Automatic matching of surgeries to predict surgeons’ next actions

Germain Forestier, François Petitjean, Laurent Riffaud, Pierre Jannin

Research output: Contribution to journalArticleResearchpeer-review

7 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)3-11
Number of pages9
JournalArtificial Intelligence in Medicine
Volume81
DOIs
Publication statusPublished - 1 Sep 2017

Keywords

  • Dynamic time warping
  • Surgery
  • Surgical process modelling
  • Temporal analysis

Cite this

Forestier, Germain ; Petitjean, François ; Riffaud, Laurent ; Jannin, Pierre. / Automatic matching of surgeries to predict surgeons’ next actions. In: Artificial Intelligence in Medicine. 2017 ; Vol. 81. pp. 3-11.
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abstract = "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.",
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Automatic matching of surgeries to predict surgeons’ next actions. / Forestier, Germain; Petitjean, François; Riffaud, Laurent; Jannin, Pierre.

In: Artificial Intelligence in Medicine, Vol. 81, 01.09.2017, p. 3-11.

Research output: Contribution to journalArticleResearchpeer-review

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