Temporal dynamics (α of DFA) of the torso in the AP axis.

<p>The figure illustrates the statistically significant 3-way interaction between Driving Experience, Time Windows, and Sickness Groups. (A) Well group. (B) Sick group.</p

Positional variability of the head in the AP axis.

<p>The figure illustrates the statistically significant 3-way interaction between Driving Experience, Time Windows, and Sickness Groups. (A) Well group. (B) Sick group.</p

Positional variability of the torso in the AP axis.

<p>The figure illustrates the statistically significant 3-way interaction between Driving Experience, Time Windows, and Sickness Groups. (A) Well group. (B) Sick group.</p

The driving game.

<p>(A) Overhead representation of the course (circuit). (B) Momentary driver’s-eye view.</p

Movement data, illustrating the statistically significant 2-way interactions between driving experience and Time Windows.

<p>(A) Positional variability of the head in the AP axis. (B) Positional variability of the head in the ML axis. (C) Positional variability of the torso in the AP Axis. (D) Positional variability of the torso in the ML axis. (E) Temporal dynamics (α of DFA) of the head in the AP axis. (F) Temporal dynamics (α of DFA) of the torso in the AP axis.</p

Movement data, illustrating the statistically significant 2-way interactions between Time Windows and Sickness Groups.

<p>(A) Positional variability of the head in the AP axis. (B) Positional variability of the head in the ML axis. (C) Positional variability of the torso in the AP axis. (D) Positional variability of the torso in the ML axis. (E) Temporal dynamics (α of DFA) of the torso in the AP axis.</p

Positional variability, illustrating the statistically significant 2-way interactions between driving experience and Sickness Groups.

<p>(A) The head in the AP axis. (B) The head in the ML axis. (C) The torso in the AP axis. (D) The torso in the ML axis.</p

Symptom severity (SSQ Total Severity Scores) for the well and sick groups.

<p>(A) Drivers. (B) Non-Drivers. Pre: Pre-exposure. Post: Post-exposure.</p

Assessing the Intraoperative Accuracy of Pedicle Screw Placement by Using a Bone-Mounted Miniature Robot System through Secondary Registration

<div><p>Introduction</p><p>Pedicle screws are commonly employed to restore spinal stability and correct deformities. The Renaissance robotic system was developed to improve the accuracy of pedicle screw placement.</p><p>Purpose</p><p>In this study, we developed an intraoperative classification system for evaluating the accuracy of pedicle screw placements through secondary registration. Furthermore, we evaluated the benefits of using the Renaissance robotic system in pedicle screw placement and postoperative evaluations. Finally, we examined the factors affecting the accuracy of pedicle screw implantation.</p><p>Results</p><p>Through use of the Renaissance robotic system, the accuracy of Kirschner-wire (K-wire) placements deviating <3 mm from the planned trajectory was determined to be 98.74%. According to our classification system, the robot-guided pedicle screw implantation attained an accuracy of 94.00% before repositioning and 98.74% after repositioning. However, the malposition rate before repositioning was 5.99%; among these placements, 4.73% were immediately repositioned using the robot system and 1.26% were manually repositioned after a failed robot repositioning attempt. Most K-wire entry points deviated caudally and laterally.</p><p>Conclusion</p><p>The Renaissance robotic system offers high accuracy in pedicle screw placement. Secondary registration improves the accuracy through increasing the precision of the positioning; moreover, intraoperative evaluation enables immediate repositioning. Furthermore, the K-wire tends to deviate caudally and laterally from the entry point because of skiving, which is characteristic of robot-assisted pedicle screw placement.</p></div

Assessing the Intraoperative Accuracy of Pedicle Screw Placement by Using a Bone-Mounted Miniature Robot System through Secondary Registration - Fig 2

<p><b>Classification system for assessing intraoperative accuracy</b> Fig 2A depicts type I classification, Fig 2B and 2C depict type II classifications, and Fig 2D depicts Type III calssification. (green line: planned trajectory; white line: implanted K-wire).</p