Finding discriminative and interpretable patterns in sequences of surgical activities

Publication date: Available online 22 September 2017
Source:Artificial Intelligence in Medicine
Author(s): Germain Forestier, François Petitjean, Pavel Senin, Laurent Riffaud, Pierre-Louis Henaux, Pierre Jannin
ObjectiveSurgery 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 to understand and analyze surgical behaviors. This article addresses the issue of identifying discriminative patterns of surgical practice from recordings of surgeries. These recordings are sequences of low-level surgical activities representing the actions performed by surgeons during surgeries.Materials and methodTo discover patterns that are specific to a group of surgeries, we use the vector space model (VSM) which is originally an algebraic model for representing text documents. We split long sequences of surgical activities into subsequences of consecutive activities. We then compute the relative frequencies of these subsequences using the tf*idf framework and we use the Cosine similarity to classify the sequences. This process makes it possible to discover which patterns discriminate one set of surgeries recordings from another set.ResultsExperiments were performed on 40 neurosurgeries of anterior cervical discectomy (ACD). The results demonstrate that our method accurately identifies patterns that can discriminate between (1) locations where the surgery took place, (2) levels of expertise of surgeons (i.e., expert vs. intermediate) and even (3) individual surgeons who performed the intervention. We also show how the tf*idf weight vector can be used to both visualize the most interesting patterns and to highlight the parts of a given surgery that are the most interesting.ConclusionsIdentifying patterns that discriminate groups of surgeon is a very important step in improving the understanding of surgical processes. The proposed method finds discriminative and interpretable patterns in sequences of surgical activities. Our approach provides intuitive results, as it identifies automatically the set of patterns explaining the differences between the groups.

Graphical abstract

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