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Cognitive Load (CL) can affect surgeon performance and the ability of training surgeons to receive feedback. We investigate the value of CL for detecting surgical errors in robot-assisted surgery (RAS) with a machine learning (ML)-based model.

We use 2 datasets (DS1, DS2) where 24 and 44 surgeons, respectively, performed a simulated dry-lab tissue dissection task on a live daVinci Xi surgical robot. The data contains video and per-second Index of Cognitive Activity (ICA), derived from pupillary change tracked via Tobii Eyetrackers. DS1 includes surgeons with low, medium, and high experience (by hours of RAS) as well as annotated tissue Tear (n=70) and Puncture (n=62) errors. DS2 contains data from low experience surgeons only and annotations for Tears (n=445) and Punctures (n=527). We extract 5 sec of video and varying history of ICA (from 5 to 60 sec …