JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1103", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Clinical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "COVID-19", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Deep Learning", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Female", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Male", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Pandemics", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Pneumonia", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Radiography", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "X-Rays", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Grupo VARPA, Instituto de Investigaci\u00f3n Biom\u00e9dica de A Coru\u00f1a (INIBIC), Universidade da Coru\u00f1a, 15006, A Coru\u00f1a, Spain", 
          "id": "http://www.grid.ac/institutes/grid.8073.c", 
          "name": [
            "Centro de Investigaci\u00f3n CITIC, Universidade da Coru\u00f1a, Campus de Elvi\u00f1a, 15071, A Coru\u00f1a, Spain", 
            "Grupo VARPA, Instituto de Investigaci\u00f3n Biom\u00e9dica de A Coru\u00f1a (INIBIC), Universidade da Coru\u00f1a, 15006, A Coru\u00f1a, Spain"
          ], 
          "type": "Organization"
        }, 
        "familyName": "\u00c1lvarez-Rodr\u00edguez", 
        "givenName": "Lorena", 
        "id": "sg:person.012772514602.86", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012772514602.86"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Grupo VARPA, Instituto de Investigaci\u00f3n Biom\u00e9dica de A Coru\u00f1a (INIBIC), Universidade da Coru\u00f1a, 15006, A Coru\u00f1a, Spain", 
          "id": "http://www.grid.ac/institutes/grid.8073.c", 
          "name": [
            "Centro de Investigaci\u00f3n CITIC, Universidade da Coru\u00f1a, Campus de Elvi\u00f1a, 15071, A Coru\u00f1a, Spain", 
            "Grupo VARPA, Instituto de Investigaci\u00f3n Biom\u00e9dica de A Coru\u00f1a (INIBIC), Universidade da Coru\u00f1a, 15006, A Coru\u00f1a, Spain"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Moura", 
        "givenName": "Joaquim de", 
        "id": "sg:person.014736775640.63", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014736775640.63"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Grupo VARPA, Instituto de Investigaci\u00f3n Biom\u00e9dica de A Coru\u00f1a (INIBIC), Universidade da Coru\u00f1a, 15006, A Coru\u00f1a, Spain", 
          "id": "http://www.grid.ac/institutes/grid.8073.c", 
          "name": [
            "Centro de Investigaci\u00f3n CITIC, Universidade da Coru\u00f1a, Campus de Elvi\u00f1a, 15071, A Coru\u00f1a, Spain", 
            "Grupo VARPA, Instituto de Investigaci\u00f3n Biom\u00e9dica de A Coru\u00f1a (INIBIC), Universidade da Coru\u00f1a, 15006, A Coru\u00f1a, Spain"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Novo", 
        "givenName": "Jorge", 
        "id": "sg:person.01263222603.88", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01263222603.88"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Grupo VARPA, Instituto de Investigaci\u00f3n Biom\u00e9dica de A Coru\u00f1a (INIBIC), Universidade da Coru\u00f1a, 15006, A Coru\u00f1a, Spain", 
          "id": "http://www.grid.ac/institutes/grid.8073.c", 
          "name": [
            "Centro de Investigaci\u00f3n CITIC, Universidade da Coru\u00f1a, Campus de Elvi\u00f1a, 15071, A Coru\u00f1a, Spain", 
            "Grupo VARPA, Instituto de Investigaci\u00f3n Biom\u00e9dica de A Coru\u00f1a (INIBIC), Universidade da Coru\u00f1a, 15006, A Coru\u00f1a, Spain"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ortega", 
        "givenName": "Marcos", 
        "id": "sg:person.0665112667.10", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0665112667.10"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/s41746-020-0288-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1128093519", 
          "https://doi.org/10.1038/s41746-020-0288-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41598-020-76550-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1132499687", 
          "https://doi.org/10.1038/s41598-020-76550-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41598-021-90766-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1138514323", 
          "https://doi.org/10.1038/s41598-021-90766-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s13755-020-00116-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1131248578", 
          "https://doi.org/10.1007/s13755-020-00116-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10916-021-01745-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1138682364", 
          "https://doi.org/10.1007/s10916-021-01745-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s42600-020-00091-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1130566023", 
          "https://doi.org/10.1007/s42600-020-00091-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s42979-021-00695-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1138366413", 
          "https://doi.org/10.1007/s42979-021-00695-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4842-2766-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084932924", 
          "https://doi.org/10.1007/978-1-4842-2766-4"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2022-04-28", 
    "datePublishedReg": "2022-04-28", 
    "description": "BackgroundThe health crisis resulting from the global COVID-19 pandemic highlighted more than ever the need for rapid, reliable and safe methods of diagnosis and monitoring of respiratory diseases. To study pulmonary involvement in detail, one of the most common resources is the use of different lung imaging modalities (like chest radiography) to explore the possible affected areas.MethodsThe study of patient characteristics like sex and age in pathologies of this type is crucial for gaining knowledge of the disease and for avoiding biases due to the clear scarcity of data when developing representative systems. In this work, we performed an analysis of these factors in chest X-ray images to identify biases. Specifically, 11 imbalance scenarios were defined with female and male COVID-19 patients present in different proportions for the sex analysis, and 6 scenarios where only one specific age range was used for training for the age factor. In each study, 3 different approaches for automatic COVID-19 screening were used: Normal vs COVID-19, Pneumonia vs COVID-19 and Non-COVID-19 vs COVID-19. The study was validated using two public chest X-ray datasets, allowing a reliable analysis to support the clinical decision-making process.ResultsThe results for the sex-related analysis indicate this factor slightly affects the system in the Normal VS COVID-19 and Pneumonia VS COVID-19 approaches, although the identified differences are not relevant enough to worsen considerably the system. Regarding the age-related analysis, this factor was observed to be influencing the system in a more consistent way than the sex factor, as it was present in all considered scenarios. However, this worsening does not represent a major factor, as it is not of great magnitude.ConclusionsMultiple studies have been conducted in other fields in order to determine if certain patient characteristics such as sex or age influenced these deep learning systems. However, to the best of our knowledge, this study has not been done for COVID-19 despite the urgency and lack of COVID-19 chest x-ray images. The presented results evidenced that the proposed methodology and tested approaches allow a robust and reliable analysis to support the clinical decision-making process in this pandemic scenario.", 
    "genre": "article", 
    "id": "sg:pub.10.1186/s12874-022-01578-w", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.9666469", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1024940", 
        "issn": [
          "1471-2288"
        ], 
        "name": "BMC Medical Research Methodology", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "22"
      }
    ], 
    "keywords": [
      "clinical decision-making process", 
      "COVID-19 screening", 
      "patient characteristics", 
      "COVID-19", 
      "male COVID-19 patients", 
      "COVID-19 patients", 
      "certain patient characteristics", 
      "sex-related analysis", 
      "age-related analysis", 
      "pulmonary involvement", 
      "respiratory disease", 
      "MethodsThe study", 
      "specific age ranges", 
      "safe method", 
      "different lung", 
      "age range", 
      "COVID-19 pandemic", 
      "health crisis", 
      "disease", 
      "sex analysis", 
      "chest X-ray images", 
      "age factor", 
      "greater magnitude", 
      "age", 
      "sex", 
      "global COVID-19 pandemic", 
      "pandemic scenario", 
      "screening", 
      "sex factor", 
      "factors", 
      "pneumonia", 
      "patients", 
      "lung", 
      "worsening", 
      "study", 
      "diagnosis", 
      "pathology", 
      "ResultsThe", 
      "decision-making process", 
      "modalities", 
      "involvement", 
      "X-ray images", 
      "pandemic", 
      "major factor", 
      "chest X-ray dataset", 
      "proportion", 
      "imbalance", 
      "analysis", 
      "differences", 
      "urgency", 
      "knowledge", 
      "training", 
      "lack", 
      "X-ray dataset", 
      "deep learning system", 
      "monitoring", 
      "characteristics", 
      "need", 
      "use", 
      "reliable analysis", 
      "data", 
      "types", 
      "approach", 
      "area", 
      "results", 
      "system", 
      "different proportions", 
      "COVID-19 chest X-ray images", 
      "images", 
      "method", 
      "different approaches", 
      "magnitude", 
      "biases", 
      "process", 
      "range", 
      "scarcity", 
      "crisis", 
      "order", 
      "detail", 
      "public chest X-ray datasets", 
      "dataset", 
      "resources", 
      "deep learning approach", 
      "way", 
      "scenarios", 
      "presented results", 
      "work", 
      "methodology", 
      "consistent way", 
      "field", 
      "learning approach", 
      "learning system", 
      "common resources", 
      "representative system", 
      "imbalance scenarios", 
      "clear scarcity"
    ], 
    "name": "Does imbalance in chest X-ray datasets produce biased deep learning approaches for COVID-19 screening?", 
    "pagination": "125", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1147366899"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s12874-022-01578-w"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "35484483"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s12874-022-01578-w", 
      "https://app.dimensions.ai/details/publication/pub.1147366899"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-06-01T22:23", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220601/entities/gbq_results/article/article_931.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1186/s12874-022-01578-w"
  }
]

Download the RDF metadata as:  json-ld nt turtle xml License info

This table displays all metadata directly associated to this object as RDF triples.

249 TRIPLES      22 PREDICATES      139 URIs      123 LITERALS      16 BLANK NODES