The goal of this thesis is to reduce the clinical risk in surgical procedures by implementing an embedded artificial intelligence (EAI) technique for helping the surgeon in prognostics by assessing the quality of the outcome of the surgical procedure.
As a case study, in this work, we considered the AI-based automatic evaluation of the quality of the suture of the gastrointestinal tract, based on the vascularization analysis applied to abdominal laparoscopic surgery. The system has been already tested during a surgical procedure: results showed that the developed algorithm successfully identifies, in real-time, well- and low-vascularized tracts.
Further research is dedicated to implementing different thresholds levels to quantify the success of the procedure.