Automatic matching of surgeries to predict surgeons' next actions.
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.


METHODS
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.
MIPS, University of Haute-Alsace, Mulhouse, France; Faculty of Information Technology, Monash University, Melbourne, Australia. Electronic address: germain.forestier@uha.fr.
Germain
G
Forestier
Faculty of Information Technology, Monash University, Melbourne, Australia. Electronic address: francois.petitjean@monash.edu.
François
F
Petitjean
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.
Laurent
L
Riffaud
INSERM MediCIS, Unit U1099 LTSI, University of Rennes 1, Rennes, France. Electronic address: pierre.jannin@univ-rennes1.fr.
Pierre
P
Jannin
D001185:Artificial Intelligence; D001331:Automation; D002574:Cervical Vertebrae / Q000601:surgery*; D017586:Diskectomy; D005260:Female; D006801:Humans; D007405:Intervertebral Disc Displacement / Q000601:surgery*; D008159:Lumbar Vertebrae / Q000601:surgery*; D008297:Male; D008875:Middle Aged; D009043:Motor Activity; D009048:Motor Skills; D009870:Operating Room Information Systems; D010348:Patient Care Team; D066231:Surgeons; D013647:Task Performance and Analysis; D013997:Time Factors
Dynamic time warping; Surgery; Surgical process modelling; Temporal analysis