Optimal Sub-Sequence Matching for the Automatic Prediction of Surgical Tasks
Surgery is one of the riskiest and most important medical acts that is performed today. The desires to improve patient outcomes, surgeon training, and also to reduce the costs of surgery, have motivated surgeons to equip their Operating Rooms with sensors that describe the surgical intervention. The richness and complexity of the data that is collected calls for new machine learning methods to support pre-, peri- and post-surgery (before, during and after).
This paper introduces a new method for the prediction of the next task that the surgeon is going to perform during the surgery (peri). Our method bases its prediction on the optimal matching of the current surgery to a set of pre-recorded surgeries.
We assess our method on a set of neurosurgeries (lumbar disc herniation removal) and show that our method outperforms the state of the art by providing a prediction (of the next task that is going to be performed by the surgeon) more than 85% of the time with a 95% accuracy.
KeywordsLumbar Disc Herniation Dynamic Time Warping Agreement Rate Surgical Task Surgical Activity
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