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Accurate and scalable annotation of medical data is critical for thedevelopment of medical AI, but obtaining time for annotation from medicalexperts is challenging. Gamified crowdsourcing has demonstrated potential forobtaining highly accurate annotations for medical data at scale, and wedemonstrate the same in this study for the segmentation of B-lines, anindicator of pulmonary congestion, on still frames within point-of-care lungultrasound clips. We collected 21,154 annotations from 214 annotators over 2.5days, and we demonstrated that the concordance of crowd consensus segmentationswith reference standards exceeds that of individual experts with the samereference standards, both in terms of B-line count (mean squared error 0.239vs. 0.308, p<0.05) as well as the spatial precision of B-line annotations (meanDice-H score 0.755 vs. 0.643, p<0.05). These results suggest thatexpert-quality segmentations can be achieved using gamified crowdsourcing.

Expert-Level Annotation Quality Achieved by Gamified Crowdsourcing for B-line Segmentation in Lung Ultrasound