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Abstract #2420

Swarm intelligence: a novel clinical strategy for improving imaging annotation accuracy, using wisdom of the crowds.

Rutwik Shah1, Bruno Astuto Arouche Nunes1, Tyler Gleason1, Justin Banaga1, Kevin Sweetwood1, Allen Ye1, Will Fletcher1, Rina Patel1, Kevin McGill1, Thomas Link1, Valentina Pedoia1, Sharmila Majumdar1, and Jason Crane1
1Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States

Radiologists play a central role in image annotation for training Machine Learning models. Key challenges in this regard include low inter-reader agreement for challenging cases and concerns of interpersonal bias amongst trainers. Inspired by biological swarm intelligence, we explored the use of real time consensus labeling by three sub-specialty (MSK) trained radiologists and five radiology residents in improving training data. A second swarm session with three residents was conducted to explore the effect of swarm size. These results were validated against clinical ground truth and also compared with results from a state-of-the-art AI model tested on the same dataset.

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