The robotic-surgery propositional bank.

Robot-assisted minimally invasive surgery is the gold standard for the surgical treatment of many pathological conditions since it guarantees to the patient shorter hospital stay and quicker recovery. Several manuals and academic papers describe how to perform these interventions and thus contain important domain-specific knowledge. This information, if automatically extracted and processed, can be used to extract or summarize surgical practices or develop decision making systems that can help the surgeon or nurses to optimize the patient's management before, during, and after the surgery by providing theoretical-based suggestions.

Machine understanding surgical actions from intervention procedure textbooks.

The automatic extraction of procedural surgical knowledge from surgery manuals, academic papers or other high-quality textual resources, is of the utmost importance to develop knowledge-based clinical decision support systems, to automatically execute some procedure's step or to summarize the procedural information, spread throughout the texts, in a structured form usable as a study resource by medical students. In this work, we propose a first benchmark on extracting detailed surgical actions from available intervention procedure textbooks and papers. We frame the problem as a Semantic Role Labeling task.

Automatic detection of procedural knowledge in robotic-assisted surgical texts.

Purpose: The automatic extraction of knowledge about intervention execution from surgical manuals would be of the utmost importance to develop expert surgical systems and assistants. In this work we assess the feasibility of automatically identifying the sentences of a surgical intervention text containing procedural information, a subtask of the broader goal of extracting intervention workflows from surgical manuals.

Methods: We frame the problem as a binary classification task.