Detection Of Coronavirus Disease 2019 Research Articles

Fused Audio Instance and Representation for Respiratory Disease Detection

Audio-based classification techniques for body sounds have long been studied to aid in the diagnosis of respiratory diseases. While most research is centered on the use of coughs as the main acoustic biomarker, other body sounds also have the potential to detect respiratory diseases. Recent studies on the coronavirus disease 2019 (COVID-19) have suggested that breath and speech sounds, in addition to cough, correlate with the disease. Our study proposes fused audio instance and representation (FAIR) as a method for respiratory disease detection. FAIR relies on constructing a joint feature vector from various body sounds represented in waveform and spectrogram form. We conduct experiments on the use case of COVID-19 detection by combining waveform and spectrogram representation of body sounds. Our findings show that the use of self-attention to combine extracted features from cough, breath, and speech sounds leads to the best performance with an area under the receiver operating characteristic curve (AUC) score of 0.8658, a sensitivity of 0.8057, and a specificity of 0.7958. Compared to models trained solely on spectrograms or waveforms, the use of both representations results in an improved AUC score, demonstrating that combining spectrogram and waveform representation helps to enrich the extracted features and outperforms the models that use only one representation. While this study focuses on COVID-19, FAIR’s flexibility allows it to combine various multi-modal and multi-instance features in many other diagnostic applications, potentially leading to more accurate diagnoses across a wider range of diseases.

Deep Learning-Based System Combining Chest X-Ray and Computerized Tomography Images for COVID-19 Diagnosis.

Aims/Background: The coronavirus disease 2019 (COVID-19) pandemic has highlighted the need for accurate and efficient diagnostic methods. This study aims to improve COVID-19 detection by integrating chest X-ray (CXR) and computerized tomography (CT) images using deep learning techniques, further improving diagnostic accuracy by using a combined imaging approach. Methods: The study used two publicly accessible databases, COVID-19 Questionnaires for Understanding the Exposure (COVID-QU-Ex) and Integrated Clinical and Translational Cancer Foundation (iCTCF), containing CXR and CT images, respectively. The proposed system employed convolutional neural networks (CNNs) for classification, specifically EfficientNet and ResNet architectures. The data underwent preprocessing steps, including image resizing, Gaussian noise addition, and data augmentation. The dataset was divided into training, validation, and test sets. Gradient-weighted Class Activation Mapping (Grad-CAM) was used for model interpretability. Results: The EfficientNet-based models outperformed the ResNet-based models across all metrics. The highest accuracy achieved was 99.44% for CXR images and 99.81% for CT images with EfficientNetB5. The models also demonstrated high precision, recall, and F1 scores. For statistical significance, the p-values were less than 0.05, indicating that the results are significant. Conclusion: Integrating CXR and CT images using deep learning significantly improves the accuracy of COVID-19 diagnosis. The EfficientNet-based models, with their superior feature extraction capabilities, show better performance than ResNet models. Grad-CAM Visualizations provide insights into the model's decision-making process, potentially reducing diagnostic errors and accelerating diagnosis processes. This approach can improve patient care and support healthcare systems in managing the pandemic more effectively.

Ultrafast and highly sensitive detection of SARS-CoV-2 spike protein by field-effect transistor graphene-based biosensors

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), etiological agent for the coronavirus disease 2019 (COVID-19), has resulted in over 775 million global infections. Early diagnosis remains pivotal for effective epidemiological surveillance despite the availability of vaccines. Antigen-based assays are advantageous for early COVID-19 detection due to their simplicity, cost-effectiveness, and suitability for point-of-care testing (PoCT). This study introduces a graphene field-effect transistor-based biosensor designed for high sensitivity and rapid response to the SARS-CoV-2 spike protein. By functionalizing graphene with monoclonal antibodies and applying short-duration gate voltage pulses, we achieve selective detection of the viral spike protein in human serum within 100 µs and at concentrations as low as 1 fg ml−1, equivalent to 8 antigen molecules per µl of blood. Furthermore, the biosensor estimates spike protein concentrations in serum from COVID-19 patients. Our platform demonstrates potential for next-generation PoCT antigen assays, promising fast and sensitive diagnostics for COVID-19 and other infectious diseases.

A light-weight and generalizable deep learning model for the prediction of COVID-19 from chest X-ray images

The detection of coronavirus disease (COVID-19) using standard laboratory tests, such as reverse transcription polymerase chain reaction (RT-PCR), is time-consuming. Complex medical imaging problems are currently being solved using machine learning and deep learning techniques. Our proposed solution utilizes chest radiography imaging techniques, which have shown to be a faster alternative for detecting COVID-19. We present an efficient and lightweight deep learning architecture for identifying COVID-19 using chest X-ray images which achieve 99.81% accuracy in intra-database testing and 100% accuracy in cross-validation testing on a separate data set. The results demonstrate the potential of our proposed model as a reliable tool for COVID-19 diagnosis using chest X-ray images, which can have a significant impact on improving the efficiency of COVID-19 diagnosis and treatment.

Explaining transfer learning models for the detection of COVID-19 on X-ray lung images

Amidst the coronavirus disease 2019 (COVID-19) pandemic, researchers are exploring innovative approaches to enhance diagnostic accuracy. One avenue is utilizing deep learning models to analyze lung X-ray images for COVID-19 diagnosis, complementing existing tests like reverse transcription polymerase chain reaction (RT-PCR). However, trusting these models, often viewed as black boxes, presents a challenge. To address this, six explainable artificial intelligence (XAI) techniques: local interpretable model agnostic explanations (LIME), Shapley additive explanations (SHAP), integrated gradients, smooth-grad, gradient-weighted class activation mapping (Grad-CAM), and Layer-CAM are applied to interpret four transfer learning models. These models: VGG16, ResNet50, InceptionV3, and DenseNet121 are analyzed to understand their workings and the rationale behind their predictions. Validating the results with medical experts poses difficulties due to time and resource constraints, alongside the scarcity of annotated X-ray datasets. To address this, a voting mechanism employing different XAI methods across various models is proposed. This approach highlights regions of lung infection, potentially reducing individual model biases stemming from their structures. If successful, this research could pave the way for an automated system for annotating infection regions, bolstering confidence in predictions and aiding in the development of more effective diagnostic tools for COVID-19.

Interpretability analysis of deep models for COVID-19 detection

During the coronavirus disease 2019 (COVID-19) pandemic, various research disciplines collaborated to address the impacts of severe acute respiratory syndrome coronavirus-2 infections. This paper presents an interpretability analysis of a convolutional neural network-based model designed for COVID-19 detection using audio data. We explore the input features that play a crucial role in the model’s decision-making process, including spectrograms, fundamental frequency (F0), F0 standard deviation, sex, and age. Subsequently, we examine the model’s decision patterns by generating heat maps to visualize its focus during the decision-making process. Emphasizing an explainable artificial intelligence approach, our findings demonstrate that the examined models can make unbiased decisions even in the presence of noise in training set audios, provided appropriate preprocessing steps are undertaken. Our top-performing model achieves a detection accuracy of 94.44%. Our analysis indicates that the analyzed models prioritize high-energy areas in spectrograms during the decision process, particularly focusing on high-energy regions associated with prosodic domains, while also effectively utilizing F0 for COVID-19 detection.

Integrated CNN and Federated Learning for COVID-19 Detection on Chest X-Ray Images.

Currently, Coronavirus Disease 2019 (COVID-19) is still endangering world health and safety and deep learning (DL) is expected to be the most powerful method for efficient detection of COVID-19. However, patients' privacy concerns prohibit data sharing between medical institutions, leading to unexpected performance of deep neural network (DNN) models. Fortunately, federated learning (FL), as a novel paradigm, allows participating clients to collaboratively train models without exposing source data outside original location. Nevertheless, the current FL-based COVID-19 detection methods prefer optimizing secondary objectives including delay, energy consumption and privacy, while few works focus on improving the model accuracy and stability. In this paper, we propose a federated learning framework with dynamic focus for COVID-19 detection on CXR images, named FedFocus. Specifically, to improve the training efficiency and accuracy, the training loss of each model is taken as the basis for parameter aggregation weights. As training layer deepens, a constantly updated dynamic factor is designed to stabilize the aggregation process. In addition, to highly restore the real dataset, the training sets in our experiments are divided based on the population and the infection of three real cities. Extensive experiments conducted on the real-world CXR images dataset demonstrate that FedFocus outperforms the baselines in model training efficiency, accuracy and stability.

Optimization of the VGG Deep Learning Model Performance for Covid-19 Detection Using CT-Scan Images

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2) causes a pneumonia-like disease known as Coronavirus Disease 2019 (COVID-19). The Reverse Transcription Polymerase Chain Reaction (RT-PCR) test is the current standard for detecting COVID-19. However, CT scans can be applied for radiological inspection to detect infections in their earliest lung stages. Machine learning, specifically deep learning, can potentially speed up the evaluation of CT scan diagnoses of COVID-19. To date, no studies have been discovered that employ SGD, Adamax, or AdaGrad optimization methods with deep learning VGG model variants for COVID-19 detection in CT scan images with datasets comprising 2,038 images. This study aims to assess and compare the performance of various optimization methods for detecting COVID-19 utilizing variations of the VGG-16 and VGG-19 models based on CT scan images. Results from performance optimization comparison tests employing two VGG deep learning models were obtained, demonstrating the influence of optimization methods on model performance. The Adamax optimization method applied to the VGG-16 model performance achieved an average accuracy of 94.11% in COVID-19 detection using CT scan images, while the Adamax optimization method applied to the VGG-19 model performance achieved an average accuracy of 93.77%.

CNNFDNF: CONVOLUTIONAL NEURAL NETWORK AND CASCADE NEURO FUZZY NETWORK FOR SEVERITY LEVEL OF COVID-19 DETECTION USING CHEST X-RAY IMAGES

Chest X-ray (CXR) images are the most important imaging modality to monitor and detect several lung diseases and Coronavirus Disease 2019 (COVID-19) disease. This is the first imaging method in the detection of COVID-19. Since the limited availability of interpreted medical images, and the accuracy are the biggest challenges in the conventional methods. Hence, a novel framework is designed for the severity level of COVID-19 detection using CXR images named Convolutional Neural Network and Cascade Neuro-Fuzzy Network (CNNFDNF). Initially, the input CXR image is allowed to the preprocessing phase that is done utilizing the Kalman filter and Region of Interest (ROI) extraction. Then, the lung lobe segmentation is executed by Psi-Net. After that, the extraction of Convolutional Neural Network (CNN) features is conducted from the interested lobe region. Moreover, the extracted image features are subjected to COVID-19 detection, where it is achieved by Deep Generative Adversarial Network (Deep GAN) trained by Henry Gas Water Wave Optimization (HGWWO). Here, HGWWO is blended by Water Wave Optimization (WWO) and Henry Gas Solubility Optimization (HGSO). The detection phase is categorized into COVID and non-COVID. If the detected image is COVID, then the CXR images are given to the second level classification. Finally, it is accomplished by employing the proposed CNNFDNF to classify them into low, moderate, or high COVID-19 infection affected at lungs. The proposed CNNFDNF is the newly devised scheme by modifying the layers of CNN and cascaded neuro-fuzzy network (DNF) structure. The measures employed for this scheme namely, accuracy, sensitivity and specificity acquired are 93.0%, 94.1%, and 93.8%. The proposed method is essential for keeping the situation under control. Because, in the severe cases may have severe pneumonia, other organ failure, and possible death.

Development of a novel Colorimetric Assay for the rapid diagnosis of Coronavirus disease 2019 from nasopharyngeal samples.

Emergence of Coronavirus disease 2019 (COVID-19) pandemic has posed a huge threat to public health. Rapid and reliable test to diagnose infected subjects is crucial for disease spread control. We developed a colorimetric test for COVID-19 detection using a Colorimetric Assay based on thiol-linked RNA modified gold nanoparticles (AuNPs) and oligonucleotide probes. This method was conducted on RNA from 200 pharyngeal swab samples initially tested by Real-Time polymerase chain reaction (RT-PCR) as gold standard. A specific oligonucleotide probe designed based on ORF1ab of COVID-19 was functionalized with AuNPs-probe conjugate. The exposure of AuNP-probe to isolated RNA samples was tested using hybridization. In this comparative study, the colorimetric functionalized AuNPs assay exhibited a detection limit of 25copies/µL. It was higher in comparison to the RT-PCR method, which could only detect 15copies/µL. The results demonstrated 100% specificity and 96% sensitivity for the developed method. Herein, we developed an incredibly rapid, simple and cost-effective Colorimetric Assay lasting approximately 30min which could process considerably higher number of COVID-19 samples compared to the RT-PCR. This AuNP-probe conjugate colorimetric method could be considered the optimum alternatives for conventional diagnostic tools especially in over-populated and/or low-income countries.

Automated detection of COVID-19 and pneumonia diseases using data mining and transfer learning algorithms with focal loss from chest X-ray images

Pneumonia is a lung infection that produces severe inflammation, which can be caused by viruses, bacteria, or fungi. The global occurrence of pneumonia cases has been notably affected by the outbreak of Coronavirus Disease 2019 (COVID-19). Recent studies have demonstrated the capability of Deep Learning (DL) algorithms in detecting COVID-19 and pneumonia in medical images like X-rays. In this study, we employ four different Chest X-ray (CXR) datasets to evaluate four pre-trained Convolutional Neural Network (CNN) models: InceptionResNet V2, Inception V3, Xception, and MobileNet. These models were fine-tuned using either the Cross-Entropy (CE) loss function only for balanced datasets or separately using both the CE and Focal Loss (FL) functions for imbalanced datasets. For the first dataset, the InceptionResNet V2 model achieved the highest multi-classification accuracy of 88.63%. The superior model for the second and fourth datasets is Inception V3, with 94.35% and 97.67% multi-classification accuracy, respectively. For the third dataset, the best model is Xception, with a binary classification accuracy of 100.00%. Our results emphasize the significance of using the FL function in solving class imbalance problems in DL models. Additionally, it highlights the effectiveness of individual DL models in detecting different pneumonia infections using different CXR datasets.

Emerging Trends of Gold Nanostructures for Point-of-Care Biosensor-Based Detection of COVID-19.

In 2019, a worldwide pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged. SARS-CoV-2 is the deadly microorganism responsible for coronavirus disease 2019 (COVID-19), which has caused millions of deaths and irreversible health problems worldwide. To restrict the spread of SARS-CoV-2, accurate detection of COVID-19 is essential for the identification and control of infected cases. Although recent detection technologies such as the real-time polymerase chain reaction delivers an accurate diagnosis of SARS-CoV-2, they require a long processing duration, expensive equipment, and highly skilled personnel. Therefore, a rapid diagnosis with accurate results is indispensable to offer effective disease suppression. Nanotechnology is the backbone of current science and technology developments including nanoparticles (NPs) that can biomimic the corona and develop deep interaction with its proteins because of their identical structures on the nanoscale. Various NPs have been extensively applied in numerous medical applications, including implants, biosensors, drug delivery, and bioimaging. Among them, point-of-care biosensors mediated with gold nanoparticles (GNPSs) have received great attention due to their accurate sensing characteristics, which are widely used in the detection of amino acids, enzymes, DNA, and RNA in samples. GNPS have reconstructed the biomedical application of biosensors because of its outstanding physicochemical characteristics. This review provides an overview of emerging trends in GNP-mediated point-of-care biosensor strategies for diagnosing various mutated forms of human coronaviruses that incorporate different transducers and biomarkers. The review also specifically highlights trends in gold nanobiosensors for coronavirus detection, ranging from the initial COVID-19 outbreak to its subsequent evolution into a pandemic.

Enhancing COVID-19 Detection: An Xception-Based Model with Advanced Transfer Learning from X-ray Thorax Images.

Rapid and precise identification of Coronavirus Disease 2019 (COVID-19) is pivotal for effective patient care, comprehending the pandemic's trajectory, and enhancing long-term patient survival rates. Despite numerous recent endeavors in medical imaging, many convolutional neural network-based models grapple with the expressiveness problem and overfitting, and the training process of these models is always resource-intensive. This paper presents an innovative approach employing Xception, augmented with cutting-edge transfer learning techniques to forecast COVID-19 from X-ray thorax images. Our experimental findings demonstrate that the proposed model surpasses the predictive accuracy of established models in the domain, including Xception, VGG-16, and ResNet. This research marks a significant stride toward enhancing COVID-19 detection through a sophisticated and high-performing imaging model.

Clinical Performance of the Reverse Transcription-Loop-Mediated Isothermal Amplification Assay for the Diagnosis of COVID-19 in a Thai Community Hospital at the Thailand-Myanmar Border.

Coronavirus disease 2019 (COVID-19) continues to be a global health threat and is a public health issue in Thailand and other countries. The extensive cross-border between Thailand and Myanmar is considered to be at a potentially high risk for COVID-19 distribution in this region. In this instance, simple and cost-effective tests for rapid and early detection of COVID-19 would be useful for effective patient management and control of the disease. This study was conducted at Mae Sot Hospital on the border of Thailand-Myanmar to evaluate the diagnostic performance of a simple colorimetric reverse transcription-loop-mediated isothermal amplification (RT-LAMP) assay developed recently for the rapid detection of SARS-CoV-2. Nasopharyngeal specimens were routinely collected and processed through automated nucleic acid extraction followed by real-time reverse transcription-polymerase chain reaction (rRT-PCR) using the Molaccu® COVID-19 Detection Kit. The RT-LAMP assay was further performed on remnant RNA samples, and the visual results were compared to those of rRT-PCR as a reference. Of the 727 samples tested, the RT-LAMP assay could detect 322 out of 374 samples positive for SARS-CoV-2 by rRT-PCR with 100% (n = 353/353) negative agreement. The comparative analysis demonstrated the overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive valueof the RT-LAMP at 92.85% (n = 675/727, 95% CI: 90.73-94.61), 86.10% (n = 322/374, 95% CI: 82.17-89.44), 100% (n = 353/353, 95% CI: 98.96-100), 100% (n = 322/322, 95% CI: 98.86-100), and 87.16% (n = 353/405, 95% CI: 84.06-89.73), respectively. This RT-LAMP assay showed good diagnostic performance in the hospital setting. It can increase laboratory capacity for rapid SARS-CoV-2 testing and has the potential for use as an alternative or a backup assay at the point of need, especially where alternatives are unavailable for any reason, such as a decline in COVID-19 cases.

Comparative study between gold standard real-time RT-PCR assay and rapid antigen test for detection of COVID-19 in Sylhet CMH

Background: The notorious pandemic coronavirus disease 2019 (COVID-19) has been spread all over the world. Its third pandemic wave has been completed and now a fourth wave is running over many countries. Almost 60 million people have been infected and more than 5.7 million people have died. Till now almost 1.2 million people have been infected and about 30000 people have died in Bangladesh from Covid-19. It has not only taken away human lives but also has put a tremendous impact on our society and economy. So early detection of this virus and isolating the patients have a significant role to control the disease. Henceforth there is an urgent need for simple, accurate and rapid identifications of C0VID -19. Rapid antigen tests can provide timely results, which is of particular importance in a primary setting. So, Performance of the Rapid antigen detection test (RAT) should be evaluated and compared with gold standard real-time reverse transcription-polymerase chain reaction (RT-PCR) test for diagnosis of COVID-19. Methods: Specimens were collected from both naso and oropharyngeal region from 1000 patients who reported to the flu centre in CMH Sylhet with the complaints of fever, cough & headache for detection of COVID virus-2 RNA by rapid antigen (RAT) and RT-PCR test. We used real time RT-PCR kit (Sansure, Biotech china, gene RdRP & N) and COVID-19 Ag Kit (Wondfo, Republic of China) for detection of COVID-19 RNA. Results: Out of 1000 samples 158 were positive and 842 were negative by real-time RT-PCR assay where 154 samples were positive, and 846 cases were negative by rapid antigen test for severe acute respiratory syndrome (SARS) CoV-2. The duration from the onset of symptoms to laboratory test in all suspected cases ranged from 0 to 02 days (with a median of 01 days). The rapid SARS-CoV-2 antigen detection tests sensitivity and specificity were 97.29% (95% CI, 90.06–99.89%) and 97.94% (95% CI, 97.26–98.57%), respectively. Seven samples were found negative in RAT but were found positive in RT-PCR, other three samples were found positive in RAT while they were found negative in RT-PCR. Conclusions: It is observed that most rapid antigen tests for COVID-19 are significantly comparable with RT-PCR tests and had enough sensitivity and specificity for 158 individuals, infected with severe acute respiratory syndrome-CoV-2.

A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review

BackgroundLung diseases, both infectious and non-infectious, are the most prevalent cause of mortality overall in the world. Medical research has identified pneumonia, lung cancer, and Corona Virus Disease 2019 (COVID-19) as prominent lung diseases prioritized over others. Imaging modalities, including X-rays, computer tomography (CT) scans, magnetic resonance imaging (MRIs), positron emission tomography (PET) scans, and others, are primarily employed in medical assessments because they provide computed data that can be utilized as input datasets for computer-assisted diagnostic systems. Imaging datasets are used to develop and evaluate machine learning (ML) methods to analyze and predict prominent lung diseases.ObjectiveThis review analyzes ML paradigms, imaging modalities' utilization, and recent developments for prominent lung diseases. Furthermore, the research also explores various datasets available publically that are being used for prominent lung diseases.MethodsThe well-known databases of academic studies that have been subjected to peer review, namely ScienceDirect, arXiv, IEEE Xplore, MDPI, and many more, were used for the search of relevant articles. Applied keywords and combinations used to search procedures with primary considerations for review, such as pneumonia, lung cancer, COVID-19, various imaging modalities, ML, convolutional neural networks (CNNs), transfer learning, and ensemble learning.ResultsThis research finding indicates that X-ray datasets are preferred for detecting pneumonia, while CT scan datasets are predominantly favored for detecting lung cancer. Furthermore, in COVID-19 detection, X-ray datasets are prioritized over CT scan datasets. The analysis reveals that X-rays and CT scans have surpassed all other imaging techniques. It has been observed that using CNNs yields a high degree of accuracy and practicability in identifying prominent lung diseases. Transfer learning and ensemble learning are complementary techniques to CNNs to facilitate analysis. Furthermore, accuracy is the most favored metric for assessment.

A brief review and scientometric analysis on ensemble learning methods for handling COVID-19

Numerous efforts and research have been conducted worldwide to combat the coronavirus disease 2019 (COVID-19) pandemic. In this regard, some researchers have focused on deep and machine-learning approaches to discover more about this disease. There have been many articles on using ensemble learning methods for COVID-19 detection. Still, there seems to be no scientometric analysis or a brief review of these researches. Hence, a combined method of scientometric analysis and brief review was used to study the published articles that employed an ensemble learning approach to detect COVID-19. This research used both methods to overcome their limitations, leading to enhanced and reliable outcomes. The related articles were retrieved from the Scopus database. Then a two-step procedure was employed. A concise review of the collected articles was conducted. Then they underwent scientometric and bibliometric analyses. The findings revealed that convolutional neural network (CNN) is the mostly employed algorithm, while support vector machine (SVM), random forest, Resnet, DenseNet, and visual geometry group (VGG) were also frequently used. Additionally, China has had a significant presence in the numerous top-ranking categories of this field of research. Both study phases yielded valuable results and rankings.

Clinical Presentation and Management of Post Coronavirus Disease 2019 Osteonecrosis of the Femoral Head: A Narrative Review of Literature

Abstract The purpose of this review was to identify and describe what is known regarding the clinical presentation and the various methods of management of osteonecrosis of the femoral head (ONFH) following coronavirus disease 2019 (COVID-19), based on current literature. COVID-19 results from infection with the Severe Acute Respiratory Syndrome Corona Virus2. A large number of infected patients manifest multi-systemic pathologies for several weeks to months following recovery. This condition is now identified as postacute COVID-19 syndrome (PACS). COVID-19 could result in ONFH either directly due to endothelial dysfunction and a hypercoagulable state, or secondary to corticosteroid medications used during its management, as a manifestation of PACS. Despite a fairly widespread awareness of this condition, there is a scarcity of literature regarding its clinical presentation and management. A literature search was conducted using the following electronic databases: PubMed/Medline, Embase, and Scopus. The following search terms were used: “covid (AND) osteonecrosis” and “covid (AND) avascular (AND) necrosis.” The preliminary search results were screened to identify relevant literature. Data pertaining to the clinical presentation and management of post-COVID-19 ONFH were retrieved from these studies, and tabulated and presented systematically. Post-COVID-19 ONFH has been reported in a wide range of age groups, with unilateral or bilateral hip involvement. A greater proportion of reported cases were associated with: the male gender, high body mass index, severe COVID-19 infection, and corticosteroid consumption. The onset of symptoms, commonly pain and difficulty in walking, has been reported to vary from a few days to a few years following the detection of COVID-19. Most studies reported patient presentation with stage-2 or stage-3 ONFH. Conservative management of this condition with bisphosphonates has shown promising preliminary results. Among surgical management options, core decompression and total hip arthroplasty have been described. Long-term outcomes of patients managed with various treatment modalities are currently not available. Future observational studies could confirm the findings in this review, and lay the foundation for high-quality systematic reviews. The information presented herein can aid in the drafting of appropriate screening and surveillance protocols for COVID-19 patients.

COVID-19 infection segmentation using hybrid deep learning and image processing techniques

The coronavirus disease 2019 (COVID-19) epidemic has become a worldwide problem that continues to affect people’s lives daily, and the early diagnosis of COVID-19 has a critical importance on the treatment of infected patients for medical and healthcare organizations. To detect COVID-19 infections, medical imaging techniques, including computed tomography (CT) scan images and X-ray images, are considered some of the helpful medical tests that healthcare providers carry out. However, in addition to the difficulty of segmenting contaminated areas from CT scan images, these approaches also offer limited accuracy for identifying the virus. Accordingly, this paper addresses the effectiveness of using deep learning (DL) and image processing techniques, which serve to expand the dataset without the need for any augmentation strategies, and it also presents a novel approach for detecting COVID-19 virus infections in lung images, particularly the infection prediction issue. In our proposed method, to reveal the infection, the input images are first preprocessed using a threshold then resized to 128 × 128. After that, a density heat map tool is used for coloring the resized lung images. The three channels (red, green, and blue) are then separated from the colored image and are further preprocessed through image inverse and histogram equalization, and are subsequently fed, in independent directions, into three separate U-Nets with the same architecture for segmentation. Finally, the segmentation results are combined and run through a convolution layer one by one to get the detection. Several evaluation metrics using the CT scan dataset were used to measure the performance of the proposed approach in comparison with other state-of-the-art techniques in terms of accuracy, sensitivity, precision, and the dice coefficient. The experimental results of the proposed approach reached 99.71%, 0.83, 0.87, and 0.85, respectively. These results show that coloring the CT scan images dataset and then dividing each image into its RGB image channels can enhance the COVID-19 detection, and it also increases the U-Net power in the segmentation when merging the channel segmentation results. In comparison to other existing segmentation techniques employing bigger 512 × 512 images, this study is one of the few that can rapidly and correctly detect the COVID-19 virus with high accuracy on smaller 128 × 128 images using the metrics of accuracy, sensitivity, precision, and dice coefficient.

COVID-19 Detection via Ultra-Low-Dose X-ray Images Enabled by Deep Learning.

The detection of Coronavirus disease 2019 (COVID-19) is crucial for controlling the spread of the virus. Current research utilizes X-ray imaging and artificial intelligence for COVID-19 diagnosis. However, conventional X-ray scans expose patients to excessive radiation, rendering repeated examinations impractical. Ultra-low-dose X-ray imaging technology enables rapid and accurate COVID-19 detection with minimal additional radiation exposure. In this retrospective cohort study, ULTRA-X-COVID, a deep neural network specifically designed for automatic detection of COVID-19 infections using ultra-low-dose X-ray images, is presented. The study included a multinational and multicenter dataset consisting of 30,882 X-ray images obtained from approximately 16,600 patients across 51 countries. It is important to note that there was no overlap between the training and test sets. The data analysis was conducted from 1 April 2020 to 1 January 2022. To evaluate the effectiveness of the model, various metrics such as the area under the receiver operating characteristic curve, receiver operating characteristic, accuracy, specificity, and F1 score were utilized. In the test set, the model demonstrated an AUC of 0.968 (95% CI, 0.956-0.983), accuracy of 94.3%, specificity of 88.9%, and F1 score of 99.0%. Notably, the ULTRA-X-COVID model demonstrated a performance comparable to conventional X-ray doses, with a prediction time of only 0.1 s per image. These findings suggest that the ULTRA-X-COVID model can effectively identify COVID-19 cases using ultra-low-dose X-ray scans, providing a novel alternative for COVID-19 detection. Moreover, the model exhibits potential adaptability for diagnoses of various other diseases.