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1.
Stud Health Technol Inform ; 290: 304-308, 2022 Jun 06.
Article in English | MEDLINE | ID: covidwho-1879404

ABSTRACT

We present an automated knowledge synthesis and discovery framework to analyze published literature to identify and represent underlying mechanistic associations that aggravate chronic conditions due to COVID-19. Our literature-based discovery approach integrates text mining, knowledge graphs and medical ontologies to discover hidden and previously unknown pathophysiologic relations, dispersed across multiple public literature databases, between COVID-19 and chronic disease mechanisms. We applied our approach to discover mechanistic associations between COVID-19 and chronic conditions-i.e. diabetes mellitus and chronic kidney disease-to understand the long-term impact of COVID-19 on patients with chronic diseases. We found several gene-disease associations that could help identify mechanisms driving poor outcomes for COVID-19 patients with underlying conditions.


Subject(s)
COVID-19 , Diabetes Mellitus , Renal Insufficiency, Chronic , Chronic Disease , Diabetes Mellitus/epidemiology , Humans , Pattern Recognition, Automated , Renal Insufficiency, Chronic/epidemiology
2.
BMC Med Inform Decis Mak ; 22(Suppl 2): 147, 2022 06 02.
Article in English | MEDLINE | ID: covidwho-1875008

ABSTRACT

BACKGROUND: Knowledges graphs (KGs) serve as a convenient framework for structuring knowledge. A number of computational methods have been developed to generate KGs from biomedical literature and use them for downstream tasks such as link prediction and question answering. However, there is a lack of computational tools or web frameworks to support the exploration and visualization of the KG themselves, which would facilitate interactive knowledge discovery and formulation of novel biological hypotheses. METHOD: We developed a web framework for Knowledge Graph Exploration and Visualization (KGEV), to construct and visualize KGs in five stages: triple extraction, triple filtration, metadata preparation, knowledge integration, and graph database preparation. The application has convenient user interface tools, such as node and edge search and filtering, data source filtering, neighborhood retrieval, and shortest path calculation, that work by querying a backend graph database. Unlike other KGs, our framework allows fast retrieval of relevant texts supporting the relationships in the KG, thus allowing human reviewers to judge the reliability of the knowledge extracted. RESULTS: We demonstrated a case study of using the KGEV framework to perform research on COVID-19. The COVID-19 pandemic resulted in an explosion of relevant literature, making it challenging to make full use of the vast and heterogenous sources of information. We generated a COVID-19 KG with heterogenous information, including literature information from the CORD-19 dataset, as well as other existing knowledge from eight data sources. We showed the utility of KGEV in three intuitive case studies to explore and query knowledge on COVID-19. A demo of this web application can be accessed at http://covid19nlp.wglab.org . Finally, we also demonstrated a turn-key adaption of the KGEV framework to study clinical phenotypic presentation of human diseases by Human Phenotype Ontology (HPO), illustrating the versatility of the framework. CONCLUSION: In an era of literature explosion, the KGEV framework can be applied to many emerging diseases to support structured navigation of the vast amount of newly published biomedical literature and other existing biological knowledge in various databases. It can be also used as a general-purpose tool to explore and query gene-phenotype-disease-drug relationships interactively.


Subject(s)
COVID-19 , Humans , Pandemics , Pattern Recognition, Automated , Phenotype , Reproducibility of Results
3.
Biomed Res Int ; 2022: 3755460, 2022.
Article in English | MEDLINE | ID: covidwho-1874896

ABSTRACT

This study analyzed the research hotspots and frontiers of exercise rehabilitation among cancer patients via CiteSpace. Relevant literature published in the core collection of the Web of Science (WoS) database from January 1, 2000, to February 6, 2022, was searched. Further, we used CiteSpace5.8R1 to generate a network map and identified top authors, institutions, countries, keywords, and research trends. A total of 2706 related literature were retrieved. The most prolific writer was found to be Kathryn H Schmitz (21 articles). The University of Toronto (64 articles) was found to be the leading institution, with the United States being the leading country. Further, "rehabilitation," "exercise," "quality of life," "cancer," and "physical activity" were the top 5 keywords based on frequency; next, "disability," "survival," "fatigue," "cancer," and "rehabilitation" were the top 5 keywords based on centrality. The keyword "fatigue" was ranked at the top of the most cited list. Finally, "rehabilitation medicine," "activities of daily living," "lung neoplasm," "implementation," "hospice," "exercise oncology," "mental health," "telemedicine," and "multidisciplinary" are potential topics for future research. Our results show that the research hotspots have changed from "quality of life," "survival," "rehabilitation," "exercise," "cancer," "physical therapy," "fatigue," and "breast cancer" to "exercise oncology," "COVID-19," "rehabilitation medicine," "inpatient rehabilitation," "implementation," "telemedicine," "lung neoplasm," "telehealth," "multidisciplinary," "psycho-oncology," "hospice," "adapted physical activity," "cancer-related symptom," "cognitive function," and "behavior maintenance." Future research should explore the recommended dosage and intensity of exercise in cancer patients. Further, following promotion of the concept of multidisciplinary cooperation and the rapid development of Internet medical care, a large amount of patient data has been accumulated; thus, how to effectively use this data to generate results of high clinical value is a question for future researchers.


Subject(s)
COVID-19 , Lung Neoplasms , Activities of Daily Living , Bibliometrics , COVID-19/epidemiology , Fatigue , Humans , Pattern Recognition, Automated , Quality of Life , United States
4.
Comput Math Methods Med ; 2022: 5137513, 2022.
Article in English | MEDLINE | ID: covidwho-1691217

ABSTRACT

Internet of Things (IoT) with deep learning (DL) is drastically growing and plays a significant role in many applications, including medical and healthcare systems. It can help users in this field get an advantage in terms of enhanced touchless authentication, especially in spreading infectious diseases like coronavirus disease 2019 (COVID-19). Even though there is a number of available security systems, they suffer from one or more of issues, such as identity fraud, loss of keys and passwords, or spreading diseases through touch authentication tools. To overcome these issues, IoT-based intelligent control medical authentication systems using DL models are proposed to enhance the security factor of medical and healthcare places effectively. This work applies IoT with DL models to recognize human faces for authentication in smart control medical systems. We use Raspberry Pi (RPi) because it has low cost and acts as the main controller in this system. The installation of a smart control system using general-purpose input/output (GPIO) pins of RPi also enhanced the antitheft for smart locks, and the RPi is connected to smart doors. For user authentication, a camera module is used to capture the face image and compare them with database images for getting access. The proposed approach performs face detection using the Haar cascade techniques, while for face recognition, the system comprises the following steps. The first step is the facial feature extraction step, which is done using the pretrained CNN models (ResNet-50 and VGG-16) along with linear binary pattern histogram (LBPH) algorithm. The second step is the classification step which can be done using a support vector machine (SVM) classifier. Only classified face as genuine leads to unlock the door; otherwise, the door is locked, and the system sends a notification email to the home/medical place with detected face images and stores the detected person name and time information on the SQL database. The comparative study of this work shows that the approach achieved 99.56% accuracy compared with some different related methods.


Subject(s)
Automated Facial Recognition , Deep Learning , Internet of Things , Algorithms , COVID-19 , Computer Security , Computer Simulation , Databases, Factual , Equipment Design , Humans , Pattern Recognition, Automated , SARS-CoV-2 , Support Vector Machine
5.
Environ Sci Pollut Res Int ; 29(18): 26396-26408, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1549518

ABSTRACT

With the global outbreak of coronavirus disease (COVID-19) all over the world, artificial intelligence (AI) technology is widely used in COVID-19 and has become a hot topic. In recent 2 years, the application of AI technology in COVID-19 has developed rapidly, and more than 100 relevant papers are published every month. In this paper, we combined with the bibliometric and visual knowledge map analysis, used the WOS database as the sample data source, and applied VOSviewer and CiteSpace analysis tools to carry out multi-dimensional statistical analysis and visual analysis about 1903 pieces of literature of recent 2 years (by the end of July this year). The data is analyzed by several terms with the main annual article and citation count, major publication sources, institutions and countries, their contribution and collaboration, etc. Since last year, the research on the COVID-19 has sharply increased; especially the corresponding research fields combined with the AI technology are expanding, such as medicine, management, economics, and informatics. The China and USA are the most prolific countries in AI applied in COVID-19, which have made a significant contribution to AI applied in COVID-19, as the high-level international collaboration of countries and institutions is increasing and more impactful. Moreover, we widely studied the issues: detection, surveillance, risk prediction, therapeutic research, virus modeling, and analysis of COVID-19. Finally, we put forward perspective challenges and limits to the application of AI in the COVID-19 for researchers and practitioners to facilitate future research on AI applied in COVID-19.


Subject(s)
Artificial Intelligence , COVID-19 , Humans , Pattern Recognition, Automated , SARS-CoV-2 , Technology
6.
Sci Rep ; 11(1): 18626, 2021 09 20.
Article in English | MEDLINE | ID: covidwho-1428899

ABSTRACT

Population confinements have been one of the most widely adopted non-pharmaceutical interventions (NPIs) implemented by governments across the globe to help contain the spread of the SARS-CoV-2 virus. While confinement measures have been proven to be effective to reduce the number of infections, they entail significant economic and social costs. Thus, different policy makers and social groups have exhibited varying levels of acceptance of this type of measures. In this context, understanding the factors that determine the willingness of individuals to be confined during a pandemic is of paramount importance, particularly, to policy and decision-makers. In this paper, we study the factors that influence the unwillingness to be confined during the COVID-19 pandemic by the means of a large-scale, online population survey deployed in Spain. We perform two types of analyses (logistic regression and automatic pattern discovery) and consider socio-demographic, economic and psychological factors, together with the 14-day cumulative incidence per 100,000 inhabitants. Our analysis of 109,515 answers to the survey covers data spanning over a 5-month time period to shed light on the impact of the passage of time. We find evidence of pandemic fatigue as the percentage of those who report an unwillingness to be in confinement increases over time; we identify significant gender differences, with women being generally less likely than men to be able to sustain long-term confinement of at least 6 months; we uncover that the psychological impact was the most important factor to determine the willingness to be in confinement at the beginning of the pandemic, to be replaced by the economic impact as the most important variable towards the end of our period of study. Our results highlight the need to design gender and age specific public policies, to implement psychological and economic support programs and to address the evident pandemic fatigue as the success of potential future confinements will depend on the population's willingness to comply with them.


Subject(s)
COVID-19/epidemiology , Pandemics , Behavior , COVID-19/economics , COVID-19/psychology , Female , Humans , Logistic Models , Male , Odds Ratio , Pattern Recognition, Automated , Spain/epidemiology , Statistics as Topic , Surveys and Questionnaires , Workplace
7.
Anal Bioanal Chem ; 413(7): 1787-1798, 2021 Mar.
Article in English | MEDLINE | ID: covidwho-1336052

ABSTRACT

Rapid and accurate identification of respiratory tract infection pathogens is of utmost importance for clinical diagnosis and treatment, as well as prevention of pathogen transmission. To meet this demand, a microfluidic chip-based PCR-array system, Onestart, was developed. The Onestart system uses a microfluidic chip packaged with all the reagents required, and the waste liquid is also collected and stored on the chip. This ready-to-use system can complete the detection of 21 pathogens in a fully integrated manner, with sample lysis, nucleic acid extraction/purification, and real-time PCR sequentially implemented on the same chip. The entire analysis process is completed within 1.5 h, and the system automatically generates a test report. The lower limit-of-detection (LOD) of the Onestart assay was determined to be 1.0 × 103 copies·mL-1. The inter-batch variation of cycle threshold (Ct) values ranged from 0.08% to 0.69%, and the intra-batch variation ranged from 0.9% to 2.66%. Analytical results of the reference sample mix showed a 100% specificity of the Onestart assay. The analysis of batched clinical samples showed consistency of the Onestart assay with real-time PCR. With its ability to provide rapid, sensitive, and specific detection of respiratory tract infection pathogens, application of the Onestart system will facilitate timely clinical management of respiratory tract infections and effective prevention of pathogen transmission. Onestart, a ready-to-use system, can detect 21 pathogens in a fully integrated manner on a microchip within 1.5 h.


Subject(s)
Automation , Polymerase Chain Reaction/methods , Respiratory Tract Infections/diagnosis , COVID-19 Testing/methods , Diagnosis, Computer-Assisted , Equipment Design , Humans , Lab-On-A-Chip Devices , Limit of Detection , Microfluidic Analytical Techniques/methods , Microfluidics , Pattern Recognition, Automated , Quality Control , RNA, Viral/analysis , Reproducibility of Results , Respiratory Tract Infections/metabolism , Respiratory Tract Infections/virology , SARS-CoV-2 , Sensitivity and Specificity , Viruses
8.
J Chem Inf Model ; 61(8): 4058-4067, 2021 08 23.
Article in English | MEDLINE | ID: covidwho-1322447

ABSTRACT

The COVID-19 pandemic has motivated researchers all over the world in trying to find effective drugs and therapeutics for treating this disease. To save time, much effort has focused on repurposing drugs known for treating other diseases than COVID-19. To support these drug repurposing efforts, we built the CAS Biomedical Knowledge Graph and identified 1350 small molecules as potentially repurposable drugs that target host proteins and disease processes involved in COVID-19. A computer algorithm-driven drug-ranking method was developed to prioritize those identified small molecules. The top 50 molecules were analyzed according to their molecular functions and included 11 drugs in clinical trials for treating COVID-19 and new candidates that may be of interest for clinical investigation. The CAS Biomedical Knowledge Graph provides researchers an opportunity to accelerate innovation and streamline the investigative process not just for COVID-19 but also in many other diseases.


Subject(s)
COVID-19 , Drug Repositioning , Antiviral Agents , Humans , Pandemics , Pattern Recognition, Automated , SARS-CoV-2
9.
Appl Clin Inform ; 12(3): 629-636, 2021 05.
Article in English | MEDLINE | ID: covidwho-1309479

ABSTRACT

OBJECTIVES: Accurate metrics of provider activity within the electronic health record (EHR) are critical to understand workflow efficiency and target optimization initiatives. We utilized newly described, log-based core metrics at a tertiary cancer center during rapid escalation of telemedicine secondary to initial coronavirus disease-2019 (COVID-19) peak onset of social distancing restrictions at our medical center (COVID-19 peak). These metrics evaluate the impact on total EHR time, work outside of work, time on documentation, time on prescriptions, inbox time, teamwork for orders, and undivided attention patients receive during an encounter. Our study aims were to evaluate feasibility of implementing these metrics as an efficient tool to optimize provider workflow and to track impact on workflow to various provider groups, including physicians, advanced practice providers (APPs), and different medical divisions, during times of significant policy change in the treatment landscape. METHODS: Data compilation and analysis was retrospectively performed in Tableau utilizing user and schedule data obtained from Cerner Millennium PowerChart and our internal scheduling software. We analyzed three distinct time periods: the 3 months prior to the initial COVID-19 peak, the 3 months during peak, and 3 months immediately post-peak. RESULTS: Application of early COVID-19 restrictions led to a significant increase of telemedicine encounters from baseline <1% up to 29.2% of all patient encounters. During initial peak period, there was a significant increase in total EHR time, work outside of work, time on documentation, and inbox time for providers. Overall APPs spent significantly more time in the EHR compared with physicians. All of the metrics returned to near baseline after the initial COVID-19 peak in our area. CONCLUSION: Our analysis showed that implementation of these core metrics is both feasible and can provide an accurate representation of provider EHR workflow adjustments during periods of change, while providing a basis for cross-vendor and cross-institutional analysis.


Subject(s)
COVID-19/epidemiology , Cancer Care Facilities/statistics & numerical data , Electronic Health Records , Neoplasms/therapy , SARS-CoV-2 , Telemedicine/methods , Telemedicine/statistics & numerical data , Algorithms , Data Collection , Documentation , Health Policy , Humans , Pattern Recognition, Automated , Retrospective Studies , Software , User-Computer Interface , Workflow
10.
Genes (Basel) ; 12(7)2021 06 29.
Article in English | MEDLINE | ID: covidwho-1288843

ABSTRACT

This study builds a coronavirus knowledge graph (KG) by merging two information sources. The first source is Analytical Graph (AG), which integrates more than 20 different public datasets related to drug discovery. The second source is CORD-19, a collection of published scientific articles related to COVID-19. We combined both chemo genomic entities in AG with entities extracted from CORD-19 to expand knowledge in the COVID-19 domain. Before populating KG with those entities, we perform entity disambiguation on CORD-19 collections using Wikidata. Our newly built KG contains at least 21,700 genes, 2500 diseases, 94,000 phenotypes, and other biological entities (e.g., compound, species, and cell lines). We define 27 relationship types and use them to label each edge in our KG. This research presents two cases to evaluate the KG's usability: analyzing a subgraph (ego-centered network) from the angiotensin-converting enzyme (ACE) and revealing paths between biological entities (hydroxychloroquine and IL-6 receptor; chloroquine and STAT1). The ego-centered network captured information related to COVID-19. We also found significant COVID-19-related information in top-ranked paths with a depth of three based on our path evaluation.


Subject(s)
COVID-19 , Knowledge Bases , COVID-19/epidemiology , COVID-19/etiology , Chloroquine/pharmacology , Computer Graphics , Databases, Factual , Hemorrhagic Fever, Ebola/drug therapy , Humans , Hydroxychloroquine/pharmacology , Pattern Recognition, Automated , Peptidyl-Dipeptidase A/genetics , PubMed , Receptors, Interleukin-6/blood , SARS-CoV-2 , STAT1 Transcription Factor
11.
Biomed Res Int ; 2021: 9954615, 2021.
Article in English | MEDLINE | ID: covidwho-1285105

ABSTRACT

The last decade (2010-2021) has witnessed the evolution of robotic applications in orthodontics. This review scopes and analyzes published orthodontic literature in eight different domains: (1) robotic dental assistants; (2) robotics in diagnosis and simulation of orthodontic problems; (3) robotics in orthodontic patient education, teaching, and training; (4) wire bending and customized appliance robotics; (5) nanorobots/microrobots for acceleration of tooth movement and for remote monitoring; (6) robotics in maxillofacial surgeries and implant placement; (7) automated aligner production robotics; and (8) TMD rehabilitative robotics. A total of 1,150 records were searched, of which 124 potentially relevant articles were retrieved in full. 87 studies met the selection criteria following screening and were included in the scoping review. The review found that studies pertaining to arch wire bending and customized appliance robots, simulative robots for diagnosis, and surgical robots have been important areas of research in the last decade (32%, 22%, and 16%). Rehabilitative robots and nanorobots are quite promising and have been considerably reported in the orthodontic literature (13%, 9%). On the other hand, assistive robots, automated aligner production robots, and patient robots need more scientific data to be gathered in the future (1%, 1%, and 6%). Technological readiness of different robotic applications in orthodontics was further assessed. The presented eight domains of robotic technologies were assigned to an estimated technological readiness level according to the information given in the publications. Wire bending robots, TMD robots, nanorobots, and aligner production robots have reached the highest levels of technological readiness: 9; diagnostic robots and patient robots reached level 7, whereas surgical robots and assistive robots reached lower levels of readiness: 4 and 3, respectively.


Subject(s)
Orthodontics/methods , Orthodontics/trends , Robotics/instrumentation , Robotics/trends , Stomatognathic System , Automation , Equipment Design , Forecasting , Humans , Orthodontic Wires , Pattern Recognition, Automated , Software
12.
J Healthc Eng ; 2021: 3277988, 2021.
Article in English | MEDLINE | ID: covidwho-1277006

ABSTRACT

The world has been facing the COVID-19 pandemic since December 2019. Timely and efficient diagnosis of COVID-19 suspected patients plays a significant role in medical treatment. The deep transfer learning-based automated COVID-19 diagnosis on chest X-ray is required to counter the COVID-19 outbreak. This work proposes a real-time Internet of Things (IoT) framework for early diagnosis of suspected COVID-19 patients by using ensemble deep transfer learning. The proposed framework offers real-time communication and diagnosis of COVID-19 suspected cases. The proposed IoT framework ensembles four deep learning models such as InceptionResNetV2, ResNet152V2, VGG16, and DenseNet201. The medical sensors are utilized to obtain the chest X-ray modalities and diagnose the infection by using the deep ensemble model stored on the cloud server. The proposed deep ensemble model is compared with six well-known transfer learning models over the chest X-ray dataset. Comparative analysis revealed that the proposed model can help radiologists to efficiently and timely diagnose the COVID-19 suspected patients.


Subject(s)
Artificial Intelligence , COVID-19 Testing , COVID-19/diagnosis , Internet of Things , SARS-CoV-2 , Brazil , China , Computer Simulation , Computer Systems , Databases, Factual , Deep Learning , Diagnosis, Computer-Assisted , Humans , Pattern Recognition, Automated , Radiography, Thoracic , United States , X-Rays
13.
Stud Health Technol Inform ; 281: 744-748, 2021 May 27.
Article in English | MEDLINE | ID: covidwho-1247805

ABSTRACT

This paper presents the results of a new approach to discover related health and social factors during the COVID-19 pandemic. The approach leverages a knowledge graph of related concepts mined from a corpus of published evidence (PubMed) prior to the pandemic. Population trends from online searches were used to identify social determinants of health (SDoH) concepts that trended high at the outset of the pandemic from a list of SDoH topics from the World Health Organization (WHO). The trending concepts were then mapped to the knowledge graph and a subsequent analysis of the derived insights, spanning two years, was conducted. This paper suggests an approach to derive new related health and social factors that may have either played a role in, or been affected by, the onset of the global COVID-19 pandemic. In particular, our results show how, from a list of SDoH topics, Food Security, Unemployment trended the highest at the start of the pandemic. Further work is needed to continue to ascertain the validity of the derived relations in a population health context and to improve mining insights from published evidence.


Subject(s)
COVID-19 , Pandemics , Humans , Pattern Recognition, Automated , SARS-CoV-2 , Social Determinants of Health
14.
Stud Health Technol Inform ; 281: 392-396, 2021 May 27.
Article in English | MEDLINE | ID: covidwho-1247793

ABSTRACT

This paper proposes an automated knowledge synthesis and discovery framework to analyze published literature to identify and represent underlying mechanistic associations that aggravate chronic conditions due to COVID-19. We present a literature-based discovery approach that integrates text mining, knowledge graphs and ontologies to discover semantic associations between COVID-19 and chronic disease concepts that were represented as a complex disease knowledge network that can be queried to extract plausible mechanisms by which COVID-19 may be exacerbated by underlying chronic conditions.


Subject(s)
COVID-19 , Diabetes Mellitus , Kidney Diseases , Data Mining , Humans , Pattern Recognition, Automated , SARS-CoV-2
15.
BMC Bioinformatics ; 22(1): 229, 2021 May 03.
Article in English | MEDLINE | ID: covidwho-1215097

ABSTRACT

BACKGROUND: Leveraging previously identified viral interactions with human host proteins, we apply a machine learning-based approach to connect SARS-CoV-2 viral proteins to relevant host biological functions, diseases, and pathways in a large-scale knowledge graph derived from the biomedical literature. Our goal is to explore how SARS-CoV-2 could interfere with various host cell functions, and to identify drug targets amongst the host genes that could potentially be modulated against COVID-19 by repurposing existing drugs. The machine learning model employed here involves gene embeddings that leverage causal gene expression signatures curated from literature. In contrast to other network-based approaches for drug repurposing, our approach explicitly takes the direction of effects into account, distinguishing between activation and inhibition. RESULTS: We have constructed 70 networks connecting SARS-CoV-2 viral proteins to various biological functions, diseases, and pathways reflecting viral biology, clinical observations, and co-morbidities in the context of COVID-19. Results are presented in the form of interactive network visualizations through a web interface, the Coronavirus Network Explorer (CNE), that allows exploration of underlying experimental evidence. We find that existing drugs targeting genes in those networks are strongly enriched in the set of drugs that are already in clinical trials against COVID-19. CONCLUSIONS: The approach presented here can identify biologically plausible hypotheses for COVID-19 pathogenesis, explicitly connected to the immunological, virological and pathological observations seen in SARS-CoV-2 infected patients. The discovery of repurposable drugs is driven by prior knowledge of relevant functional endpoints that reflect known viral biology or clinical observations, therefore suggesting potential mechanisms of action. We believe that the CNE offers relevant insights that go beyond more conventional network approaches, and can be a valuable tool for drug repurposing. The CNE is available at https://digitalinsights.qiagen.com/coronavirus-network-explorer .


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , Pattern Recognition, Automated , Transcriptome
16.
Comput Math Methods Med ; 2021: 8854892, 2021.
Article in English | MEDLINE | ID: covidwho-1202025

ABSTRACT

Pneumonia remains a threat to human health; the coronavirus disease 2019 (COVID-19) that began at the end of 2019 had a major impact on the world. It is still raging in many countries and has caused great losses to people's lives and property. In this paper, we present a method based on DeepConv-DilatedNet of identifying and localizing pneumonia in chest X-ray (CXR) images. Two-stage detector Faster R-CNN is adopted as the structure of a network. Feature Pyramid Network (FPN) is integrated into the residual neural network of a dilated bottleneck so that the deep features are expanded to preserve the deep feature and position information of the object. In the case of DeepConv-DilatedNet, the deconvolution network is used to restore high-level feature maps into its original size, and the target information is further retained. On the other hand, DeepConv-DilatedNet uses a popular fully convolution architecture with computation shared on the entire image. Then, Soft-NMS is used to screen boxes and ensure sample quality. Also, K-Means++ is used to generate anchor boxes to improve the localization accuracy. The algorithm obtained 39.23% Mean Average Precision (mAP) on the X-ray image dataset from the Radiological Society of North America (RSNA) and got 38.02% Mean Average Precision (mAP) on the ChestX-ray14 dataset, surpassing other detection algorithms. So, in this paper, an improved algorithm that can provide doctors with location information of pneumonia lesions is proposed.


Subject(s)
COVID-19/complications , COVID-19/diagnostic imaging , Pattern Recognition, Automated , Pneumonia/diagnostic imaging , Algorithms , Deep Learning , Diagnosis, Computer-Assisted , Humans , Lung/diagnostic imaging , Neural Networks, Computer , ROC Curve , Radiography, Thoracic , Reproducibility of Results
17.
IEEE J Biomed Health Inform ; 25(6): 1852-1863, 2021 06.
Article in English | MEDLINE | ID: covidwho-1165626

ABSTRACT

The coronavirus (COVID-19) pandemic has been adversely affecting people's health globally. To diminish the effect of this widespread pandemic, it is essential to detect COVID-19 cases as quickly as possible. Chest radiographs are less expensive and are a widely available imaging modality for detecting chest pathology compared with CT images. They play a vital role in early prediction and developing treatment plans for suspected or confirmed COVID-19 chest infection patients. In this paper, a novel shape-dependent Fibonacci-p patterns-based feature descriptor using a machine learning approach is proposed. Computer simulations show that the presented system (1) increases the effectiveness of differentiating COVID-19, viral pneumonia, and normal conditions, (2) is effective on small datasets, and (3) has faster inference time compared to deep learning methods with comparable performance. Computer simulations are performed on two publicly available datasets; (a) the Kaggle dataset, and (b) the COVIDGR dataset. To assess the performance of the presented system, various evaluation parameters, such as accuracy, recall, specificity, precision, and f1-score are used. Nearly 100% differentiation between normal and COVID-19 radiographs is observed for the three-class classification scheme using the lung area-specific Kaggle radiographs. While Recall of 72.65 ± 6.83 and specificity of 77.72 ± 8.06 is observed for the COVIDGR dataset.


Subject(s)
COVID-19/diagnostic imaging , Pattern Recognition, Automated , Pneumonia, Viral/diagnostic imaging , Automation , COVID-19/virology , Computer Simulation , Humans , Machine Learning , Pneumonia, Viral/virology , Radiography, Thoracic , SARS-CoV-2/isolation & purification , Sensitivity and Specificity , Tomography, X-Ray Computed
18.
J Korean Med Sci ; 36(5): e46, 2021 Feb 01.
Article in English | MEDLINE | ID: covidwho-1059630

ABSTRACT

BACKGROUND: It is difficult to distinguish subtle differences shown in computed tomography (CT) images of coronavirus disease 2019 (COVID-19) and bacterial pneumonia patients, which often leads to an inaccurate diagnosis. It is desirable to design and evaluate interpretable feature extraction techniques to describe the patient's condition. METHODS: This is a retrospective cohort study of 170 confirmed patients with COVID-19 or bacterial pneumonia acquired at Yeungnam University Hospital in Daegu, Korea. The Lung and lesion regions were segmented to crop the lesion into 2D patches to train a classifier model that could differentiate between COVID-19 and bacterial pneumonia. The K-means algorithm was used to cluster deep features extracted by the trained model into 20 groups. Each lesion patch cluster was described by a characteristic imaging term for comparison. For each CT image containing multiple lesions, a histogram of lesion types was constructed using the cluster information. Finally, a Support Vector Machine classifier was trained with the histogram and radiomics features to distinguish diseases and severity. RESULTS: The 20 clusters constructed from 170 patients were reviewed based on common radiographic appearance types. Two clusters showed typical findings of COVID-19, with two other clusters showing typical findings related to bacterial pneumonia. Notably, there is one cluster that showed bilateral diffuse ground-glass opacities (GGOs) in the central and peripheral lungs and was considered to be a key factor for severity classification. The proposed method achieved an accuracy of 91.2% for classifying COVID-19 and bacterial pneumonia patients with 95% reported for severity classification. The CT quantitative parameters represented by the values of cluster 8 were correlated with existing laboratory data and clinical parameters. CONCLUSION: Deep chest CT analysis with constructed lesion clusters revealed well-known COVID-19 CT manifestations comparable to manual CT analysis. The constructed histogram features improved accuracy for both diseases and severity classification, and showed correlations with laboratory data and clinical parameters. The constructed histogram features can provide guidance for improved analysis and treatment of COVID-19.


Subject(s)
COVID-19/diagnostic imaging , Lung/diagnostic imaging , Pneumonia, Bacterial/diagnostic imaging , Respiratory Distress Syndrome/diagnostic imaging , Tomography, X-Ray Computed , Adult , Aged , Algorithms , Artificial Intelligence , Cluster Analysis , Deep Learning , Female , Humans , Male , Middle Aged , Pattern Recognition, Automated , Reproducibility of Results , Republic of Korea/epidemiology , Respiratory Distress Syndrome/complications , Retrospective Studies , Severity of Illness Index , Support Vector Machine
20.
J Healthc Eng ; 2020: 6648574, 2020.
Article in English | MEDLINE | ID: covidwho-991957

ABSTRACT

For the last few years, computer-aided diagnosis (CAD) has been increasing rapidly. Numerous machine learning algorithms have been developed to identify different diseases, e.g., leukemia. Leukemia is a white blood cells- (WBC-) related illness affecting the bone marrow and/or blood. A quick, safe, and accurate early-stage diagnosis of leukemia plays a key role in curing and saving patients' lives. Based on developments, leukemia consists of two primary forms, i.e., acute and chronic leukemia. Each form can be subcategorized as myeloid and lymphoid. There are, therefore, four leukemia subtypes. Various approaches have been developed to identify leukemia with respect to its subtypes. However, in terms of effectiveness, learning process, and performance, these methods require improvements. This study provides an Internet of Medical Things- (IoMT-) based framework to enhance and provide a quick and safe identification of leukemia. In the proposed IoMT system, with the help of cloud computing, clinical gadgets are linked to network resources. The system allows real-time coordination for testing, diagnosis, and treatment of leukemia among patients and healthcare professionals, which may save both time and efforts of patients and clinicians. Moreover, the presented framework is also helpful for resolving the problems of patients with critical condition in pandemics such as COVID-19. The methods used for the identification of leukemia subtypes in the suggested framework are Dense Convolutional Neural Network (DenseNet-121) and Residual Convolutional Neural Network (ResNet-34). Two publicly available datasets for leukemia, i.e., ALL-IDB and ASH image bank, are used in this study. The results demonstrated that the suggested models supersede the other well-known machine learning algorithms used for healthy-versus-leukemia-subtypes identification.


Subject(s)
Deep Learning , Diagnosis, Computer-Assisted , Internet of Things , Leukemia/classification , Leukemia/diagnosis , Pattern Recognition, Automated , Algorithms , COVID-19/epidemiology , Cloud Computing , Databases, Factual , Diagnostic Imaging , Humans , Leukemia, Lymphocytic, Chronic, B-Cell/diagnosis , Leukemia, Myelogenous, Chronic, BCR-ABL Positive/diagnosis , Leukemia, Myeloid, Acute/diagnosis , Machine Learning , Neural Networks, Computer , Precursor Cell Lymphoblastic Leukemia-Lymphoma/diagnosis , Telemedicine
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