Germain Forestier1, François Petitjean2, Laurent Riffaud3, Pierre Jannin4. 1. MIPS, University of Haute-Alsace, Mulhouse, France; Faculty of Information Technology, Monash University, Melbourne, Australia. Electronic address: germain.forestier@uha.fr. 2. Faculty of Information Technology, Monash University, Melbourne, Australia. Electronic address: francois.petitjean@monash.edu. 3. INSERM MediCIS, Unit U1099 LTSI, University of Rennes 1, Rennes, France; Department of Neurosurgery, Pontchaillou University Hospital, Rennes, France. Electronic address: laurent.riffaud@chu-rennes.fr. 4. INSERM MediCIS, Unit U1099 LTSI, University of Rennes 1, Rennes, France. Electronic address: pierre.jannin@univ-rennes1.fr.
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.
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.
Authors: Jani Koskinen; Antti Huotarinen; Antti-Pekka Elomaa; Bin Zheng; Roman Bednarik Journal: Int J Comput Assist Radiol Surg Date: 2021-12-15 Impact factor: 2.924
Authors: Fabio Carrillo; Hooman Esfandiari; Sandro Müller; Marco von Atzigen; Aidana Massalimova; Daniel Suter; Christoph J Laux; José M Spirig; Mazda Farshad; Philipp Fürnstahl Journal: Front Surg Date: 2022-01-25