Augmented Reality in Spine Surgery: A Narrative Review.

Augmented reality (AR) navigation refers to novel technologies that superimpose images, such as radiographs and navigation pathways, onto a view of the operative field. The development of AR navigation has focused on improving the safety and efficacy of neurosurgical and orthopedic procedures. In this review, the authors focus on 3 types of AR technology used in spine surgery: AR surgical navigation, microscope-mediated heads-up display, and AR head-mounted displays. Microscope AR and head-mounted displays offer the advantage of reducing attention shift and line-of-sight interruptions inherent in traditional navigation systems. With the U.S. Food and Drug Administration's recent clearance of the XVision AR system (Augmedics, Arlington Heights, IL), the adoption and refinement of AR technology by spine surgeons will only accelerate.

Automatic detection of cotton balls during brain surgery: Where deep learning meets ultrasound imaging to tackle foreign objects.

Cotton balls are a versatile and efficient tool commonly used in neurosurgical procedures to absorb fluids and manipulate delicate tissues. However, the use of cotton balls is accompanied by the risk of accidental retention in the brain after surgery. Retained cotton balls can lead to dangerous immune responses and potential complications, such as adhesions and textilomas. In a previous study, we showed that ultrasound can be safely used to detect cotton balls in the operating area due to the distinct acoustic properties of cotton compared with the acoustic properties of surrounding tissue. In this study, we enhance the experimental setup using a 3D-printed custom depth box and a Butterfly IQ handheld ultrasound probe. Cotton balls were placed in variety of positions to evaluate size and depth detectability limits. Recorded images were then analyzed using a novel algorithm that implements recently released YOLOv4, a state-of-the-art, real-time object recognition system. As per the radiologists' opinion, the algorithm was able to detect the cotton ball correctly 61% of the time, at approximately 32 FPS. The algorithm could accurately detect cotton balls up to 5mm in diameter, which corresponds to the size of surgical balls used by neurosurgeons, making the algorithm a promising candidate for regular intraoperative use.

Development of a Smart Hospital Assistant: Integrating Artificial Intelligence and a Voice-User Interface for Improved Surgical Outcomes.

Patient safety and efficiency are top priorities in any surgical procedure. One effective way to achieve these objectives is to automate the logistical and routine tasks that occur in the operating suite. Inspired by smart assistant technology already widely used in the consumer sector, we engineered the Smart Hospital Assistant (SHA), a smart, voice-controlled virtual assistant that handles natural speech recognition while executing non-surgical functions to aid any surgery. In simulated procedures, the SHA reduced operating time, optimized surgical staff resources, and reduced the number of major touch-points that can lead to surgical site infections. The SHA holds promise not only for use in the operating theater, but also in understaffed healthcare environments where automation can improve healthcare delivery.

Translucent Customized Cranial Implants Made of Clear Polymethylmethacrylate: An Early Outcome Analysis of 55 Consecutive Cranioplasty Cases.

BACKGROUND: Large skull reconstruction, with the use of customized cranial implants, restores cerebral protection, physiologic homeostasis, and one's preoperative appearance. Cranial implants may be composed of either bone or a myriad of alloplastic biomaterials. Recently, patient-specific cranial implants have been fabricated using clear polymethylmethacrylate (PMMA), a visually transparent and sonolucent variant of standard opaque PMMA. Given the new enhanced diagnostic and therapeutic applications of clear PMMA, we present here a study evaluating all outcomes and complications in a consecutive patient series. METHODS: A single-surgeon, retrospective, 3-year study was conducted on all consecutive patients undergoing large cranioplasty with clear PMMA implants (2016-2019). Patients who received clear PMMA implants with embedded neurotechnologies were excluded due to confounding variables. All outcomes were analyzed in detail and compared with previous studies utilizing similar alloplastic implant materials. RESULTS: Fifty-five patients underwent cranioplasty with customized clear PMMA implants. Twenty-one (38%) were performed using a single-stage cranioplasty method (ie, craniectomy and cranioplasty performed during the same operation utilizing a prefabricated, oversized design and labor-intense, manual modification), whereas the remaining 34 (62%) underwent a standard, 2-stage reconstruction (craniectomy with a delayed surgery for cranioplasty and minimal-to-no implant modification necessary). The mean cranial defect size was 101.8 cm. The mean follow-up time was 9 months (range, 1.5-39). Major complications requiring additional surgery occurred in 7 patients (13%) consisting of 2 (4%) cerebrospinal fluid leaks, 2 (4%) epidural hematomas, and 3 (4%) infections. In addition, 3 patients developed self-limiting or nonoperative complications including 2 (4%) with new onset seizures and 1 (2%) with delayed scalp healing. CONCLUSIONS: This is the first reported consecutive case series of cranioplasty reconstruction using customized clear PMMA implants, demonstrating excellent results with regard to ease of use, safety, and complication rates well below published rates when compared with other alloplastic materials. Clear PMMA also provides additional benefits, such as visual transparency and sonolucency, which is material specific and unavailable with autologous bone. Although these early results are promising, further studies with multicenter investigations are well justified to evaluate long-term outcomes.

Minimally invasive therapeutic ultrasound: Ultrasound-guided ultrasound ablation in neuro-oncology.

INTRODUCTION: To improve patient outcomes (eg, reducing blood loss and infection), practitioners have gravitated toward noninvasive and minimally invasive surgeries (MIS), which demand specialized toolkits. Focused ultrasound, for example, facilitates thermal ablation from a distance, thereby reducing injury to surrounding tissue. Focused ultrasound can often be performed noninvasively; however, it is more difficult to carry out in neuro-oncological tumors, as ultrasound is dramatically attenuated while propagating through the skull. This shortcoming has prompted exploration of MIS options for intracranial placement of focused ultrasound probes, such as within the BrainPath™ (NICO Corporation, Indianapolis, IN). Herein, we present the design, development, and in vitro testing of an image-guided, focused ultrasound prototype designed for use in MIS procedures. This probe can ablate neuro-oncological lesions despite its small size. MATERIALS & METHODS: Preliminary prototypes were iteratively designed, built, and tested. The final prototype consisted of three 8-mm-diameter therapeutic elements guided by an imaging probe. Probe functionality was validated on a series of tissue-mimicking phantoms. RESULTS: Lesions were created in tissue-mimicking phantoms with average dimensions of 2.5 × 1.2 × 6.5 mm and 3.4 × 3.25 × 9.36 mm after 10- and 30-second sonification, respectively. 30 s sonification with 118 W power at 50% duty cycle generated a peak temperature of 68 °C. Each ablation was visualized in real time by the built-in imaging probe. CONCLUSION: We developed and validated an ultrasound-guided focused ultrasound probe for use in MIS procedures. The dimensional constraints of the prototype were designed to reflect those of BrainPath trocars, which are MIS tools used to create atraumatic access to deep-seated brain pathologies.

Three-dimensional assessment of robot-assisted pedicle screw placement accuracy and instrumentation reliability based on a preplanned trajectory.

OBJECTIVE: Robotic spine surgery systems are increasingly used in the US market. As this technology gains traction, however, it is necessary to identify mechanisms that assess its effectiveness and allow for its continued improvement. One such mechanism is the development of a new 3D grading system that can serve as the foundation for error-based learning in robot systems. Herein the authors attempted 1) to define a system of providing accuracy data along all three pedicle screw placement axes, that is, cephalocaudal, mediolateral, and screw long axes; and 2) to use the grading system to evaluate the mean accuracy of thoracolumbar pedicle screws placed using a single commercially available robotic system. METHODS: The authors retrospectively reviewed a prospectively maintained, IRB-approved database of patients at a single tertiary care center who had undergone instrumented fusion of the thoracic or lumbosacral spine using robotic assistance. Patients with preoperatively planned screw trajectories and postoperative CT studies were included in the final analysis. Screw accuracy was measured as the net deviation of the planned trajectory from the actual screw trajectory in the mediolateral, cephalocaudal, and screw long axes. RESULTS: The authors identified 47 patients, 51% male, whose pedicles had been instrumented with a total of 254 screws (63 thoracic, 191 lumbosacral). The patients had a mean age of 61.1 years and a mean BMI of 30.0 kg/m2. The mean screw tip accuracies were 1.3 ± 1.3 mm, 1.2 ± 1.1 mm, and 2.6 ± 2.2 mm in the mediolateral, cephalocaudal, and screw long axes, respectively, for a net linear deviation of 3.6 ± 2.3 mm and net angular deviation of 3.6° ± 2.8°. According to the Gertzbein-Robbins grading system, 184 screws (72%) were classified as grade A and 70 screws (28%) as grade B. Placement of 100% of the screws was clinically acceptable. CONCLUSIONS: The accuracy of the discussed robotic spine system is similar to that described for other surgical systems. Additionally, the authors outline a new method of grading screw placement accuracy that measures deviation in all three relevant axes. This grading system could provide the error signal necessary for unsupervised machine learning by robotic systems, which would in turn support continued improvement in instrumentation placement accuracy.