Gamified Crowdsourcing as a Novel Approach to Lung Ultrasound Dataset Labeling
Study Objective: Machine learning models have advanced medical image
processing and can yield faster, more accurate diagnoses. Despite a wealth of
available medical imaging data, high-quality labeled data for model training is
lacking. We investigated whether a gamified crowdsourcing platform enhanced
with inbuilt quality control metrics can produce lung ultrasound clip labels
comparable to those from clinical experts.
  Methods: 2,384 lung ultrasound clips were retrospectively collected from 203
patients. Six lung ultrasound experts classified 393 of these clips as having
no B-lines, one or more discrete B-lines, or confluent B-lines to create two
sets of reference standard labels (195 training set clips and 198 test set
clips). Sets were respectively used to A) train users on a gamified
crowdsourcing platform, and B) compare concordance of the resulting crowd
labels to the concordance of individual experts to reference standards.
  Results: 99,238 crowdsourced opinions on 2,384 lung ultrasound clips were
collected from 426 unique users over 8 days. On the 198 test set clips, mean
labeling concordance of individual experts relative to the reference standard
was 85.0% +/- 2.0 (SEM), compared to 87.9% crowdsourced label concordance
(p=0.15). When individual experts' opinions were compared to reference standard
labels created by majority vote excluding their own opinion, crowd concordance
was higher than the mean concordance of individual experts to reference
standards (87.4% vs. 80.8% +/- 1.6; p<0.001).
  Conclusion: Crowdsourced labels for B-line classification via a gamified
approach achieved expert-level quality. Scalable, high-quality labeling
approaches may facilitate training dataset creation for machine learning model
development.