-19. Recent studies have indicated that patients with a history of diabetes can increase the risk of severe acute respiratory syndrome coronavirus 2 infection. Additionally, patients without any history of diabetes can acquire new-onset DM when infected with COVID-19. Thus, there is a need to explore the bidirectional link between these two conditions, confirming the vicious loop between "DM/COVID-19". This narrative review presents (1) the bidirectional association between the DM and COVID-19, (2) the manifestations of the DM/COVID-19 loop leading to cardiovascular disease, (3) an understanding of primary and secondary factors that influence mortality due to the DM/COVID-19 loop, (4) the role of vitamin-D in DM patients during COVID-19, and finally, (5) the monitoring tools for tracking atherosclerosis burden in DM patients during COVID-19 and "COVID-triggered DM" patients. We conclude that the bidirectional nature of DM/COVID-19 causes acceleration towards cardiovascular events. Due to this alarming condition, early monitoring of atherosclerotic burden is required in "Diabetes patients during COVID-19" or "new-onset Diabetes triggered by COVID-19 in Non-Diabetes patients".

Point-of-care ultrasound in the COVID-19 era: A scoping review.

In the midst of the COVID-19 pandemic, unprecedented pressure has been added to healthcare systems around the globe. Imaging is a crucial component in the management of COVID-19 patients. Point-of-care ultrasound (POCUS) such as hand-carried ultrasound emerges in the COVID-19 era as a tool that can simplify the imaging process of COVID-19 patients, and potentially reduce the strain on healthcare providers and healthcare resources. The preliminary evidence available suggests an increasing role of POCUS in diagnosing, monitoring, and risk-stratifying COVID-19 patients. This scoping review aims to delineate the challenges in imaging COVID-19 patients, discuss the cardiopulmonary complications of COVID-19 and their respective sonographic findings, and summarize the current data and recommendations available. There is currently a critical gap in knowledge in the role of POCUS in the COVID-19 era. Nonetheless, it is crucial to summarize the current preliminary data available in order to help fill this gap in knowledge for future studies.