Three patient-specific 3D models were created using CT DICOM data. These 3D models were both converted to be used within a dV-trainer simulator environment as well as cast in 3D printed plaster model.
Medical students were recruited and randomized into two group:
- dV Trainer Group: allowed to view 3D model using the dV-trainer simulator then asked to choose the best 3D printed model that represented the 3D model viewed.
- No dV Trainer Group: allowed to view CT imaging only then asked choose the best 3D printed model that represented the 3D model viewed.
Dr. Arun Rai concluded that the use of the dV-Trainer improved renal tumor visuospatial recognition and the selection of closer renal mass compared to standard 2D imaging alone. He added that in this study, trainees who had access to the dV-trainer, on average were 50% closer to the correct renal mass. Lastly, he stated that all other factors such as age, 3D aptitude score, dV-Trainer score, ability to assign the correnct R.E.N.A.L nephrometry score, MS year, and desired future specialty did not associate with tumor localizing ability.
Presented by: Arun Rai, Baylor, MD, College of Medicine
Authors: Arun Rai, J Scovell, R Link
Affiliation: Baylor College of Medicine, United States
Written by: Renai Yoon, medical writer for UroToday at the 36th World Congress of Endourology (WCE) and SWL - September 20-23, 2018 Paris, France