A mask-guided attention deep learning model for COVID-19 diagnosis based on an integrated CT scan images database

The global extent of COVID-19 mutations and the consequent depletion of hospital resources highlighted the necessity of effective computer-assisted medical diagnosis. COVID-19 detection mediated by deep learning models can help diagnose this highly contagious disease and lower infectivity and mortality rates. Computed tomography (CT) is the preferred imaging modality for building automatic COVID-19 screening and diagnosis models. It is well-known that the training set size significantly impacts the performance and generalization of deep learning models. However, accessing a large dataset of CT scan images from an emerging disease like COVID-19 is challenging. Therefore, data efficiency becomes a significant factor in choosing a learning model. To this end, we present a multi-task learning approach, namely, a mask-guided attention (MGA) classifier, to improve the generalization and data efficiency of COVID-19 classification on lung CT scan images. The novelty of this method is compensating for the scarcity of data by employing more supervision with lesion masks, increasing the sensitivity of the model to COVID-19 manifestations, and helping both generalization and classification performance. Our proposed model achieves better overall performance than the single-task (without MGA module) baseline and state-of-the-art models, as measured by various popular metrics.

Trends in electroanalytical assays for COVID-19 diagnosis

The use of electrochemical biosensors is highlighted for SARS-CoV-2 detection and COVID-19 diagnosis. In a brief description of virus structure, fundamental features of proteins and nucleic acid are approached for a comprehensive strategy over biosensor designs. Relevant works are described and related to specific structural proteins used as viral biomarkers. Furthermore, the challenges and perspectives are pointed to the evolution of electroanalysis and the establishment of methods comparable to the gold standard, RT-PCR. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. All rights reserved.

Low-dose computed tomography in COVID-19: systematic review

BACKGROUND: The increased number of computed tomography scans during the COVID-19 pandemic has emphasized the task of decreasing radiation exposure of patients, since it is known to be associated with an elevated risk of cancer development. The ALARA (as low as reasonably achievable) principle, proposed by the International Commission on Radiation Protection, should be adhered to in the operation of radiation diagnostics departments, even during the pandemic. AIM: To systematize data on the appropriateness and effectiveness of low-dose computed tomography in the diagnosis of lung lesions in COVID-19. MATERIALS AND METHODS: Relevant national and foreign literature in scientific libraries PubMed and eLIBRARY, using English and Russian queries "low-dose computed tomography” and "COVID-19,” published between 2020 and 2022 were analyzed. Publications were evaluated after assessing the relevance to the review topic by title and analysis. The references were further analyzed to identify articles omitted during the search that may meet the inclusion criteria. RESULTS: Published studies summarized the current data on the imaging of COVID-19 lung lesions and the use of computed tomography scans and identified possible options for reducing the effective dose. CONCLUSION: We present techniques to reduce radiation exposure during chest computed tomography and preserve high-quality diagnostic images potentially sufficient for reliable detection of COVID-19 signs. Reducing radiation dose is a valid approach to obtain relevant diagnostic information, preserving opportunities for the introduction of advanced computational analysis technologies in clinical practice. © Eco-Vector, 2023.

Artificial Intelligence-based Speech Signal for COVID-19 Diagnostics

The speech signal has numerous features that represent the characteristics of a specific language and recognize emotions. It also contains information that can be used to identify the mental, psychological, and physical states of the speaker. Recently, the acoustic analysis of speech signals offers a practical, automated, and scalable method for medical diagnosis and monitoring symptoms of many diseases. In this paper, we explore the deep acoustic features from confirmed positive and negative cases of COVID-19 and compare the performance of the acoustic features and COVID-19 symptoms in terms of their ability to diagnose COVID-19. The proposed methodology consists of the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images to extract deep audio features. In addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 from acoustic features compared to COVID-19 symptoms, achieving an accuracy of 97%. The experimental results show that the proposed method remarkably improves the accuracy of COVID-19 detection over the handcrafted features used in previous studies. © 2022 ACM.

Bright photon upconversion in LiYbF4:Tm3+@LiYF4 nanoparticles and their application for singlet oxygen generation and in immunoassay for SARS-CoV-2 nucleoprotein.

Photon upconversion is an intensively investigated phenomenon in the materials sciences due to its unique applications, mainly in biomedicine for disease prevention and treatment. This study reports the synthesis and properties of tetragonal LiYbF4:Tm3+@LiYF4 core@shell nanoparticles (NPs) and their applications. The NPs had sizes ranging from 18.5 to 23.7 nm. As a result of the energy transfer between Yb3+ and Tm3+ ions, the synthesized NPs show intense emission in the ultraviolet (UV) range up to 347 nm under 975 nm excitation. The bright emission in the UV range allows for singlet oxygen generation in the presence of hematoporphyrin on the surface of NPs. Our studies show that irradiation with a 975 nm laser of the functionalized NPs allows for the production of amounts of singlet oxygen easily detectable by Singlet Oxygen Sensor Green. The high emission intensity of NPs at 800 nm allowed the application of the synthesized NPs in an upconversion-linked immunosorbent assay (ULISA) for highly sensitive detection of the nucleoprotein from SARS-CoV-2, the causative agent of Covid-19. This article proves that LiYbF4:Tm3+@LiYF4 core@shell nanoparticles can be perfect alternatives for the most commonly studied upconverting NPs based on the NaYF4 host compound and are good candidates for biomedical applications.

Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review.

During the early phase of the COVID-19 pandemic, reverse transcriptase-polymerase chain reaction (RT-PCR) testing faced limitations, prompting the exploration of machine learning (ML) alternatives for diagnosis and prognosis. Providing a comprehensive appraisal of such decision support systems and their use in COVID-19 management can aid the medical community in making informed decisions during the risk assessment of their patients, especially in low-resource settings. Therefore, the objective of this study was to systematically review the studies that predicted the diagnosis of COVID-19 or the severity of the disease using ML. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), we conducted a literature search of MEDLINE (OVID), Scopus, EMBASE, and IEEE Xplore from January 1 to June 31, 2020. The outcomes were COVID-19 diagnosis or prognostic measures such as death, need for mechanical ventilation, admission, and acute respiratory distress syndrome. We included peer-reviewed observational studies, clinical trials, research letters, case series, and reports. We extracted data about the study's country, setting, sample size, data source, dataset, diagnostic or prognostic outcomes, prediction measures, type of ML model, and measures of diagnostic accuracy. Bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO), with the number CRD42020197109. The final records included for data extraction were 66. Forty-three (64%) studies used secondary data. The majority of studies were from Chinese authors (30%). Most of the literature (79%) relied on chest imaging for prediction, while the remainder used various laboratory indicators, including hematological, biochemical, and immunological markers. Thirteen studies explored predicting COVID-19 severity, while the rest predicted diagnosis. Seventy percent of the articles used deep learning models, while 30% used traditional ML algorithms. Most studies reported high sensitivity, specificity, and accuracy for the ML models (exceeding 90%). The overall concern about the risk of bias was "unclear" in 56% of the studies. This was mainly due to concerns about selection bias. ML may help identify COVID-19 patients in the early phase of the pandemic, particularly in the context of chest imaging. Although these studies reflect that these ML models exhibit high accuracy, the novelty of these models and the biases in dataset selection make using them as a replacement for the clinicians' cognitive decision-making questionable. Continued research is needed to enhance the robustness and reliability of ML systems in COVID-19 diagnosis and prognosis.

Momentum contrast transformer for COVID-19 diagnosis with knowledge distillation.

Intelligent diagnosis has been widely studied in diagnosing novel corona virus disease (COVID-19). Existing deep models typically do not make full use of the global features such as large areas of ground glass opacities, and the local features such as local bronchiolectasis from the COVID-19 chest CT images, leading to unsatisfying recognition accuracy. To address this challenge, this paper proposes a novel method to diagnose COVID-19 using momentum contrast and knowledge distillation, termed MCT-KD. Our method takes advantage of Vision Transformer to design a momentum contrastive learning task to effectively extract global features from COVID-19 chest CT images. Moreover, in transfer and fine-tuning process, we integrate the locality of convolution into Vision Transformer via special knowledge distillation. These strategies enable the final Vision Transformer simultaneously focuses on global and local features from COVID-19 chest CT images. In addition, momentum contrastive learning is self-supervised learning, solving the problem that Vision Transformer is challenging to train on small datasets. Extensive experiments confirm the effectiveness of the proposed MCT-KD. In particular, our MCT-KD is able to achieve 87.43% and 96.94% accuracy on two publicly available datasets, respectively.

GIONet: Global information optimized network for multi-center COVID-19 diagnosis via COVID-GAN and domain adversarial strategy.

The outbreak of coronavirus disease (COVID-19) in 2019 has highlighted the need for automatic diagnosis of the disease, which can develop rapidly into a severe condition. Nevertheless, distinguishing between COVID-19 pneumonia and community-acquired pneumonia (CAP) through computed tomography scans can be challenging due to their similar characteristics. The existing methods often perform poorly in the 3-class classification task of healthy, CAP, and COVID-19 pneumonia, and they have poor ability to handle the heterogeneity of multi-centers data. To address these challenges, we design a COVID-19 classification model using global information optimized network (GIONet) and cross-centers domain adversarial learning strategy. Our approach includes proposing a 3D convolutional neural network with graph enhanced aggregation unit and multi-scale self-attention fusion unit to improve the global feature extraction capability. We also verified that domain adversarial training can effectively reduce feature distance between different centers to address the heterogeneity of multi-center data, and used specialized generative adversarial networks to balance data distribution and improve diagnostic performance. Our experiments demonstrate satisfying diagnosis results, with a mixed dataset accuracy of 99.17% and cross-centers task accuracies of 86.73% and 89.61%.

[Validation of a symptom scale for COVID-19 patients in ambulatory care]

Background: A symptom scale can be useful for the standardization of clinical evaluations and follow-up of COVID-19 patients in ambultaroy care. Scale development should be accompanied by an assessment of its reliablility and validity. Objective: To develop and measure the psychometric characteristics of a COVID-19 symptom scale to be answered by either healthcare personnel or adult patients in ambulatory care. Material and methods: The scale was developed by an expert panel using the Delphi method. We evaluated inter-rater reliability, where we defined a good correlation if Spearman's Rho was >= 0.8;test-retest, where we defined a good correlation if Spearman's Rho was >= 0.7;factor analysis using principal component methodology;and discriminant validity using Mann-Whitney's U test. A p < 0.05 was considered statistically significant. Results: We obtained an 8 symptom scale, each symptom is scored from 0-4, with a total minimum score of 0 and a maximum of 32 points. Inter-rater reliability was 0.995 (n = 31), test-retest showed correlation of 0.88 (n = 22), factor analysis detected 4 factors (n = 40) and discriminant capacity of healthy versus sick adults was significant (p < 0.0001, n = 60). Conclusions: We obtained a reliable and valid Spanish (from Mexico) symptom scale for COVID-19 ambulatory care, answerable by patients and health care staff. Copyright © 2023 Revista Medica del Instituto Mexicano del Seguro Social.

Low-dose computed tomography in COVID-19: systematic review

BACKGROUND: The increased number of computed tomography scans during the COVID-19 pandemic has emphasized the task of decreasing radiation exposure of patients, since it is known to be associated with an elevated risk of cancer development. The ALARA (as low as reasonably achievable) principle, proposed by the International Commission on Radiation Protection, should be adhered to in the operation of radiation diagnostics departments, even during the pandemic. AIM: To systematize data on the appropriateness and effectiveness of low-dose computed tomography in the diagnosis of lung lesions in COVID-19. MATERIALS AND METHODS: Relevant national and foreign literature in scientific libraries PubMed and eLIBRARY, using English and Russian queries "low-dose computed tomography” and "COVID-19,” published between 2020 and 2022 were analyzed. Publications were evaluated after assessing the relevance to the review topic by title and analysis. The references were further analyzed to identify articles omitted during the search that may meet the inclusion criteria. RESULTS: Published studies summarized the current data on the imaging of COVID-19 lung lesions and the use of computed tomography scans and identified possible options for reducing the effective dose. CONCLUSION: We present techniques to reduce radiation exposure during chest computed tomography and preserve high-quality diagnostic images potentially sufficient for reliable detection of COVID-19 signs. Reducing radiation dose is a valid approach to obtain relevant diagnostic information, preserving opportunities for the introduction of advanced computational analysis technologies in clinical practice. © Eco-Vector, 2023.

SARS-CoV-2 antigen detection by saliva; an alternative to nasopharyngeal specimen: A cross-sectional study.

Background and Aims: Saliva samples are less invasive and more convenient for patients than naso- and/or oropharynx swabs (NOS). However, there is no US Food and Drug Administration-approved severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rapid antigen test kit, which can be useful in a prolonged pandemic to reduce transmission by allowing suspected individuals to self-sampling. We evaluated the performances of High sensitive AQ+ Rapid SARS-CoV-2 Antigen Test (AQ+ kit) using nasopharyngeal swabs (NPs) and saliva specimens from the same patients in laboratory conditions. Methods: The real-time reverse transcription-polymerase chain reaction (rRT-PCR) test result was used for screening the inrolled individuals and compared as the gold standard. NP and saliva samples were collected from 100 rRT-PCR positives and 100 negative individuals and tested with an AQ+ kit. Results: The AQ+ kit showed good performances in both NP and saliva samples with an overall accuracy of 98.5% and 94.0%, and sensitivity of 97.0% and 88.0%, respectively. In both cases, specificity was 100%. AQ+ kit performance with saliva was in the range of the World Health Organization recommended value. Conclusion: xOur findings indicate that the saliva specimen can be used as an alternative and less invasive to NPs for quick and reliable SARS-CoV-2 antigen detection.

Automatic Diagnosis of COVID-19 Based on Machine Learning

The lives and health of individuals are significantly threatened by the extremely infectious and dangerous Corona Virus Disease 2019 (COVID-19). For the containment of the epidemic, quick and precise COVID-19 detection and diagnosis are essential. Currently, artificial diagnosis based on medical imaging and nucleic acid detection are the major approaches used for COVID-19 detection and diagnosis. However, nucleic acid detection takes a long time and requires a dedicated test box, while manual diagnosis based on medical images relies too much on professional knowledge, and analysis takes a long time, and it is difficult to find hidden lesions. Thanks to the rapid development of pattern recognition algorithms, building a COVID-19 diagnostic model based on machine learning and clinical symptoms has become a feasible rapid detection solution. In this paper, support vector machines and random forest algorithms are used to build a COVID-19 diagnostic model, respectively. Based on the quantitative comparison of the performance of the two methods, the future development trends in this field are discussed. © 2023 IEEE.

Artificial Intelligence Based COVID-19 Detection and Classification Model on Chest X-ray Images.

Diagnostic and predictive models of disease have been growing rapidly due to developments in the field of healthcare. Accurate and early diagnosis of COVID-19 is an underlying process for controlling the spread of this deadly disease and its death rates. The chest radiology (CT) scan is an effective device for the diagnosis and earlier management of COVID-19, meanwhile, the virus mainly targets the respiratory system. Chest X-ray (CXR) images are extremely helpful in the effective diagnosis of COVID-19 due to their rapid outcomes, cost-effectiveness, and availability. Although the radiological image-based diagnosis method seems faster and accomplishes a better recognition rate in the early phase of the epidemic, it requires healthcare experts to interpret the images. Thus, Artificial Intelligence (AI) technologies, such as the deep learning (DL) model, play an integral part in developing automated diagnosis process using CXR images. Therefore, this study designs a sine cosine optimization with DL-based disease detection and classification (SCODL-DDC) for COVID-19 on CXR images. The proposed SCODL-DDC technique examines the CXR images to identify and classify the occurrence of COVID-19. In particular, the SCODL-DDC technique uses the EfficientNet model for feature vector generation, and its hyperparameters can be adjusted by the SCO algorithm. Furthermore, the quantum neural network (QNN) model can be employed for an accurate COVID-19 classification process. Finally, the equilibrium optimizer (EO) is exploited for optimum parameter selection of the QNN model, showing the novelty of the work. The experimental results of the SCODL-DDC method exhibit the superior performance of the SCODL-DDC technique over other approaches.

A deep neural architecture for harmonizing 3-D input data analysis and decision making in medical imaging

Harmonizing the analysis of data, especially of 3-D image volumes, consisting of different number of slices and annotated per volume, is a significant problem in training and using deep neural networks in various applications, including medical imaging. Moreover, unifying the decision making of the networks over different input datasets is crucial for the generation of rich data-driven knowledge and for trusted usage in the applications. This paper presents a new deep neural architecture, named RACNet, which includes routing and feature alignment steps and effectively handles different input lengths and single annotations of the 3-D image inputs, whilst providing highly accurate decisions. In addition, through latent variable extraction from the trained RACNet, a set of anchors are generated providing further insight on the network's decision making. These can be used to enrich and unify data-driven knowledge extracted from different datasets. An extensive experimental study illustrates the above developments, focusing on COVID-19 diagnosis through analysis of 3-D chest CT scans from databases generated in different countries and medical centers.

COVIDAL: A Machine Learning Classifier for Digital COVID-19 Diagnosis in German Hospitals

For the fight against the COVID-19 pandemic, it is particularly important to map the course of infection, in terms of patients who have currently tested SARS-CoV-2 positive, as accurately as possible. In hospitals, this is even more important because resources have become scarce. Although polymerase chain reaction (PCR) and point of care (POC) antigen testing capacities have been massively expanded, they are often very time-consuming and cost-intensive and, in some cases, lack appropriate performance. To meet these challenges, we propose the COVIDAL classifier for AI-based diagnosis of symptomatic COVID-19 subjects in hospitals based on laboratory parameters. We evaluate the algorithm's performance by unique multicenter data with approximately 4,000 patients and an extraordinary high ratio of SARS-CoV-2-positive patients. We analyze the influence of data preparation, flexibility in optimization targets, as well as the selection of the test set on the COVIDAL outcome. The algorithm is compared with standard AI, PCR, POC antigen testing and manual classifications of seven physicians by a decision theoretic scoring model including performance metrics, turnaround times and cost. Thereby, we define health care settings in which a certain classifier for COVID-19 diagnosis is to be applied. We find sensitivities, specificities, and accuracies of the COVIDAL algorithm of up to 90 percent. Our scoring model suggests using PCR testing for a focus on performance metrics. For turnaround times, POC antigen testing should be used. If balancing performance, turnaround times, and cost is of interest, as, for example, in the emergency department, COVIDAL is superior based on the scoring model. © 2023 Association for Computing Machinery.

Region-of-Interest Aware 3D ResNet for Classification of COVID-19 Chest Computerised Tomography Scans

Coronavirus disease 2019, commonly known as COVID-19, is an extremely contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Computerised Tomography (CT) scans based diagnosis and progression analysis of COVID-19 have recently received academic interest. Most algorithms include two-stage analysis where a slice-level analysis is followed by the patient-level analysis. However, such an analysis requires labels for individual slices in the training data. In this paper, we propose a single-stage 3D approach that does not require slice-wise labels. Our proposed method comprises volumetric data pre-processing and 3D ResNet transfer learning. The pre-processing includes pulmonary segmentation to identify the regions of interest, volume resampling and a novel approach for extracting salient slices. This is followed by proposing a region-of-interest aware 3D ResNet for feature learning. The backbone networks utilised in this study include 3D ResNet-18, 3D ResNet-50 and 3D ResNet-101. Our proposed method employing 3D ResNet-101 has outperformed the existing methods by yielding an overall accuracy of 90%. The sensitivity for correctly predicting COVID-19, Community Acquired Pneumonia (CAP) and Normal class labels in the dataset is 88.2%, 96.4% and 96.1%, respectively. © 2013 IEEE.

Coronavirus Diagnosis Based on Chest X-Ray Images and Pre-Trained DenseNet-121

A serious global problem called COVID-19 has killed a great number of people and rendered many projects useless. The obtained individual's identification at the appropriate time is one of the crucial methods to reduce losses. By detecting and recognizing contaminated individuals in the early stages, artificial intelligence can help many associations in these situations. In this study, we offer a fully automated method to identify COVID-19 from a patient's chest X-ray images without the need for a clinical expert's assistance. The proposed approach was evaluated on the public COVID-19 X-ray dataset that achieves high performance and reduces computational complexity. This dataset contains 400 photos, 100 images of individuals who were infected with Covid-19, 100 images of individuals with no COVID-19, 100 images of a viral pneumonia and a 100 more images that we reserve them for testing part. So we have an overall 300 images for training and 100 for testing. The obtained results were so satisfying, an F1 score of 0.98, a Recall of 0.98, and an Accuracy of 0.98. The classification method deep learning-based DenseNet-121, transfer learning, as well as data augmentation techniques were implemented to improve the model more accurately. Our proposed approach outperforms several CNNs and all recent works on COVID‑19 images. Even though there are not enough training photos comparing to other extra-large datasets. © 2023 Lavoisier. All rights reserved.

Heat stroke management during the COVID-19 pandemic: Recommendations from the experts in Japan (2nd edition).

Both coronavirus disease 2019 (COVID-19) and heat stroke have symptoms of fever or hyperthermia and the difficulty in distinguishing them could lead to a strain on emergency medical care. To mitigate the potential confusion that could arise from actions for preventing both COVID-19 spread and heat stroke, particularly in the context of record-breaking summer season temperatures, this work offers new knowledge and evidence that address concerns regarding indoor ventilation and indoor temperatures, mask wearing and heat stroke risk, and the isolation of older adults. Specifically, the current work is the second edition to the previously published guidance for handling heat stroke during the COVID-19 pandemic, prepared by the "Working group on heat stroke medical care during the COVID-19 epidemic," composed of members from four organizations in different medical and related fields. The group was established by the Japanese Association for Acute Medicine Heatstroke and Hypothermia Surveillance Committee. This second edition includes new knowledge, and conventional evidence gleaned from a primary selection of 60 articles from MEDLINE, one article from Cochrane, 13 articles from Ichushi, and a secondary/final selection of 56 articles. This work summarizes the contents that have been clarified in the prevention and treatment of infectious diseases and heat stroke to provide guidance for the prevention, diagnosis, and treatment of heat stroke during the COVID-19 pandemic.

SARS-CoV-2 Detection and COVID-19 Diagnosis: A Bird's Eye View.

The battle against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) associated coronavirus disease 2019 (COVID-19) is continued worldwide by administering firsttime emergency authorized novel mRNA-based and conventional vector-antigen-based antiCOVID-19 vaccines to prevent further transmission of the virus as well as to reduce the severe respiratory complications of the infection in infected individuals. However; the emergence of numerous SARS-CoV-2 variants is of concern, and the identification of certain breakthrough and reinfection cases in vaccinated individuals as well as new cases soaring in some low-to-middle income countries (LMICs) and even in some resource-replete nations have raised concerns that only vaccine jabs would not be sufficient to control and vanquishing the pandemic. Lack of screening for asymptomatic COVID-19-infected subjects and inefficient management of diagnosed COVID-19 infections also pose some concerns and the need to fill the gaps among policies and strategies to reduce the pandemic in hospitals, healthcare services, and the general community. For this purpose, the development and deployment of rapid screening and diagnostic procedures are prerequisites in premises with high infection rates as well as to screen mass unaffected COVID-19 populations. Novel methods of variant identification and genome surveillance studies would be an asset to minimize virus transmission and infection severity. The proposition of this pragmatic review explores current paradigms for the screening of SARS-CoV-2 variants, identification, and diagnosis of COVID-19 infection, and insights into the late-stage development of new methods to better understand virus super spread variants and genome surveillance studies to predict pandemic trajectories.