# Augmented Reality: Mapping Methods and Tools for Enhancing the Human Role in Healthcare HMI
## 3 Results
### Intraoperative Solution
#### 3.3.2 Spine Surgery
In all cases, the AI software was successful in identifying landmarks and generating a safe trajectory, achieving an accuracy similar to that of standard fluoroscopic guidance. The cadaveric study by [PERSON] et al. [58] focused on determining the feasibility and the clinical accuracy of an ARSN for minimally invasive instrumentation of spinal and lumbar pedicle screws compared to the standard fluoroscopy-guided minimally invasive technique. The ARSN system consists of four optical cameras placed in the frame of a C-arm detector, and, for visual tracking, optical markers applied to the patient's skin. Minimally invasive screw placement with the ARSN system was shown to be as suitable and accurate as fluoroscopy, without requiring additional navigation time or X-ray imaging during the surgery. Additionally, the achieved clinical accuracy was comparable to values reported in current literature where different navigation systems for minimally invasive procedures were used [59, 63]. In a laboratory study on cadaveric animals, [PERSON] et al. [59] assessed the feasibility and accuracy of pedicle cannulation using automatic instrument tracking integrated into an ARSN system, to retrieve the instrument position with regard to deep anatomy. Cone-beam computed tomography (CBCT) was performed on two pig cadavers to generate a 3D model to plan the insertion of seventy-eight pedicle screws. CBCT was acquired after the insertion and the distance between the navigated device and the corresponding pre-planned path and angular deviations were measured. ARSN with instrument tracking for minimally invasive spine surgery achieved clinical accuracy from 97.4% to 100% depending on the screw size considered for placement, proving to be a feasible, radiation-free, and accurate navigation method. [PERSON] et al. [49] investigated the feasibility, efficacy, and accuracy of a novel AR tool for semi-automatic and minimally invasive pedicle screw placement with the addition of a robotic arm. A hybrid operating room was equipped with an ARSN system to plan screw paths and direct the customized robotic arm. Thanks to the robot guide, some Jamshidi needles were placed in 113 pedicles of four cadavers with 100% clinical accuracy, demonstrating how the positioning of the pedicle screws would greatly benefit from using a semi-automatic surgical robot, achieving much higher precision than that clinically acceptable. Some works describe AR-based systems in which the superimposition of virtual images was performed intraoperatively using a separate screen or a microscope. [PERSON] et al. [61] implemented support for degenerative spine surgery by applying an approach based on items available on the market, demonstrating that a microscope-based AR system is feasible. A HUD integrated into the operating microscope was used for AR visualization, showing AR data in the user's line of sight without the need for glasses. The operating microscope was calibrated by adjusting the superimposition of the 3D reference array representation on the actual reference array structure [60]. The automatic registration of AR was based on intraoperative CT, and its robustness was assessed by comparing the pointer tip position in relation to known structures, for instance, the retractor systems, or fiducials markers placed on the skin. Results indicate that this kind of application has enormous potential and could be useful in complex anatomical situations and the training of trainees. In another study [50], [PERSON] et al. also deepened whether microscope-based AR support can be applied in different spine surgery procedures for pathologies such as tumors and intradural lesions, deformities, infections, and degenerative cases. AR has proven its feasibility, especially in complex anatomical situations due to the patient positioning; indeed, in all cases of reinterventions, surgical navigation made the approach to the spine easier.