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Accuracy and Precision of a Surgical Navigation System: Effect of Camera and Patient Tracker Position and Number of Active Markers.

ABSTRACT: Surgical navigation systems are increasingly used to aid resection and reconstruction of osseous malignancies. In the process of implementing image-based surgical navigation systems, there are numerous opportunities for error that may impact surgical outcome. This study aimed to examine modifiable sources of error in an idealized scenario, when using a bidirectional infrared surgical navigation system.Accuracy and precision were assessed using a computerized-numerical-controlled (CNC) machined grid with known distances between indentations while varying: 1) the distance from the grid to the navigation camera (range 150 to 247cm), 2) the distance from the grid to the patient tracker device (range 20 to 40cm), and 3) whether the minimum or maximum number of bidirectional infrared markers were actively functioning. For each scenario, distances between grid points were measured at 10-mm increments between 10 and 120mm, with twelve measurements made at each distance. The accuracy outcome was the root mean square (RMS) error between the navigation system distance and the actual grid distance. To assess precision, four indentations were recorded six times for each scenario while also varying the angle of the navigation system pointer. The outcome for precision testing was the standard deviation of the distance between each measured point to the mean three-dimensional coordinate of the six points for each cluster.Univariate and multiple linear regression revealed that as the distance from the navigation camera to the grid increased, the RMS error increased (p<0.001). The RMS error also increased when not all infrared markers were actively tracking (p=0.03), and as the measured distance increased (p<0.001). In a multivariate model, these factors accounted for 58% of the overall variance in the RMS error. Standard deviations in repeated measures also increased when not all infrared markers were active (p<0.001), and as the distance between navigation camera and physical space increased (p=0.005). Location of the patient tracker did not affect accuracy (0.36) or precision (p=0.97).In our model laboratory test environment, the infrared bidirectional navigation system was more accurate and precise when the distance from the navigation camera to the physical (working) space was minimized and all bidirectional markers were active. These findings may require alterations in operating room setup and software changes to improve the performance of this system.

SUBMITTER: Gundle KR

PROVIDER: S-EPMC5481622 | biostudies-other | 2017

REPOSITORIES: biostudies-other

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Project description:Inexperienced operating assistants are often tasked with the important role of handling camera navigation during laparoscopic surgery. Incorrect handling can lead to poor visualization, increased operating time, and frustration for the operating surgeon-all of which can compromise patient safety. The objectives of this trial were to examine how to train laparoscopic camera navigation and to explore the transfer of skills to the operating room.A randomized, single-center superiority trial with three groups: The first group practiced simulation-based camera navigation tasks (camera group), the second group practiced performing a simulation-based cholecystectomy (procedure group), and the third group received no training (control group). Participants were surgical novices without prior laparoscopic experience. The primary outcome was assessment of camera navigation skills during a laparoscopic cholecystectomy. The secondary outcome was technical skills after training, using a previously developed model for testing camera navigational skills. The exploratory outcome measured participants' motivation toward the task as an operating assistant.Thirty-six participants were randomized. No significant difference was found in the primary outcome between the three groups (p = 0.279). The secondary outcome showed no significant difference between the interventions groups, total time 167 s (95% CI, 118-217) and 194 s (95% CI, 152-236) for the camera group and the procedure group, respectively (p = 0.369). Both interventions groups were significantly faster than the control group, 307 s (95% CI, 202-412), p = 0.018 and p = 0.045, respectively. On the exploratory outcome, the control group for two dimensions, interest/enjoyment (p = 0.030) and perceived choice (p = 0.033), had a higher score.Simulation-based training improves the technical skills required for camera navigation, regardless of practicing camera navigation or the procedure itself. Transfer to the clinical setting could, however, not be demonstrated. The control group demonstrated higher interest/enjoyment and perceived choice than the camera group.

Project description:Background and objectivesOblique lumbar interbody fusion (OLIF) procedures involve anterior insertion of interbody cage in lateral position. Following OLIF, insertion of pedicle screws and rod system is performed in a prone position (OLIF-con). The location of the cage is important for restoration of lumbar lordosis and indirect decompression. However, inserting the cage at the desired location is difficult without reduction of spondylolisthesis, and reduction after insertion of interbody cage may limit the amount of reduction. Recent introduction of spinal navigation enabled both surgical procedures in one lateral position (OLIF-one). Therefore, reduction of spondylolisthesis can be performed prior to insertion of interbody cage. The objective of this study was to compare the reduction of spondylolisthesis and the placement of cage between OLIF-one and OLIF-con.MethodsWe retrospectively reviewed 72 consecutive patients with spondylolisthesis for this study; 30 patients underwent OLIF-one and 42 underwent OLIF-con. Spinal navigation system was used for OLIF-one. In OLIF-one, the interbody cage was inserted after reducing spondylolisthesis, whereas in OLIF-con, the cage was inserted before reduction. The following parameters were measured on X-rays: pre- and postoperative spondylolisthesis slippage, reduction degree, and the location of the cage in the disc space.ResultsBoth groups showed significant improvement in back and leg pains (p < .05). Transient motor or sensory changes occurred in three patients after OLIF-con and in two patients after OLIF-one. Pre- and postoperative slips were 26.3±7.7% and 6.6±6.2% in OLIF-one, and 23.1±7.0% and 7.4±5.8% in OLIF-con. The reduction of slippage was 74.4±6.3% after OLIF-one and 65.4±5.7% after OLIF-con, with a significant difference between the two groups (p = .04). The cage was located at 34.2±8.9% after OLIF-one and at 42.8±10.3% after OLIF-con, with a significant difference between the two groups (p = .004).ConclusionSwitching the sequence of surgical procedures with OLIF-one facilitated both the reduction of spondylolisthesis and the placement of the cage at the desired location.

Project description:Scientists are increasingly using volunteer efforts of citizen scientists to classify images captured by motion-activated trail cameras. The rising popularity of citizen science reflects its potential to engage the public in conservation science and accelerate processing of the large volume of images generated by trail cameras. While image classification accuracy by citizen scientists can vary across species, the influence of other factors on accuracy is poorly understood. Inaccuracy diminishes the value of citizen science derived data and prompts the need for specific best-practice protocols to decrease error. We compare the accuracy between three programs that use crowdsourced citizen scientists to process images online: Snapshot Serengeti, Wildwatch Kenya, and AmazonCam Tambopata. We hypothesized that habitat type and camera settings would influence accuracy. To evaluate these factors, each photograph was circulated to multiple volunteers. All volunteer classifications were aggregated to a single best answer for each photograph using a plurality algorithm. Subsequently, a subset of these images underwent expert review and were compared to the citizen scientist results. Classification errors were categorized by the nature of the error (e.g., false species or false empty), and reason for the false classification (e.g., misidentification). Our results show that Snapshot Serengeti had the highest accuracy (97.9%), followed by AmazonCam Tambopata (93.5%), then Wildwatch Kenya (83.4%). Error type was influenced by habitat, with false empty images more prevalent in open-grassy habitat (27%) compared to woodlands (10%). For medium to large animal surveys across all habitat types, our results suggest that to significantly improve accuracy in crowdsourced projects, researchers should use a trail camera set up protocol with a burst of three consecutive photographs, a short field of view, and determine camera sensitivity settings based on in situ testing. Accuracy level comparisons such as this study can improve reliability of future citizen science projects, and subsequently encourage the increased use of such data.

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Accuracy and Precision of a Surgical Navigation System: Effect of Camera and Patient Tracker Position and Number of Active Markers.

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