A mixed-reality surgical trainer with comprehensive sensing for fetal laser minimally invasive surgery

Allan Javaux, David Bouget, Caspar Gruijthuijsen, Danail Stoyanov, Tom Vercauteren, Sebastien Ourselin, Jan Deprest, Kathleen Denis, Emmanuel Vander Poorten

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)
191 Downloads (Pure)



Smaller incisions and reduced surgical trauma made minimally invasive surgery (MIS) grow in popularity even though long training is required to master the instrument manipulation constraints. While numerous training systems have been developed in the past, very few of them tackled fetal surgery and more specifically the treatment of twin-twin transfusion syndrome (TTTS). To address this lack of training resources, this paper presents a novel mixed-reality surgical trainer equipped with comprehensive sensing for TTTS procedures. The proposed trainer combines the benefits of box trainer technology and virtual reality systems. Face and content validation studies are presented and a use-case highlights the benefits of having embedded sensors.


Face and content validity of the developed setup was assessed by asking surgeons from the field of fetal MIS to accomplish specific tasks on the trainer. A small use-case investigates whether the trainer sensors are able to distinguish between an easy and difficult scenario.

The trainer was deemed sufficiently realistic and its proposed tasks relevant for practicing the required motor skills. The use-case demonstrated that the motion and force sensing capabilities of the trainer were able to analyze surgical skill.


The developed trainer for fetal laser surgery was validated by surgeons from a specialized center in fetal medicine. Further similar investigations in other centers are of interest, as well as quality improvements which will allow to increase the difficulty of the trainer. The comprehensive sensing appeared to be capable of objectively assessing skill.
Original languageEnglish
JournalInternational Journal of Computer Assisted Radiology and Surgery
Early online date27 Jul 2018
Publication statusE-pub ahead of print - 27 Jul 2018


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