期刊
ARTIFICIAL INTELLIGENCE IN MEDICINE
卷 81, 期 -, 页码 3-11出版社
ELSEVIER
DOI: 10.1016/j.artmed.2017.03.007
关键词
Temporal analysis; Dynamic time warping; Surgical process modelling; Surgery
资金
- Australian Research Council [DE170100037]
- Air Force Office of Scientific Research, Asian Office of Aerospace Research and Development (AOARD) [FA2386-16-1-4023]
- Australian Research Council [DE170100037] Funding Source: Australian Research Council
Objective: More than half a million surgeries are performed every day worldwide, which makes surgery one of the most important component of global health care. In this context, the objective of this paper is to introduce a new method for the prediction of the possible next task that a surgeon is going to perform during surgery. Material and Method: We formulate the problem as finding the optimal registration of a partial sequence to a complete reference sequence of surgical activities. We propose an efficient algorithm to find the optimal partial alignment and a prediction system using maximum a posteriori probability estimation and filtering. We also introduce a weighting scheme allowing to improve the predictions by taking into account the relative similarity between the current surgery and a set of pre-recorded surgeries. Results: Our method is evaluated on two types of neurosurgical procedures: lumbar disc herniation removal and anterior cervical discectomy. Results show that our method outperformed the state of the art by predicting the next task that the surgeon will perform with 95% accuracy. Conclusions: This work shows that, even from the low-level description of surgeries and without other sources of information, it is often possible to predict the next surgical task when the conditions are consistent with the previously recorded surgeries. We also showed that our method is able to assess when there is actually a large divergence between the predictions and decide that it is not reasonable to make a prediction. (C) 2017 Elsevier B.V. All rights reserved.
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