You are here: indexactivitiessetsmsq

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Next revision
Previous revision
activities:set:smsq [2021/01/14 17:40]
ahuaulme created
activities:set:smsq [2021/01/14 17:42] (current)
ahuaulme
Line 19: Line 19:
 For this project, we focused on rectopexy, an abdominal surgery. Rectopexy is performed to correct anal prolapse by fixing the rectum to the promontory through meshes [4]. Firstly, we modeling the "​typical"​ rectopexy and the deviation from this "​ideal"​ surgery. For this, we chose to use cognitive task analysis with interview of surgeon and observation in the operating room and based on laparoscopic videos. With this work, we were able to describe the "​typical"​ rectopexy with medical vocabulary on different levels of granularity. \\  For this project, we focused on rectopexy, an abdominal surgery. Rectopexy is performed to correct anal prolapse by fixing the rectum to the promontory through meshes [4]. Firstly, we modeling the "​typical"​ rectopexy and the deviation from this "​ideal"​ surgery. For this, we chose to use cognitive task analysis with interview of surgeon and observation in the operating room and based on laparoscopic videos. With this work, we were able to describe the "​typical"​ rectopexy with medical vocabulary on different levels of granularity. \\ 
  
-Secondly, we tried to detect deviations in a surgical process model and to distinguish the ones due to patient'​s anatomical particularities from the ones due to intra-operative events. In order to accomplish this, we use a combination of dynamic time warping and a hidden Markov model methods. Finally, we tried to identify patterns to explain and predict the appearance of intra-operative adverse events. ​+{{  :​activities:​theme1:​ah_detect_deviation.png?​300|}} 
 +Secondly, we tried to detect deviations in a surgical process model and to distinguish the ones due to patient'​s anatomical particularities from the ones due to intra-operative events. In order to accomplish this, we use a combination of dynamic time warping and a hidden Markov model methods. Finally, we tried to identify patterns to explain and predict the appearance of intra-operative adverse events. ​\\  
 + 
 +\\  
 + 
 +\\  
  
 ====== Publications ====== ====== Publications ======
inserm rennes1 ltsi