Value-assessment of computer-assisted navigation strategies during percutaneous needle placement

Abstract

Purpose Navigational strategies create a scenario whereby percutaneous needle-based interventions of the liver can be guided using both pre-interventional 3D imaging datasets and dynamic interventional ultrasound (US). To score how such technologies impact the needle placement process, we performed kinematic analysis on different user groups. Methods Using a custom biopsy phantom, three consecutive exercises were performed by both novices and experts (n = 26). The exercise came in three options: (1) US-guidance, (2) US-guidance with pre-interventional image-registration (US + Reg) and (3) US-guidance with pre-interventional image-registration and needle-navigation (US + Reg + Nav). The traveled paths of the needle were digitized in 3D. Using custom software algorithms, kinematic metrics were extracted and related to dexterity, decision making indices to obtain overall performance scores (PS). Results Kinematic analysis helped quantifying the visual assessment of the needle trajectories. Compared to US-guidance, novices yielded most improvements using Reg (PSavg(US) = 0.43 vs. PSavg(US+Reg) = 0.57 vs. PSavg(US+Reg+Nav) = 0.51). Interestingly, the expert group yielded a reversed trend (PSavg(US) = 0.71 vs PSavg(US+Reg) = 0.58 vs PSavg(US+Reg+Nav) = 0.59). Conclusion Digitizing the movement trajectory allowed us to objectively assess the impact of needle-navigation strategies on percutaneous procedures. In particular, our findings suggest that these advanced technologies have a positive impact on the kinematics derived performance of novices.

Artificial Intelligence‐Supported Video Analysis as a Means to Assess the Impact of DROP‐IN Image Guidance on Robotic Surgeons: Radioguided Sentinel Lymph Node versus PSMA‐Targeted Prostate Cancer Surgery

“…Registered coordinates of the DROP‐IN Probe tip allowed us to reconstruct its movement trajectory over time. In line with our previous works, [ 9,14b,16a,26 ] the digitized track allowed us to use custom algorithms (MATLAB, the MathWorks Inc., MA, USA) to extract kinematic metrics such as straightness index, angular dispersion, and adjusted speed. To eliminate the effect of camera location and zooming, the adjusted speed was considered as percentage of position change in each time point.…”

“…To score the surgical performance, we calculated the dexterity and decision‐making index as previously described [16a,19] . The overall dexterity (Dx) was represented by the normalized jerkiness over the time and traveled pathlength (Equation ()), wherein a low dexterity index indicated more smooth instrument movements.…”

“…A low index indicates a more focused procedure. The weight factors (i.e., , , and ) were determined as described in other studies [16a,19] …”

“…To this end, marker‐based video tracking of da Vinci instruments has been exploited in the form of patterned‐ [ 13 ] and fluorescent markers. [ 14 ] There is a growing body of evidence suggesting that such approaches can be used to monitor instrument use, [ 15 ] to compare the performance difference between experts and novices, [ 16 ] to perform scoring during virtual simulation training, [ 17 ] to generate freehand scans, [13a,18] and to assess how image guidance technologies effect performance [14b,16a,19] . To make video‐based instrument tracking accessible for a global audience of robotic surgeons, markerless detection algorithms are needed that can automatically extract the required data out of endoscopic videostreams.…”

Where Robotic Surgery Meets the Metaverse

“…A concept that has been successfully used to determine the difference in the use of medical technologies between novices and experts e.g. during robotic surgery [ 12 ] and liver punctions [ 13 ]. The findings obtained during these assessments can be further corroborated by relating them to complication rates and outcome data [ 14 ].…”