Academic Journal
View in EDS

Deep-learning generated B-line score mirrors clinical progression of disease for patients with heart failure

Bibliographic Details
Title: Deep-learning generated B-line score mirrors clinical progression of disease for patients with heart failure
Authors: Cristiana Baloescu, Alvin Chen, Alexander Varasteh, Jane Hall, Grzegorz Toporek, Shubham Patil, Robert L. McNamara, Balasundar Raju, Christopher L. Moore
Source: The Ultrasound Journal, Vol 16, Iss 1, Pp 1-9 (2024)
Publisher Information: SpringerOpen, 2024.
Publication Year: 2024
Collection: LCC:Medical physics. Medical radiology. Nuclear medicine
Subject Terms: Point-of-care ultrasound, Lung ultrasound, B-lines, Heart failure, Artificial intelligence, Medical physics. Medical radiology. Nuclear medicine, R895-920
More Details: Abstract Background Ultrasound can detect fluid in the alveolar and interstitial spaces of the lung using the presence of artifacts known as B-lines. The aim of this study was to determine whether a deep learning algorithm generated B-line severity score correlated with pulmonary congestion and disease severity based on clinical assessment (as identified by composite congestion score and Rothman index) and to evaluate changes in the score with treatment. Patients suspected of congestive heart failure underwent daily ultrasonography. Eight lung zones (right and left anterior/lateral and superior/inferior) were scanned using a tablet ultrasound system with a phased-array probe. Mixed effects modeling explored the association between average B-line score and the composite congestion score, and average B-line score and Rothman index, respectively. Covariates tested included patient and exam level data (sex, age, presence of selected comorbidities, baseline sodium and hemoglobin, creatinine, vital signs, oxygen delivery amount and delivery method, diuretic dose). Results Analysis included 110 unique subjects (3379 clips). B-line severity score was significantly associated with the composite congestion score, with a coefficient of 0.7 (95% CI 0.1–1.2 p = 0.02), but was not significantly associated with the Rothman index. Conclusions Use of this technology may allow clinicians with limited ultrasound experience to determine an objective measure of B-line burden.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2524-8987
Relation: https://doaj.org/toc/2524-8987
DOI: 10.1186/s13089-024-00391-4
Access URL: https://doaj.org/article/2cf1376aabda4a61baab428a5002bf6c
Accession Number: edsdoj.2cf1376aabda4a61baab428a5002bf6c
Database: Directory of Open Access Journals
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://resolver.ebsco.com/c/xy5jbn/result?sid=EBSCO:edsdoj&genre=article&issn=25248987&ISBN=&volume=16&issue=1&date=20240901&spage=1&pages=1-9&title=The Ultrasound Journal&atitle=Deep-learning%20generated%20B-line%20score%20mirrors%20clinical%20progression%20of%20disease%20for%20patients%20with%20heart%20failure&aulast=Cristiana%20Baloescu&id=DOI:10.1186/s13089-024-00391-4
    Name: Full Text Finder (for New FTF UI) (s8985755)
    Category: fullText
    Text: Find It @ SCU Libraries
    MouseOverText: Find It @ SCU Libraries
  – Url: https://doaj.org/article/2cf1376aabda4a61baab428a5002bf6c
    Name: EDS - DOAJ (s8985755)
    Category: fullText
    Text: View record from DOAJ
    MouseOverText: View record from DOAJ
Header DbId: edsdoj
DbLabel: Directory of Open Access Journals
An: edsdoj.2cf1376aabda4a61baab428a5002bf6c
RelevancyScore: 1047
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 1047.25537109375
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Deep-learning generated B-line score mirrors clinical progression of disease for patients with heart failure
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Cristiana+Baloescu%22">Cristiana Baloescu</searchLink><br /><searchLink fieldCode="AR" term="%22Alvin+Chen%22">Alvin Chen</searchLink><br /><searchLink fieldCode="AR" term="%22Alexander+Varasteh%22">Alexander Varasteh</searchLink><br /><searchLink fieldCode="AR" term="%22Jane+Hall%22">Jane Hall</searchLink><br /><searchLink fieldCode="AR" term="%22Grzegorz+Toporek%22">Grzegorz Toporek</searchLink><br /><searchLink fieldCode="AR" term="%22Shubham+Patil%22">Shubham Patil</searchLink><br /><searchLink fieldCode="AR" term="%22Robert+L%2E+McNamara%22">Robert L. McNamara</searchLink><br /><searchLink fieldCode="AR" term="%22Balasundar+Raju%22">Balasundar Raju</searchLink><br /><searchLink fieldCode="AR" term="%22Christopher+L%2E+Moore%22">Christopher L. Moore</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: The Ultrasound Journal, Vol 16, Iss 1, Pp 1-9 (2024)
– Name: Publisher
  Label: Publisher Information
  Group: PubInfo
  Data: SpringerOpen, 2024.
– Name: DatePubCY
  Label: Publication Year
  Group: Date
  Data: 2024
– Name: Subset
  Label: Collection
  Group: HoldingsInfo
  Data: LCC:Medical physics. Medical radiology. Nuclear medicine
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Point-of-care+ultrasound%22">Point-of-care ultrasound</searchLink><br /><searchLink fieldCode="DE" term="%22Lung+ultrasound%22">Lung ultrasound</searchLink><br /><searchLink fieldCode="DE" term="%22B-lines%22">B-lines</searchLink><br /><searchLink fieldCode="DE" term="%22Heart+failure%22">Heart failure</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+physics%2E+Medical+radiology%2E+Nuclear+medicine%22">Medical physics. Medical radiology. Nuclear medicine</searchLink><br /><searchLink fieldCode="DE" term="%22R895-920%22">R895-920</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Abstract Background Ultrasound can detect fluid in the alveolar and interstitial spaces of the lung using the presence of artifacts known as B-lines. The aim of this study was to determine whether a deep learning algorithm generated B-line severity score correlated with pulmonary congestion and disease severity based on clinical assessment (as identified by composite congestion score and Rothman index) and to evaluate changes in the score with treatment. Patients suspected of congestive heart failure underwent daily ultrasonography. Eight lung zones (right and left anterior/lateral and superior/inferior) were scanned using a tablet ultrasound system with a phased-array probe. Mixed effects modeling explored the association between average B-line score and the composite congestion score, and average B-line score and Rothman index, respectively. Covariates tested included patient and exam level data (sex, age, presence of selected comorbidities, baseline sodium and hemoglobin, creatinine, vital signs, oxygen delivery amount and delivery method, diuretic dose). Results Analysis included 110 unique subjects (3379 clips). B-line severity score was significantly associated with the composite congestion score, with a coefficient of 0.7 (95% CI 0.1–1.2 p = 0.02), but was not significantly associated with the Rothman index. Conclusions Use of this technology may allow clinicians with limited ultrasound experience to determine an objective measure of B-line burden.
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: article
– Name: Format
  Label: File Description
  Group: SrcInfo
  Data: electronic resource
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 2524-8987
– Name: NoteTitleSource
  Label: Relation
  Group: SrcInfo
  Data: https://doaj.org/toc/2524-8987
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1186/s13089-024-00391-4
– Name: URL
  Label: Access URL
  Group: URL
  Data: <link linkTarget="URL" linkTerm="https://doaj.org/article/2cf1376aabda4a61baab428a5002bf6c" linkWindow="_blank">https://doaj.org/article/2cf1376aabda4a61baab428a5002bf6c</link>
– Name: AN
  Label: Accession Number
  Group: ID
  Data: edsdoj.2cf1376aabda4a61baab428a5002bf6c
PLink https://login.libproxy.scu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edsdoj&AN=edsdoj.2cf1376aabda4a61baab428a5002bf6c
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1186/s13089-024-00391-4
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 9
        StartPage: 1
    Subjects:
      – SubjectFull: Point-of-care ultrasound
        Type: general
      – SubjectFull: Lung ultrasound
        Type: general
      – SubjectFull: B-lines
        Type: general
      – SubjectFull: Heart failure
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Medical physics. Medical radiology. Nuclear medicine
        Type: general
      – SubjectFull: R895-920
        Type: general
    Titles:
      – TitleFull: Deep-learning generated B-line score mirrors clinical progression of disease for patients with heart failure
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Cristiana Baloescu
      – PersonEntity:
          Name:
            NameFull: Alvin Chen
      – PersonEntity:
          Name:
            NameFull: Alexander Varasteh
      – PersonEntity:
          Name:
            NameFull: Jane Hall
      – PersonEntity:
          Name:
            NameFull: Grzegorz Toporek
      – PersonEntity:
          Name:
            NameFull: Shubham Patil
      – PersonEntity:
          Name:
            NameFull: Robert L. McNamara
      – PersonEntity:
          Name:
            NameFull: Balasundar Raju
      – PersonEntity:
          Name:
            NameFull: Christopher L. Moore
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 09
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
              Value: 25248987
          Numbering:
            – Type: volume
              Value: 16
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: The Ultrasound Journal
              Type: main
ResultId 1