Evaluation of Laparoscopy Augmented Reality for Partial Nephrectomy

 

Lap Trainer Box

Lap Trainer Box

Key Points

To evaluate an AR system in a tumor surgical resection model, comparing the accuracy of the resection with and without the AR system. 90 growths were portioned: 28 were utilized to test the AR programming, 62 were utilized to analyze careful resection: 29 cancers were resected utilizing AR, and 33 without AR. Examination of our obsessive outcomes showed that altogether, there were 14 cancers that were totally missed or had a positive edge in the non-AR bunch.
For minimally invasive surgery technique, many of them have not received practical training with simulators resembling Lap Trainer Box.

Theoretical

Nowadays, bunches of careful intercession done by laparoscopy that permits a less forceful medical procedure. During laparoscopy, material criticism is restricted and limitation of growths inside an organ can be hard. The exact confinement of growths permits a moderate therapy in renal disease. Expanded Reality is an innovation that permits a specialist to see sub-surface designs in an endoscopic video, by overlaying preoperative MRI information.
Augmented Reality allows to perform real-time location of some lesion and anatomical landmarks, and so to assist the surgeon during the surgical procedure.

Design

Our AR strategy comprises of three stages. 
Stage 1: division. Utilizing the MRI pictures, the kidneys and pseudo-tumor surfaces portioned to develop a 3D model. 
Stage 2: the intra-employable 3D model of the not entirely settled. 
Stage 3: consolidation. The pre-employable and intra-usable models melded, and the laparoscopic view expanded.

Intervention 

Alginate infuse into the parenchyma to make pseudotumors estimating 4-10mm. The kidneys were then broke down by MRI. Kidneys then position into pelvic coaches, and the pseudotumors resected laparoscopically. Primary Outcome and Measure: Resection edges estimate minutely to assess the precision of resection.

Experimental Evaluation 

Along with clinical development in gynecology, we have initiated development of the system. For use in urology for partial nephrectomy in kidney cancer. Partial nephrectomy is the standard treatment for tumors smaller than 4 cm. The incidence of these small-volume tumors has greatly increased, in particular due to their accidental discovery during an imaging examination carried out for another reason. 
Partial nephrectomy can also performed for tumors larger than 4 cm if obtaining a healthy margin is technically possible. The objective of this surgery is the removal of the tumor associated with a healthy margin. Partial nephrectomy at a healthy margin achieves the same survival rate as radical nephrectomy, while preserving the nephrotic capital and therefore renal function as much as possible.

Scientific Approach 

One of the main difficulties of PN by laparoscopy or robotic surgery is the localization of the tumor within the parenchyma when it does not deform the surface of the kidney. The problem is therefore the same as for small or multiple fibroids. With the aim of a scientific approach, as for the use of AR for myomectomy, we have developed a kidney tumor model to test and develop our system. A porcine model of tumor creation used. 
It has demonstrated the reliability, reproducibility and advantages of AR in PN in this experimental model. The medical article corresponding to this work reproduce below. This article submitted in European Urology. The other two articles published in non-medical journals reproduced in appendix 3 and 4. The first relates to the development of the texture-based monitoring system that has improve following the overall development of the system and the second relates to the development of the system adapted to kidney tumors.

Results

90 cancers were sectioned: 28 were utilized to test the AR programming, 62 were utilized to haphazardly think about careful resection: 29 growths were resected utilizing AR, and 33 without AR. Examination of our neurotic outcomes 35 showed 4 disappointments in the AR gathering, and 10 in the Non-AR bunch. There was no finished miss in the AR bunch, while there were 4 finished misses in the non-AR bunch. Altogether, there 14 growths that were totally missed or had a positive edge in the non-AR bunch.

Conclusions 

Our AR framework works with exact limitation of tiny inward cancers. AR in laparoscopic medical procedure could hence improve the exactness of careful resection, in any event, for little cancers. Critical data like resection edges, and vascularization could similarly showed.
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