Eric C. Larson
Papers
2
Total Citations
20
H-Index
2
About
Eric C. Larson is a researcher at the intersection of machine learning and surgical robotics, with a primary focus on automated surgical skill assessment. His work leverages deep learning—particularly convolutional and multi-task attention networks—to evaluate surgeon performance from video recordings of robotic-assisted procedures. Larson’s most-cited paper, "Evaluating robotic-assisted surgery training videos with multi-task convolutional neural networks" (2021, 16 citations), pioneered the use of neural networks to assess surgical skill from training videos, moving beyond synthetic tissue models. He extended this approach in a 2023 study (4 citations) that applied fully convolutional segmentation and multi-task attention networks specifically to robotic-assisted partial nephrectomies, analyzing tumor resection and renography steps in actual surgeries. This work represents a significant step toward objective, scalable surgical evaluation, with potential applications in training, credentialing, and quality improvement. Larson’s contributions are particularly notable for bridging computer vision and clinical practice, offering a data-driven pathway to enhance surgical education and patient outcomes.
Research Focus
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Top Papers
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