Prevalence of COVID Associated Mucormycosis with Antifungal Susceptibility Profiles of Isolates in a Tertiary Care Hospital

Mucormycosis is an angioinvasive opportunistic fungal infection, but these have become emerging pathogens, especially in conditions with underlying predisposing risk factors in a favourable setting. With the exponential rise in COVID-19 cases, there was an increase in the number of mucormycosis cases among them. The global prevalence rate of mucormycosis in COVID-19 globally varies from 0.005 to 1.7 per million population and in India, it is approximately 0.14 cases/1000. The objective of this study is to detect the prevalence of mucormycosis with the antifungal susceptibility pattern among COVID-19 patients admitted in our hospital. A total of 347 COVID-19 and post-COVID-19 patients with symptoms suggestive of mucormycosis were included in this study. Nasal scrapings, debrided necrotic tissue, unhealthy tissue bits and biopsy tissues taken through FESS were processed for mycological examination under sterile conditions. Among the total 347 samples processed, 87(25%) were positive for fungal culture. Among the culture positves 7.8% (25) belong to mucorales. Among the total 87 fungal isolates, the majority of organism isolated was Aspergillus sp(68%), followed by Rhizopus sp (18%). Rhizopus/Aspergillus sp (5%), Mucor species (5%), Rhizomucor sp (2%), Mucor/Aspergillus sp(1%), Curvularia sp (1%) were the other fungi isolated. All the strains of Mucorales were sensitive to Posaconazole and one strain showed resistance to amphotericin B with MIC 8 microg/ml by microbroth dilution method based on CLSI M27 guidelines for Amphotericin B, and Posaconazole.Copyright © The Author(s) 2023.

CT-based severity assessment for COVID-19 using weakly supervised non-local CNN.

Evaluating patient criticality is the foremost step in administering appropriate COVID-19 treatment protocols. Learning an Artificial Intelligence (AI) model from clinical data for automatic risk-stratification enables accelerated response to patients displaying critical indicators. Chest CT manifestations including ground-glass opacities and consolidations are a reliable indicator for prognostic studies and show variability with patient condition. To this end, we propose a novel attention framework to estimate COVID-19 severity as a regression score from a weakly annotated CT scan dataset. It takes a non-locality approach that correlates features across different parts and spatial scales of the 3D scan. An explicit guidance mechanism from limited infection labeling drives attention refinement and feature modulation. The resulting encoded representation is further enriched through cross-channel attention. The attention model also infuses global contextual awareness into the deep voxel features by querying the base CT scan to mine relevant features. Consequently, it learns to effectively localize its focus region and chisel out the infection precisely. Experimental validation on the MosMed dataset shows that the proposed architecture has significant potential in augmenting existing methods as it achieved a 0.84 R-squared score and 0.133 mean absolute difference.

Attention-Based Residual Learning Network for COVID-19 Detection Using Chest CT Images

COVID-19 is a deadly viral infection that is highly contagious and has brought a great loss of human lives and economic resources. Hence, it is very critical to detect the virus at an early stage with high accuracy. Deep learning algorithms are very effective in learning the discriminative features of medical images. It can facilitate the rapid diagnosis of the disease. In this research, different deep learning architectures have been evaluated on a balanced CT image dataset for COVID-19. From the analysis performed, this research proposes a deep learning architecture that performs substantially better than the reviewed models. This research aims to analyze and identify an effective baseline deep architecture on which a model can be built for advanced COVID-19 detection. The architectures analyzed in this research include, AlexNet, DenseNet, GoogLeNet, InceptionV4, ResNet, ShuffleNet, SqueezeNet, and Visual Geometric Group (VGG16). Experimental results indicated that GoogLeNet based network was able to detect COVID-19 with an accuracy of 83.27%. This backbone model was further modified to design a better performing network exclusively for COVID-19. This work proposes an attention-based residual learning block that is integrated with the GoogLeNet backbone. The proposed model performed obtained an accuracy of 91.26%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Contour-enhanced attention CNN for CT-based COVID-19 segmentation.

Accurate detection of COVID-19 is one of the challenging research topics in today's healthcare sector to control the coronavirus pandemic. Automatic data-powered insights for COVID-19 localization from medical imaging modality like chest CT scan tremendously augment clinical care assistance. In this research, a Contour-aware Attention Decoder CNN has been proposed to precisely segment COVID-19 infected tissues in a very effective way. It introduces a novel attention scheme to extract boundary, shape cues from CT contours and leverage these features in refining the infected areas. For every decoded pixel, the attention module harvests contextual information in its spatial neighborhood from the contour feature maps. As a result of incorporating such rich structural details into decoding via dense attention, the CNN is able to capture even intricate morphological details. The decoder is also augmented with a Cross Context Attention Fusion Upsampling to robustly reconstruct deep semantic features back to high-resolution segmentation map. It employs a novel pixel-precise attention model that draws relevant encoder features to aid in effective upsampling. The proposed CNN was evaluated on 3D scans from MosMedData and Jun Ma benchmarked datasets. It achieved state-of-the-art performance with a high dice similarity coefficient of 85.43% and a recall of 88.10%.

AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions.

Background and objective: In recent years, Artificial Intelligence has had an evident impact on the way research addresses challenges in different domains. It has proven to be a huge asset, especially in the medical field, allowing for time-efficient and reliable solutions. This research aims to spotlight the impact of deep learning and machine learning models in the detection of COVID-19 from medical images. This is achieved by conducting a review of the state-of-the-art approaches proposed by the recent works in this field. Methods: The main focus of this study is the recent developments of classification and segmentation approaches to image-based COVID-19 detection. The study reviews 140 research papers published in different academic research databases. These papers have been screened and filtered based on specified criteria, to acquire insights prudent to image-based COVID-19 detection. Results: The methods discussed in this review include different types of imaging modality, predominantly X-rays and CT scans. These modalities are used for classification and segmentation tasks as well. This review seeks to categorize and discuss the different deep learning and machine learning architectures employed for these tasks, based on the imaging modality utilized. It also hints at other possible deep learning and machine learning architectures that can be proposed for better results towards COVID-19 detection. Along with that, a detailed overview of the emerging trends and breakthroughs in Artificial Intelligence-based COVID-19 detection has been discussed as well. Conclusion: This work concludes by stipulating the technical and non-technical challenges faced by researchers and illustrates the advantages of image-based COVID-19 detection with Artificial Intelligence techniques.

Learning distinctive filters for COVID-19 detection from chest X-ray using shuffled residual CNN.

COVID-19 is a deadly viral infection that has brought a significant threat to human lives. Automatic diagnosis of COVID-19 from medical imaging enables precise medication, helps to control community outbreak, and reinforces coronavirus testing methods in place. While there exist several challenges in manually inferring traces of this viral infection from X-ray, Convolutional Neural Network (CNN) can mine data patterns that capture subtle distinctions between infected and normal X-rays. To enable automated learning of such latent features, a custom CNN architecture has been proposed in this research. It learns unique convolutional filter patterns for each kind of pneumonia. This is achieved by restricting certain filters in a convolutional layer to maximally respond only to a particular class of pneumonia/COVID-19. The CNN architecture integrates different convolution types to aid better context for learning robust features and strengthen gradient flow between layers. The proposed work also visualizes regions of saliency on the X-ray that have had the most influence on CNN's prediction outcome. To the best of our knowledge, this is the first attempt in deep learning to learn custom filters within a single convolutional layer for identifying specific pneumonia classes. Experimental results demonstrate that the proposed work has significant potential in augmenting current testing methods for COVID-19. It achieves an F1-score of 97.20% and an accuracy of 99.80% on the COVID-19 X-ray set.