• Guillaume Kugener, Yichao Zhu, Dhiraj J Pangal, Aditya Sinha, Nicholas Markarian, Arman Roshannai, Justin Chan, Animashree Anandkumar, Andrew J Hung, Bozena B Wrobel, Gabriel Zada, and Daniel A Donoho.
• Department of Neurosurgery, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA.
• Neurosurgery. 2022 Jun 1; 90 (6): 823829823-829.

BackgroundDeep neural networks (DNNs) have not been proven to detect blood loss (BL) or predict surgeon performance from video.ObjectiveTo train a DNN using video from cadaveric training exercises of surgeons controlling simulated internal carotid hemorrhage to predict clinically relevant outcomes.MethodsVideo was input as a series of images; deep learning networks were developed, which predicted BL and task success from images alone (automated model) and images plus human-labeled instrument annotations (semiautomated model). These models were compared against 2 reference models, which used average BL across all trials as its prediction (control 1) and a linear regression with time to hemostasis (a metric with known association with BL) as input (control 2). The root-mean-square error (RMSE) and correlation coefficients were used to compare the models; lower RMSE indicates superior performance.ResultsOne hundred forty-three trials were used (123 for training and 20 for testing). Deep learning models outperformed controls (control 1: RMSE 489 mL, control 2: RMSE 431 mL, R2 = 0.35) at BL prediction. The automated model predicted BL with an RMSE of 358 mL (R2 = 0.4) and correctly classified outcome in 85% of trials. The RMSE and classification performance of the semiautomated model improved to 260 mL and 90%, respectively.ConclusionBL and task outcome classification are important components of an automated assessment of surgical performance. DNNs can predict BL and outcome of hemorrhage control from video alone; their performance is improved with surgical instrument presence data. The generalizability of DNNs trained on hemorrhage control tasks should be investigated.Copyright © Congress of Neurological Surgeons 2022. All rights reserved.

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