Non-Linear Temporal Scaling of Surgical Processes

Abstract : Objective : Surgery is one of the riskiest and most important medical acts that is performed today. Understanding the ways in which surgeries are similar or different from each other is of major interest. Desires to improve patient outcomes and surgeon training, and to reduce the costs of surgery, all motivate a better understanding of surgical practices. To facilitate this, surgeons have started recording the activities that are performed during surgery. New methods have to be developed to be able to make the most of this extremely rich and complex data. The objective of this work is to enable the simultaneous comparison of a set of surgeries, in order to be able to extract high-level information about surgical practices. Material and Method : We introduce non-linear temporal scaling (NLTS): a method that finds a multiple alignment of a set of surgeries. Experiments are carried out on a set of lumbar disc neurosurgeries. We assess our method both on a highly standardised phase of the surgery (closure) and on the whole surgery.Results : Experiments show that NLTS makes it possible to consistently derive standards of surgical practice and to understand differences between groups of surgeries. We take the training of surgeons as the common theme for the evaluation of the results and highlight, for example, the main differences between the practices of junior and senior surgeons in the removal of a lumbar disc herniation. Conclusions : NLTS is an effective and efficient method to find a multiple alignment of a set of surgeries. NLTS realigns a set of sequences along their intrinsic timeline, which makes it possible to extract standards of surgical practices.
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Germain Forestier, Francois Petitjean, Laurent Riffaud, Pierre Jannin. Non-Linear Temporal Scaling of Surgical Processes. Artificial Intelligence in Medicine, Elsevier, 2014, 62 (3), pp.143-152. 〈http://www.sciencedirect.com/science/article/pii/S0933365714001225〉. 〈10.1016/j.artmed.2014.10.007〉. 〈hal-00862978v2〉

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