Can Crowdsourced Annotations Improve AI-based Congestion Scoring For Bedside Lung Ultrasound?

Lung ultrasound (LUS) has become an indispensable tool at the bedside in emergency and acute care settings, offering a fast and non-invasive way to assess pulmonary congestion. Its portability and cost-effectiveness make it particularly valuable in resource-limited environments where quick decision-making is critical. Despite its advantages, the interpretation of B-line artifacts, which are key diagnostic indicators for conditions related to pulmonary congestion, can vary significantly among clinicians and even for the same clinician over time. This variability, coupled with the time pressure in acute settings, poses a challenge. To address this, our study introduces a new B-line segmentation method to calculate congestion scores from LUS images, aiming to standardize interpretations. We utilized a large dataset of 31,000 B-line annotations synthesized from over 550,000 crowdsourced opinions on LUS images of 299 patients to improve model training and accuracy. This approach has yielded a model with 94% accuracy in B-line counting (within a margin of 1) on a test set of 100 patients, demonstrating the potential of combining extensive data and crowdsourcing to refine lung ultrasound analysis for pulmonary congestion.