The goal of BESAFE was to evaluate whether automated identification of insufficient training of surgeons and the surgical team could help in preventing surgical errors and whether such a technology could become a commercial product. By observing the patterns of use of an intra-operative touch screen display, we anticipated that IBSEN’s algorithms could group individuals according their technical proficiency and level of stress and raise an alarm prior to irreversible nerve damage.


  • Firstly, a fundamental test was run in which naive users would be “observed” by machine learning code while performing a mock-up surgery. The system successfully classified users amongst well trained and relaxed, or poorly trained and stressed, and confirmed a correlation between the outcome of the dummy surgery and the risk score provided by the machine learning software. A video clip of software in action can be accessed here: http://www.afferent-technologies.com/software_clip.mp4
  • Secondly, a business plan, informed by the results of the first task, was prepared outlining both the technical strategy to reach a final product and exploitation plan, with emphasis on the financials that will make the project possible from R&D to commercialisation. It was concluded overall that the project can be technically and financially successful and it is an investable opportunity for institutional investors.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N. 847378.

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