Augmented and virtual reality in spine surgery, current applications and future potentials

BACKGROUND CONTEXT: The field of artificial intelligence (AI) is rapidly advancing, especially with recent improvements in deep learning (DL) techniques. Augmented (AR) and virtual reality (VR) are finding their place in healthcare, and spine surgery is no exception. The unique capabilities and advantages of AR and VR devices include their low cost, flexible integration with other technologies, user-friendly features and their application in navigation systems, which makes them beneficial across different aspects of spine surgery. Despite the use of AR for pedicle screw placement, targeted cervical foraminotomy, bone biopsy, osteotomy planning, and percutaneous intervention, the current applications of AR and VR in spine surgery remain limited. PURPOSE: The primary goal of this study was to provide the spine surgeons and clinical researchers with the general information about the current applications, future potentials, and accessibility of AR and VR systems in spine surgery. STUDY DESIGN/SETTING: We reviewed titles of more than 250 journal papers from google scholar and PubMed with search words: augmented reality, virtual reality, spine surgery, and orthopaedic, out of which 89 related papers were selected for abstract review. Finally, full text of 67 papers were analyzed and reviewed. METHODS: The papers were divided into four groups: technological papers, applications in surgery, applications in spine education and training, and general application in orthopaedic. A team of two reviewers performed paper reviews and a thorough web search to ensure the most updated state of the art in each of four group is captured in the review. RESULTS: In this review we discuss the current state of the art in AR and VR hardware, their preoperative applications and surgical applications in spine surgery. Finally, we discuss the future potentials of AR and VR and their integration with AI, robotic surgery, gaming, and wearables. CONCLUSIONS: AR and VR are promising technologies that will soon become part of standard of care in spine surgery. (C) 2021 Published by Elsevier Inc

Collision Measurements Using Digital Image Correlation Techniques

In this study, the digital image correlation (DIC) techniques have been used to analyze the motion during the collisions. The spline interpolation along with two dimensional Fast Fourier Transform (FFT) cross correlation has been used in order to increase the accuracy and decrease the computation time of the method respectively. Three different examples have been analyzed: normal impact of a metal rod with a 3D printed polymer flat, oblique impact of a tennis ball with a tennis racket, and oblique impact of a lacrosse ball with a wooden flat. A speckle pattern study has been done to find the optimum pattern for the DIC technique. For the normal impact of the rod, the velocity during the impact have been measured. The normal velocity has been found by the DIC technique. For the oblique impact of the balls, the linear and angular motion have been calculated during the impact. The velocity field on the ball surface has been measured using the DIC technique. The Hough transform method has been used in combination with the measured velocity field to find the velocity of the centroid of the balls. The angular velocity during the impact has been found using the velocity field of the surface of the ball. It has been shown that the DIC technique can be used to measure the motion of colliding objects

Experimental and Theoretical Modeling of Behavior of 3D-Printed Polymers Under Collision With a Rigid Rod

The behavior of five different 3D-printed polymers has been analyzed both theoretically and experimentally under low-speed collision conditions. The impact of a rigid rod with a flat specimen fabricated of 3D-printed materials was analyzed. An experimental setup has been designed in order to capture the motion of the rod during the impact using a high-speed camera. Image processing algorithms were developed to estimate the velocity before and after the impact as well as the coefficient of restitution. Also, permanent deformations after the impact were scanned with an optical profilometer. In this work, a theoretical formulation for the contact force during the impact is proposed. The impact was divided into two phases, compression and restitution, in which materials considered elastic–plastic in the first and fully elastic in the second one. The experimental results are used to measure the damping coefficient. Results show a good correlation between the proposed formulation for the contact force and the behavior of materials

Experimental and Theoretical Modeling of Behavior of 3D-Printed Polymers Under Collision With a Rigid Rod

The behavior of five different 3D-printed polymers has been analyzed both theoretically and experimentally under low-speed collision conditions. The impact of a rigid rod with a flat specimen fabricated of 3D-printed materials was analyzed. An experimental setup has been designed in order to capture the motion of the rod during the impact using a high-speed camera. Image processing algorithms were developed to estimate the velocity before and after the impact as well as the coefficient of restitution. Also, permanent deformations after the impact were scanned with an optical profilometer. In this work, a theoretical formulation for the contact force during the impact is proposed. The impact was divided into two phases, compression and restitution, in which materials considered elastic–plastic in the first and fully elastic in the second one. The experimental results are used to measure the damping coefficient. Results show a good correlation between the proposed formulation for the contact force and the behavior of materials

Understanding and Engineering of Natural Surfaces with Additive Manufacturing 3D-Printed Biosurfaces

Benthic algae systems that attach to substrata have been shown effective in water pollution remediation and biomass production, but yields are limited by attachment preferences in wild cultivars. This work seeks to uncover the surface topography preferences for algal attachment by reproducing natural surface topographies using additive manufacturing. To date, no other research efforts have taken advantage of using additive manufacturing to reverse engineer the characteristics of natural surfaces for enhancement of the attachment preferences of certain periphyton species towards substrata topography. Natural rocks and surfaces with attached biofilms were retrieved from streams, scanned with optical profilometry, and the surface characteristics were analyzed. A material jetting process is used to additively manufacture the surfaces, followed by optical profilometry to validate the resultant topography. The results show that certain texture parameters (e.g., Smr, Sa, and Sv) are significant in affecting the biomass adhesion of specific algal communities

3D Printed Ultrasound Cuff with Machine Learning Algorithms for the Detection of Knee Implant Loosening (tbd)

Millions of patients worldwide undergo joint replacements each year, and in recent years, these procedures are being implemented in increasingly younger patients. However, based on the ages and general health of the patients, recipients of these replacements can outlive their original implant; thus, creating the need for revision surgeries. Revision surgery and recovery are painful and inconvenient for the patient. One of the main causes of revision surgery is implant loosening, which occurs when there is a space along the interface of the implant and the bone. Current methods to detect knee implant loosening include radiography, CT scans, and MRI imaging, which are costly and cannot detect early onset problems. Additionally, the diagnosis is solely based on the discretion of the clinician who is overlooking the imaging. We have proposed and built a prototype for a wearable cuff device that uses ultrasound signals to detect implant loosening of knee replacements. Our cuff can detect loosening at earlier stages at a lower cost than these current methods. Our ultrasound sensors, which are built into our 3D printed wearable, send and receive sinusoidal signals that can then be run through our predictive machine learning algorithms. The algorithm results help doctors identify different types of loosening of varying degrees of severity and locations around the implant. We successfully designed and 3D printed a flexible, wearable cuff that meets all of our engineering criteria. Additionally, we tested the feasibility of sending and receiving signals from the sensors within our cuff. We simulated the knee cross-section with and without knee implant defects with eight piezoelectric sensors applied symmetrically on the skin. The machine learning algorithms that we trained with our simulation data can sense the presence of a defect, and if there is a defect, identify details about location, size and shape of the loosening. In future experiments, our cuff will send and receive signals from knee implant-introduced cadaver knees with and without induced defects. We will run these data sets through our machine learning algorithms and test the entire product as a whole. Our cuff can be resized and our algorithms can be retrained on different joint interfaces in order to be applied to different joint replacement types (such as hip, spine, shoulder, etc).NAhttp://deepblue.lib.umich.edu/bitstream/2027.42/176707/1/Capstone_Final_Report_-_Elizabeth_Hughes.docxhttp://deepblue.lib.umich.edu/bitstream/2027.42/176707/2/Honors_Capstone_Ultrasound_Poster_-_Elizabeth_Hughes.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/176707/3/BMES_Ultrasound_Cuff_Presentation_-_Elizabeth_Hughes.ppt

Understanding and Engineering of Natural Surfaces with Additive Manufacturing 3D-Printed Biosurfaces

Benthic algae systems that attach to substrata have been shown effective in water pollution remediation and biomass production, but yields are limited by attachment preferences in wild cultivars. This work seeks to uncover the surface topography preferences for algal attachment by reproducing natural surface topographies using additive manufacturing. To date, no other research efforts have taken advantage of using additive manufacturing to reverse engineer the characteristics of natural surfaces for enhancement of the attachment preferences of certain periphyton species towards substrata topography. Natural rocks and surfaces with attached biofilms were retrieved from streams, scanned with optical profilometry, and the surface characteristics were analyzed. A material jetting process is used to additively manufacture the surfaces, followed by optical profilometry to validate the resultant topography. The results show that certain texture parameters (e.g., Smr, Sa, and Sv) are significant in affecting the biomass adhesion of specific algal communities

Understanding and Engineering of Natural Surfaces With Additive Manufacturing

Benthic algae systems that attach to substrata have been shown effective in water pollution remediation and biomass production, but yields are limited by attachment preferences in wild cultivars. This work seeks to uncover the surface topography preferences for algal attachment by reproducing natural surface topographies using additive manufacturing. To date, no other research efforts have taken advantage of using additive manufacturing to reverse engineer the characteristics of natural surfaces for enhancement of the attachment preferences of certain periphyton species towards substrata topography. Natural rocks and surfaces with attached biofilms were retrieved from streams, scanned with optical profilometry, and the surface characteristics were analyzed. A material jetting process is used to additively manufacture the surfaces, followed by optical profilometry to validate the resultant topography. The results show that certain texture parameters (e.g., Smr, Sa, and Sv) are significant in affecting the biomass adhesion of specific algal communities