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Research

Evaluating the benefits of machine learning for diagnosing deep vein thrombosis compared with gold standard ultrasound: a feasibility study

Kerstin Nothnagel and Mohammed Farid Aslam
BJGP Open 2024; 8 (4): BJGPO.2024.0057. DOI: https://doi.org/10.3399/BJGPO.2024.0057
Kerstin Nothnagel
1 Population Health Sciences, Canynge Hall, Bristol Medical School, University of Bristol, Bristol, UK
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  • ORCID record for Kerstin Nothnagel
  • For correspondence: Kerstin.Nothnagel{at}bristol.ac.uk
Mohammed Farid Aslam
2 Imperial College London, London, England, UK
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Article Figures & Data

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  •  A–G Workflow of ThinkSono Guidance (it guides the operator through anatomical landmarks and indicates when to compress. ThinkSono Guidance analyses the images, and provides a remote diagnosis)
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    Figure 1. A–G Workflow of ThinkSono Guidance (it guides the operator through anatomical landmarks and indicates when to compress. ThinkSono Guidance analyses the images, and provides a remote diagnosis)
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    Figure 2. Flowchart

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    Table 1. American College of Emergency Physicians (ACEP) score
    ACEP score
    1 No recognisable structures, no objective data can be gathered
    2 Minimally recognisable structures, but insufficient for diagnosis
    3 Minimal criteria met for diagnosis, recognisable structures but with some technical or other flaws
    4 Minimal criteria met for diagnosis, all structures images well and diagnosis supported easily
    5 Minimal criteria met for diagnosis, all structures images with excellent image quality and diagnosis completely supported
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    Table 2. Remote specialist reported diagnosis, index scan and sonographer diagnosis, and reference scan
    Reference scan positiveReference scan negative
    Index scan positive 9% TP9% FP
    Index scan negative 0% FN83% TN
    • FN = false negative. FP = false positive. TN = true negative. TP = true positive.

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    Table 3. Summary of key information
    All scansResult
    Image qualitya 75% (68/91) scans with ACEP ≥3
    Remote diagnosis64% (58/91) scans included for diagnosis
    Risk triagingb 53% (48/91) of participants at low risk
    Scans considered for remote analysis
    Image qualitya 91% (68/75) scans with ACEP ≥3
    Remote diagnosis77% (58/75) scans included for diagnosis
    Risk triagingb 64% (48/75) of participants at low risk
    • aDemonstrating adequate quality for remote interpretation. bSuggesting potential avoidance of a formal scan in secondary care

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    Table 4. Risk triaging
    Risk categoryCriteria for designation
    High risk Incomplete examination
    At least 1 sequence with ACEP <3
    Incompressible veins
    Other reasons deemed by reviewer, for example, considerations for differential diagnosis such as a Baker's Cyst
    Low risk Complete examination
    All cine-loop sequences with ACEP ≥3
    All veins are identified as compressible
    • ACEP = American College of Emergency Physicians

Supplementary Data

  • KN_10.3399BJGPO.2024.0057.pdf -

    Supplementary material is not copyedited or typeset, and is published as supplied by the author(s). The author(s) retain(s) responsibility for its accuracy.

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Evaluating the benefits of machine learning for diagnosing deep vein thrombosis compared with gold standard ultrasound: a feasibility study
Kerstin Nothnagel, Mohammed Farid Aslam
BJGP Open 2024; 8 (4): BJGPO.2024.0057. DOI: 10.3399/BJGPO.2024.0057

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Evaluating the benefits of machine learning for diagnosing deep vein thrombosis compared with gold standard ultrasound: a feasibility study
Kerstin Nothnagel, Mohammed Farid Aslam
BJGP Open 2024; 8 (4): BJGPO.2024.0057. DOI: 10.3399/BJGPO.2024.0057
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Keywords

  • Clinical (general)
  • Screening
  • diagnosis
  • venous thrombosis
  • artificial intelligence

More in this TOC Section

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