Evidence map›Paper›PMID 36292042›Full record

ArticleDiagnostics (Basel, Switzerland)2022

Enhancing Annotation Efficiency with Machine Learning: Automated Partitioning of a Lung Ultrasound Dataset by View.

Bennett VanBerlo, Delaney Smith, Jared Tschirhart, Blake VanBerlo, Derek Wu, Alex Ford, Joseph McCauley, Benjamin Wu, Rushil Chaudhary, Chintan Dave and 4 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Bennett VanBerloFaculty of Engineering, University of Western Ontario, London, ON N6A 5C1, Canada.
Delaney SmithFaculty of Mathematics, University of Waterloo, Waterloo, ON N2L 3G1, Canada.ORCID 0000-0001-8367-880X
Jared TschirhartSchulich School of Medicine and Dentistry, Western University, London, ON N6A 5C1, Canada.ORCID 0000-0003-1313-350X
Blake VanBerloFaculty of Mathematics, University of Waterloo, Waterloo, ON N2L 3G1, Canada.ORCID 0000-0002-1593-8275
Derek WuDepartment of Medicine, Western University, London, ON N6A 5C1, Canada.
Alex FordLawson Health Research Institute, London, ON N6C 2R5, Canada.
Joseph McCauleyFaculty of Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
Benjamin WuLawson Health Research Institute, London, ON N6C 2R5, Canada.
Rushil ChaudharyDepartment of Medicine, Western University, London, ON N6A 5C1, Canada.
Chintan DaveDivision of Critical Care Medicine, Western University, London, ON N6A 5C1, Canada.
Jordan HoDepartment of Family Medicine, Western University, London, ON N6A 5C1, Canada.
Jason DeglintFaculty of Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
Brian LiFaculty of Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.ORCID 0000-0002-7496-9506
Robert ArntfieldDivision of Critical Care Medicine, Western University, London, ON N6A 5C1, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAnnotating large medical imaging datasets is an arduous and expensive task, especially when the datasets in question are not organized according to deep learning goals. Here, we propose a method that exploits the hierarchical organization of annotating tasks to optimize efficiency.

methodsWe trained a machine learning model to accurately distinguish between one of two classes of lung ultrasound (LUS) views using 2908 clips from a larger dataset. Partitioning the remaining dataset by view would reduce downstream labelling efforts by enabling annotators to focus on annotating pathological features specific to each view.

resultsIn a sample view-specific annotation task, we found that automatically partitioning a 780-clip dataset by view saved 42 min of manual annotation time and resulted in 55±6 additional relevant labels per hour.

conclusionsAutomatic partitioning of a LUS dataset by view significantly increases annotator efficiency, resulting in higher throughput relevant to the annotating task at hand. The strategy described in this work can be applied to other hierarchical annotation schemes.

Indexed as

annotationcomputer visiondeep learninglabellinglung ultrasoundmachine learningmedical imaging

Identifiers

PMID36292042
PMCPMC9601089

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.