Search Results (4)

Hashimoto’s disease (HD) is the most common cause of hypothyroidism in developed countries. The exact pathomechanism behind it has not been clearly established; however, an interplay of genetic susceptibility, environmental triggers (including diet) and epigenetic factors seems to be involved. Among the latter, increasingly more attention has been paid to some hormonally active substances, known as endocrine disruptors, which are commonly used worldwide. HD has become a condition widely reported in the media, acting as a culprit for inexplicable weight gain, chronic fatigue or weakness. Nevertheless, the recognition of HD is undeniably increasing and represents a major public health burden. At the same time, improving access to imaging tests has increased the number of incidentally diagnosed adrenal tumors. Above all, the widespread use of chest computed tomography (CT) due to the COVID-19 pandemic has contributed to frequent incidental detection of adrenal lesions. Fortunately, a vast majority of these findings are asymptomatic benign tumors with no excessive hormonal activity, and therefore, they are defined as adrenal incidentalomas (AIs). Interestingly, recent studies have indicated that patients with AIs are more prone to obesity and insulin resistance. Although mutual relationships between the thyroid and the adrenal glands have been studied widely, still, little is known about the possible pathophysiological associations between thyroid autoimmunity and the occurrence of adrenal incidentalomas. This article presents a brief review of the common endocrine disorders with a special focus on the frequently coexisting insulin resistance and/or obesity. Furthermore, in response to the recent growing interest in endocrine disruptors, with their transgenerational epigenetic effects that influence hormonal system function, a concise overview of the topic has also been included. Full article

The chest lesion caused by COVID-19 infection pandemic is threatening the lives and well-being of people all over the world. Artificial intelligence (AI)-based strategies are efficient methods for helping radiologists by assessing the vast number of chest X-ray images, which may play a significant role in simplifying and improving the diagnosis of chest lesion caused by COVID-19 infection. Machine learning (ML) and deep learning (DL) are such AI strategies that have helped researchers predict chest lesion caused by COVID-19 infection cases. But ML and DL strategies face challenges like transmission delays, a lack of computing power, communication delays, and privacy concerns. Federated Learning (FL) is a new development in ML that makes it easier to collect, process, and analyze large amounts of multidimensional data. This could help solve the challenges that have been identified in ML and DL. However, FL algorithms send and receive large amounts of weights from client-side trained models, resulting in significant communication overhead. To address this problem, we offer a unified framework combining FL and a particle swarm optimization algorithm (PSO) to speed up the government’s response time to chest lesion caused by COVID-19 infection outbreaks. The Federated Particle Swarm Optimization approach is tested on a multidimensional chest lesion caused by the COVID-19 infection image dataset and the chest X-ray (pneumonia) dataset from Kaggle’s repository. Our research shows that the proposed model works better when there is an uneven amount of data, has lower communication costs, and is therefore more efficient from a network’s point of view. The results of the proposed approach were validated; 96.15% prediction accuracy was achieved for chest lesions caused by the COVID-19 infection dataset, and 96.55% prediction accuracy was achieved for the chest X-ray (pneumonia) dataset. These results can be used to develop a progressive approach for the early detection of chest lesion caused by COVID-19 infection. Full article

Diagnosing COVID-19 infection through the classification of chest images using machine learning techniques faces many controversial problems owing to the intrinsic nature of medical image data and classification architectures. The detection of lesions caused by COVID-19 in the human lung with properties such as location, size, and distribution is more practical and meaningful to medical workers for severity assessment, progress monitoring, and treatment, thus improving patients’ recovery. We proposed a COVID-19-associated lung lesion detector based on an object detection architecture. It correctly learns disease-relevant features by focusing on lung lesion annotation data of medical images. An annotated COVID-19 image dataset is currently nonexistent. We designed our semi-self-supervised method, which can extract knowledge from available annotated pneumonia image data and guide a novice in annotating lesions on COVID-19 images in the absence of a medical specialist. We prepared a sufficient dataset with nearly 8000 lung lesion annotations to train our deep learning model. We comprehensively evaluated our model on a test dataset with nearly 1500 annotations. The results demonstrated that the COVID-19 images annotated by our method significantly enhanced the model’s accuracy by as much as 1.68 times, and our model competes with commercialized solutions. Finally, all experimental data from multiple sources with different annotation data formats are standardized into a unified COCO format and publicly available to the research community to accelerate research on the detection of COVID-19 using deep learning. Full article

Due to the outbreak of lung infections caused by the coronavirus disease (COVID-19), humans have to face an unprecedented and devastating global health crisis. Since chest computed tomography (CT) images of COVID-19 patients contain abundant pathological features closely related to this disease, rapid detection and diagnosis based on CT images is of great significance for the treatment of patients and blocking the spread of the disease. In particular, the segmentation of the COVID-19 CT lung-infected area can quantify and evaluate the severity of the disease. However, due to the blurred boundaries and low contrast between the infected and the non-infected areas in COVID-19 CT images, the manual segmentation of the COVID-19 lesion is laborious and places high demands on the operator. Quick and accurate segmentation of COVID-19 lesions from CT images based on deep learning has drawn increasing attention. To effectively improve the segmentation effect of COVID-19 lung infection, a modified UNet network that combines the squeeze-and-attention (SA) and dense atrous spatial pyramid pooling (Dense ASPP) modules) (SD-UNet) is proposed, fusing global context and multi-scale information. Specifically, the SA module is introduced to strengthen the attention of pixel grouping and fully exploit the global context information, allowing the network to better mine the differences and connections between pixels. The Dense ASPP module is utilized to capture multi-scale information of COVID-19 lesions. Moreover, to eliminate the interference of background noise outside the lungs and highlight the texture features of the lung lesion area, we extract in advance the lung area from the CT images in the pre-processing stage. Finally, we evaluate our method using the binary-class and multi-class COVID-19 lung infection segmentation datasets. The experimental results show that the metrics of Sensitivity, Dice Similarity Coefficient, Accuracy, Specificity, and Jaccard Similarity are 0.8988 (0.6169), 0.8696 (0.5936), 0.9906 (0.9821), 0.9932 (0.9907), and 0.7702 (0.4788), respectively, for the binary-class (multi-class) segmentation task in the proposed SD-UNet. The result of the COVID-19 lung infection area segmented by SD-UNet is closer to the ground truth compared to several existing models such as CE-Net, DeepLab v3+, UNet++, and other models, which further proves that a more accurate segmentation effect can be achieved by our method. It has the potential to assist doctors in making more accurate and rapid diagnosis and quantitative assessment of COVID-19. Full article