Using AI to enhance the performance of surgeons: decoding intraoperative surgical activity via accurate machine learning systems natBME Caltech USC surgery AI artificialintelligence machinelearning surgical
By Bhavana KunkalikarApr 3 2023Reviewed by Danielle Ellis, B.Sc. In a recent study published in Nature Biomedical Engineering, researchers proposed the utilization of a vision transformer model to decode surgeon activities from surgical videos.
About the study In the present study, researchers presented a machine learning system that utilizes a vision transformer as well as supervised contrastive learning to decode elements involved in intraoperative surgical activities from videos obtained during robotic surgeries. To gain a deeper understanding of the extent to which the constituent elements of SAIS played a role in its overall efficacy, the team conducted experiments involving modified versions of SAIS, wherein certain components were either removed or altered. The results of these experiments were then analyzed in terms of their positive predictive value with respect to the decoding of surgical subphases.
Results The study results indicated that SAIS exhibited consistent decoding of surgical subphases, with an area under the ROC curve of 0.925 for needle driving, 0.945 for needle handling, and 0.951 for needle withdrawal. It was noted that SAIS could proficiently decipher the advanced stages of surgical procedures, including but not limited to suturing and dissection. The study showed that SAIS demonstrated exceptional performance, with AUC values equal to or greater than 0.
The study also revealed that SAIS had a low likelihood of acquiring a specialized anatomical method for interpreting gestures and is resistant to the directional aspect of gesture motion. This observation is supported by the comparable performance of the deployed model on video samples featuring gestures executed in both the right and left neurovascular bundles.
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