Name: Bioelectric Analyses of an Osseointegrated Intelligent Implant Design System for Amputees (Video) | JoVE Journal
Uploaded: 2009-07-15T00:00:00.000+00:00
Duration: 14 min 31 s
Description: Discover bioelectric analyses using CT scans and finite element modeling to design intelligent osseointegrated implants for amputees. This computational method establishes safe electrical parameters to enhance skeletal fixation and reduce infection risks.

ai_agent_IP

ai_agent

maintenance

instantTrialDurationMinutes

isBiopharma

enableNewMyJoveSectionDesign

educationFeatures

seoMetaApi

ai_copilot_institution_blacklist

ai_chat_institution_blacklist

freeAccessWindow

ssoRoleCapture

scheduledMaintenanceBanner

joveLabsFeatures

disable_autoplay_for_bots

extended_trial_duration

5 minutes

playerTrialDesktop

This material is based upon research supported (or supported in part by) the Technology Commercialization Office, Salt Lake City, UT, Office of Research and Development, Rehabilitation R&amp;D Service, DVA SLC Health Care System, Salt Lake City, UT, Department of Defense PRMRP Grant (No. PR054520), the Albert &amp; Margaret Hofmann Chair and the Department of Orthopaedics, University of Utah School of Medicine, Salt Lake City, UT; Technical support for the simulations was provided by the Center for Integrative Biomedical Computing of Scientific Computing and Imaging Institute and was made possible in part by software from the NIH/NCRR Center for Integrative Biomedical Computing, P41-RR12553-07.
Additional gratitude is extended to Gwenevere Shaw for assistance with manuscript preparation and Dustin Williams for the image of biofilm.

Part 1: Using Computed Tomography (CT) Scans for Amputee Reconstruction
<ol><li>Retrospective CT scans were collected from the University of Utah and Department of Veteran Affairs hospitals after obtaining IRB and HIPAA approval.</li>
<li>CT scans were selected because they allow clear distinction between tissue types based on x-ray absorbency.</li>
<li>CTs were manually inspected and included in the study based on the absence of metal implants to prevent image artifact.</li>
</ol>
Part 2: Model Generation with Seg3D
<ol>
<li>Files were downloaded as Dicom images and loaded into Seg3D (version 1.11.0, software.sci.utah.edu) as a new volume.</li>
<li>A median filter was used to smooth the imported volumes prior to determining geometrically defined tissue structures.</li>
<li>The tissue boundaries of the bone, bone marrow, organs and adipose tissue were generated by thresholding the CT files interactively (Figure 1). 
<img src="/files/ftp_upload/1237/1237fig1.jpg" alt="Equine bone analysis, before and after treatment comparison, medical imaging results." data-altupdatedat="1787630408000" data-alt="Figure 1"> 
Figure 1: A sagittal cross section of an amputee residual limb thresholded and separated into specific tissue types.</li>
<li>The musculature was obtained by manually setting seed points inside the thresholded muscle tissue and using a confidence connected filter to find all the tissue connected to the seed points. This step eliminated erroneous tissues which may have been grouped together with the muscle based on similar absorbency from CTs.</li>
<li>The skin, which was impossible to discern reliably from the CT images, was generated by dilating the outermost tissue 2 millimeters based on average skin thickness to produce a layer of homogeneous thickness that surrounded the full model1.</li>
<li>Segmentations were manually inspected, corrected to ensure accuracy and combined in a hierarchy into a single label map required for finite element analysis (Figure 1). 
<img src="/files/ftp_upload/1237/1237fig2.jpg" alt="3D anatomical model, cross-section, purple hue, used in medical imaging and educational study." data-altupdatedat="1787630408000" data-alt="Figure 2"> 
Figure 2: Representative hierarchical model of a bilateral amputee created with Seg3D.</li>
</ol>
Part 3: Preparation for Finite Element Analysis
<ol>
<li>A 10 cm implant was designed in Matlab to serve as the implanted orthopedic device and cathode for electrical stimulation and imported into SCIRun (version 4.0, software.sci.utah.edu).</li>
</ol>
Part 4: Electrode Placement &amp; Design
<ol>
<li>SCIRun was utilized for electrode design because it supports interactive electrode placement and simulation.</li>
<li>A network was created and modules organized with specific functions to generate the mesh (Figure 3).  Modules were important for defining boundary conditions, tissue conductivities, mesh refinements, generating Matlab histograms, recording field data, etc (Table 1). 
<img src="/files/ftp_upload/1237/1237fig3.jpg" alt="Computational modeling, diagram with UI elements for 3D reconstruction, image processing analysis." data-altupdatedat="1787630408000" data-alt="Figure 3"> 
Figure 3: Representative network image from a pilot study using a two band external electrode configuration. 
<table border="1" cellspacing="0" cellpadding="0">
<tbody>
 <tr>
 <td>Table 1 </td>
 <td>  </td>
 </tr>
 <tr>
 <td colspan="2">Conductivities Assigned to Segmented Tissues </td>
 </tr>
 <tr>
 <td>Tissue Type</td>
 <td>Conductivities [S/m]</td>
 </tr>
 <tr>
 <td>Organ</td>
 <td>0.22</td>
 </tr>
 <tr>
 <td>Skin</td>
 <td>0.26</td>
 </tr>
 <tr>
 <td>Adipose</td>
 <td>0.09</td>
 </tr>
 <tr>
 <td>Muscle</td>
 <td>0.25</td>
 </tr>
 <tr>
 <td>Cortical Bone</td>
 <td>0.02</td>
 </tr>
 <tr>
 <td>Bone Marrow</td>
 <td>0.07</td>
 </tr>
</tbody>
</table>
</li>
<li>The configurations for the electrodes consisted of a one patch electrode, two patch electrodes, one continuous band and two continuous bands.</li>
<li>External electrode bands were applied to the residual limb of the models generated from patient CT scans and were 1.6 cm in thickness.</li>
<li>Electrode patches were placed as a strip covering approximately half the diameter of the residual limb and were 3 cm in thickness.</li>
<li>The inner cortical implant which represented the osseointegrated implant was set to endosteal diameter to allow for perfect implant fit and fill2.</li>
</ol>
Part 5: Finite Element Analysis
<ol>
<li>The simulations were generated assuming that the electric metrics could be calculated using a quasi-static approach with no time dependency.</li>
<li>The model was computed by solving Laplace’s equation for each tissue type generated from the Seg3D segmentations.</li>
<li>The boundary conditions were formed by the electrodes that injected currents and the guideline that current remained within the body.</li>
<li>Since the electrodes and the implant had a much larger conductivity than the surrounding tissues, it was assumed that the implant (cathode) was at a constant potential, likewise the surface electrodes were modeled with a constant potential difference from the percutaneous implant.</li>
<li>To evaluate the efficacy of electrode configuration and sizing, patient specific models were developed and the electrical potential around the implant interface was used to determine localized field strengths.</li>
<li>The model was generated using a hexahedral mesh that consisted of approximately 1.8 million elements which were treated as piecewise homogenous, ohmic and isotropic.</li>
<li>The optimal model for this experiment was selected with a relative difference &lt;5% in voltage gradients confirmed with a mesh sensitivity study to insure model accuracy (Table 2). 
<table border="1" cellspacing="0" cellpadding="0" width="305">
<tbody>
 <tr>
 <td>Table 2 </td>
 <td>  </td>
 <td>  </td>
 <td>  </td>
 </tr>
 <tr>
 <td colspan="3">Mesh Sensitivity Study for Amputee Model</td>
 <td> </td>
 </tr>
 <tr>
 <td>Mesh</td>
 <td>Elements</td>
 <td>Nodes</td>
 <td>Relative Difference</td>
 </tr>
 <tr>
 <td>100 100 50</td>
 <td>149089</td>
 <td>161131</td>
 <td>0.0995</td>
 </tr>
 <tr>
 <td>125 125 75</td>
 <td>350180</td>
 <td>371472</td>
 <td>0.0802</td>
 </tr>
 <tr>
 <td>150 150 100</td>
 <td>673032</td>
 <td>706082</td>
 <td>0.0545</td>
 </tr>
 <tr>
 <td>175 175 125</td>
 <td>1146778</td>
 <td>1194044</td>
 <td>0.0527</td>
 </tr>
 <tr>
 <td>200 200 150</td>
 <td>1796690</td>
 <td>1860772</td>
 <td>0.0439</td>
 </tr>
 <tr>
 <td>250 250 200</td>
 <td>3745038</td>
 <td>3850202</td>
 <td>0.0364</td>
 </tr>
 <tr>
 <td>275 275 225</td>
 <td>5097243</td>
 <td>5226587</td>
 <td>0.0301</td>
 </tr>
 <tr>
 <td>300 300 250 </td>
 <td>6742588</td>
 <td>6898729</td>
 <td>0.0000</td>
 </tr>
 </tbody>
</table>
</li>
<li>Using an iterative solver, the electric metrics in the finite element models were computed for the electrode configurations.</li>
</ol>

<ol><li>Tortora, G. J., Nielsen, M. T. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aTortora%2C+GJ+%22Principles+of+Human+Anatomy%22">Principles of Human Anatomy</a>. Roesch, B., et al. , John Wiley & Sons, Inc. Hoboken, NJ. (2009).</li>
<li>Bloebaum, R. D., Bachus, K. N., Momberger, N. G., Hofmann, A. A. Orthopaedic Research Society 39th Annual Meeting, San Francisco, CA, , (1993).</li>
<li>Goldberg, M. S. Military Medical/NBC Technology. 11 (8), 31(2007).</li>
<li>Fischer, H. Report No. Order Code RS22452. , (2008).</li>
<li>Moore, T. J., et al. Clin Orthop Relat Res. 238, 219(1989).</li>
<li>Hagberg, K., Branemark, R. Prosthet Orthot Int. 25 (3), 186(2001).</li>
<li>Todd, T. W., Barber, C. G. J Bone Joint Surg Am. 16, 53(1934).</li>
<li>Jaegers, S. M., Arendzen, J. H., de Jongh, H. J. Archives of Physical Medicine and Rehabilitation. 76 (8), 736(1995).</li>
<li>Potter, B. K., et al. Journal of American Academy of Orthopaedic Surgeons. 14 (10), 191(2006).</li>
<li>Kulkarni, J., Adams, J., Thomas, E., Silman, A. Clin Rehabil. 12 (4), 348(1998).</li>
<li>Smith, D. G., et al. Clin Orthop Relat Res. (361), 29(1999).</li>
<li>Pasquina, P. F., et al. Archives of Physical Medicine and Rehabilitation. 87 (3), 34(2006).</li>
<li>Ysander, M., Branemark, R., Olmarker, K., Myers, R. R. Journal of Rehabilitation Research & Development. 38 (2), 183(2001).</li>
<li>Couch, N. P., David, J. K., Tilney, N. L., Crane, C., et al. American Journal of Surgery. 133 (4), 469(1977).</li>
<li>Morgenroth, D. C., Shakir, A., Orendurff, M. S., Czerniecki, J. M. Am J Phys Med Rehabil. 88 (2), 108(2009).</li>
<li>Pendegrass, C. J., et al. Journal of Bone and Joint Surgery British. 90 (1), 114(2008).</li>
<li>Lee, W. C., et al. Med Eng Phys. 30 (7), 825(2008).</li>
<li>Brighton, C. T., Friedenberg, Z. B., Zemsky, L. M., Pollis, P. R. J Bone Joint Surg Am. 57 (3), 368(1975).</li>
<li>Spadaro, J. A. Bioelectromagnetics. 18 (3), 193(1997).</li>
<li>Brighton, C. T., Friedenberg, Z. B., Mitchell, E. I., Booth, R. E. Clin Orthop Relat Res. 124, 2(1976).</li>
<li>Friedenberg, Z. B., Brighton, C. T. J Bone Joint Surg Am. 48 (5), 915(1966).</li>
<li>Yonemori, K., et al. Bone. 19 (2), 173(1996).</li>
<li>Treharne, R. W., Brighton, C. T., Korostoff, E., Pollack, S. R. Clin Orthop Relat Res. (145), 300(1979).</li>
<li>Wiesmann, H., et al. Biochimica et Biophysica Acta. 1538 (1), 28(2001).</li>
<li>McLeod, K. J., Rubin, C. T. J Bone Joint Surg Am. 74 (6), 920(1992).</li>
<li>Rubinacci, A., Tessari, L. Calcified Tissue International. 35 (6), 728(1983).</li>
<li>Brighton, C. T., et al. J. Bone Joint Surg Am. 63 (5), 847(1981).</li>
<li>Friedenberg, Z. B., Zemsky, L. M., Pollis, R. P., Brighton, C. T. J Bone Joint Surg Am. 56 (5), 1023(1974).</li>
<li>Jorgensen, T. E. Clin Orthop Relat Res. 124, 124(1977).</li>
<li>Ehrlich, G. D., et al. Clin Orthop Relat Res. 437, 59(2005).</li>
<li>Soong, H. K., et al. Investigative Ophthalmology & Visual Science. 31 (11), 2278(1990).</li>
<li>Li, W. P., et al. Bone. 32 (8), 986(2006).</li>
<li>Leitgeb, N., Cech, R., Schrottner, J. Radiat Prot Dosimetry. 124 (2), 124(2007).</li>
<li>van der Borden, A. J., et al. Biomaterials. 28 (12), 2122(2007).</li>
<li>van der Borden, A. J., van der Mei, H. C., Busscher, H. J. Biomaterials. 26 (33), (2005).</li>
<li>Costerton, J. W., et al. Annual Review of Microbiology. 41, 435(1987).</li>
<li>Neut, D., van der Mei, H. C., Bulstra, S. K., Busscher, H. J. Acta Orthop. 78 (3), 299(2007).</li>
<li>Anwar, H., Dasgupta, M. K., Costerton, J. W. Antimicrobial Agents and Chemotherapy. 34 (11), 2043(1990).</li>
<li>Costerton, J. W. Clin Orthop Relat Res. (437), 7(2005).</li>
<li>Nelson, C. L., et al. Clin Orthop Relat Res. 437, 25(2005).</li>
<li>Gunterberg, B., et al. Conference Book of IXth World Congress ISPO, Amsterdam, The Netherlands, , (1998).</li>
<li>Lavine, L. S., Grodzinsky, A. J. J Bone Joint Surg Am. 69 (4), 626(1987).</li>
<li>Chakkalakal, D. A., Johnson, M. W. Clin Orthop Relat Res). (161), 133(1981).</li>
<li>Buckwalter, J. A., Glimcher, M. J., Cooper, R. R., Recker, R. J Bone Joint Surg Am. 77 (2), 1276(1995).</li>
<li>Lane, J. M., Vigorita, V. J. J Bone Joint Surg Am. 65 (2), 274(1983).</li>
</ol>

The technology disclosed in the article, specifically utilizing an orthopedic implant as a functional cathode to increase skeletal attachment, reduce rehabilitation and prevent bacterial adhesion is a novel idea invented by the first author in the journal submission. An invention disclosure form has been submitted to the University of Utah and a United States provisional patent has been filed protecting all technology disclosed.

Understanding the Electrical Stimulation Paradigm
Enhancements in medical care and evacuation strategies on the field of combat have lead to an increased number of warriors surviving disastrous war related injuries. While the improved survival rate is a medical advancement, servicemen and women are returning from combat with amputations that require intensive follow-up care, extensive rehabilitation and expensive prosthetic services from the Veteran Affairs Health Care System3. Congressional reports detail that over 1,000 war-related amputations have occurred as a result of Operation Enduring Freedom (OEF) and Operation Iraqi Freedom (OIF) conflicts4.
In the case of OEF and OIF veterans, approximately 15% of returning warriors have lost multiple limbs and a significant number of returning servicemen and women have short residual limbs where socket technology is not an option or has been rejected by the patient. The reported discontinued use of upper extremity prosthesis even exceeds 50% because the fixation devices are cumbersome and difficult to use comfortably5. Lower extremity prostheses are equally problematic and the common problems associated with soft tissue sockets include the inability to walk on challenging terrain6, limited residual limb length7, patient discomfort5, concern with non-physiological loading8, irritation from heterotopic ossification9 and risk of debilitating diseases10. However, osseointegration technology is a novel surgical technique that may reduce pain11, skin irritation12, enhance osseoperception13, improve mobility6, decrease pressure sores associated with sockets6, reduce energy for ambulation7,14 and better serve veteran and warriors with limited residual limb length15.
Despite the numerous physical and psychological advantages of osseointegration, the associated surgical procedures require more advanced infection prevention treatment streategies16, require long rehabilitation programs and include restrictive weight bearing protocols which may last up to 1.5 years postoperative17. Because the viability of the host bone and length of the residual limb is important for muscle attachment and functionality, developing new devices to improve osseointegration is key for returning servicemen and women. Therefore, development of an osseointegrated intelligent implant design (OIID) system which uses controlled electrical stimulation may reduce the length of rehabilitation and increase skeletal attachment for veteran and warrior amputees. However, since no current device is commercially available and directed for use with percutaneous osseointegrated implants, the motivation of the program is to confirm safety and efficacy with finite element analysis.
Understanding the role of electrical stimulation in bone remodeling, specifically the deposition of osteoids and mineralization, has remained speculative. However, electrical activity observed in bone may be the result of mechanical loading18,19 and therefore an electrical stimulus may be an effective mechanism for inducing bone repair19. The logic behind the hypothesis is explained in a fracture healing model. When long bones are loaded, the side in tension becomes electropositive and the compression side electronegative20,21, however, once a bone is broken, the site will remain electronegative with respect to the surrounding environment until healing has commenced and homeostasis resumed21. Simulating the natural healing cascade with an electrical signal has been believed to assist with calcium deposition22, slight alterations in oxygen content and pH23, recruitment of growth factors22 and assists with osteoblast migration and secretion of additional extracellular matrix24.
The premise that electrical stimulation alone could govern complete bone repair has been redefined and the current new hypothesis proposes that complete unions are formed by mechanical loads and an electrical stimulation co-stimulus19. The electrical impulses observed in vivo are associated with piezoelectric deformation of collagen or the large electro-kinetic currents produced by ionic constituents flowing past mineral portions of the bone matrix25. In fact, spontaneous potentials have been reported in bone as large as 6 millivolts and correlates with an increase in the mineral apposition rate of bone26.
Early work by Brighton and Friedenberg18,21,27,28 used the concept of electrical stimulation for bone regeneration in the 1960s and 1970s and demonstrated that direct current could be used to repair non-unions in a shorter period of time when compared to traditional healing methods. Additional models have investigated bone formation with restrictive weight bearing and demonstrated a thirty-one percent increase in osteogenic activity between controls and electrically stimulated limbs25.
While researchers in the field of electrical stimulation have paved the way for understanding the mechanism for osteoblast matrix deposition with electrical stimulation, inadequate understanding has limited the expansion of this technology. While there are many cases of successful healing of non-unions and fracture healing models, examples of patient discomfort and failed attempts are replete in the literature as well29. The problem with electrical stimulation occurs from scientists and clinicians controlling the wrong electric metrics and concentrating solely on current magnitudes. Previous researchers have looked to current as the &#x201C;magic bullet&#x201D; to fixing the approximate 500,000 non-unions which occur annually30. However, repeatability between models has been limited from joule heating complications31 and not determining current densities32. In fact, all manufactured biomedical devices must be limited to a current density less than 2 mA/cm2 as outlined by the International Electrotechnical Commission to prevent localized tissue necrosis and patient discomfort33.
Aside from assisting with skeletal fixation, controlled electrical stimulation may also prevent bacterial adhesion on orthopedic implants and reduce the risk for osteomyelitis and biofilm formation34-37. Biofilm formation on orthopedic devices lead to patient complications and significant distress for those who depend on these devices38. Emphasis is placed on the necessity to have completely sterilized instrumentation and implants prior to surgery39 , however it is often difficult to diagnose bacteria adhesion as evident from many negative cultured cases which indeed are infected40. This problem is often coupled with the fact that biofilms are slow growing in nature40, cannot be growth accurately in vitro39, depend on the type of bacteria cells, cleanliness of the surface and the immune system of the affected person39. Investigation of European transfemoral amputees with oseeointegration technology reveal the most frequent problem is infection (frequent superficial infections, 1/3 periprosthetic infections)41. While there have been vast improvements in surgical preparation, eradicating bacteria is one of the fundamental factors for improving osseointegration since biofilms are between 500-5000 thousand times more difficult to eradicate due to their non-platonic form34,35,39. Therefore, utilizing electrical stimulation as a modality for removing harmful bacterial colonies and increasing skeletal fixation are important factors for ensuring the protection of patient health and OIID efficacy.
The advantages of using veteran and warrior amputees are that the relative youth and otherwise good health of these individuals make them an ideal population for aggressive rehabilitation and a percutaneous post will serve as an ambulatory aid and may be developed as an exposed cathode for electrical stimulation. The presence of an osseointegrated implant does not require additional surgical procedures to insert electrical components, allows the device to be controlled externally and prevents further risk of infection42. Therefore, by understanding the method of current injection into the residual limb of veteran and warrior amputees, an electric field on the magnitude of 1-10 V/cm may be established, controlled and measured at the implant interface. It is hypothesized that this will allow safe levels of electricity to be delivered, capable of inducing osteoblast migration and improving skeletal attachment. An electric field of this degree will increase the quantity and quality of bone at the implant interface, and improve the prospects for accelerated rehabilitation and skeletal fixation for an amputee. Use of electrical stimulation has not been investigated as a modality to accelerate osseointegration in an intramedullary prosthetic implant and presents numerous opportunities for translational research to improve patient care.
Experimental Results
The necessity for patient specific models with a percutaneous electrical stimulation device was supported in the study. The simulations developed for the proposed biomedical device may have the capabilities of expediting skeletal attachment by increasing osteoblast migration and preventing bacterial adhesion27,34,36,39. Computation modeling has effectively shown that 1-10 V/cm electric fields and current densities below 2 mA/cm2 may be generated using the implant as a functional cathode and is most homogenously distributed using a two band external electrode. The OIID system may be the first step to resolving the classic problem associated with electrical stimulation; the inability to define current pathways in the human body43. Therefore, establishing tools for enhancing skeletal attachment may assist with reducing the length of rehabilitation required for an osseointegrated procedure.
Utilizing electrical stimulation for older amputees is also a critical aspect which must be explored as well. Bone mass is maximum a decade after skeletal growth ceases but decreases significantly by the eighth and ninth decade44. As long bones change with age, the endosteal diameter tends to increase more rapidly than the periosteal diameter which may lead to implant loosening45. This problem coupled with the reduction of strain on bones by weaker muscles may contribute to debilitating diseases such as osteoporosis and osteopenia45 and require additional treatment options for patients with osseointegrated implants. However, controlled electrical stimulation and mechanical loading may act as a synergistic catalyst of bone ongrowth and maintain host bone bed integrity with elderly patients using an OIID system.

bioelectric analyses of an osseointegrated intelligent implant design system for amputees

Bioelectric Analyses of an Osseointegrated Intelligent Implant Design System for Amputees

The projected number of American amputees is expected to rise to 3.6 million by 2050.  Many of these individuals depend on artificial limbs to perform routine activities, but prosthetic suspensions using traditional socket technology can prove to be cumbersome and uncomfortable for a person with limb loss.  Moreover, for those with high proximal amputations, limited residual limb length may prevent exoprosthesis attachment all together. Osseointegrated implant technology is a novel operative procedure which allows firm skeletal attachment between the host bone and an implant. Preliminary results in European amputees with osseointegrated implants have shown improved clinical outcomes by allowing direct transfer of loads to the bone-implant interface.  Despite the apparent advantages of osseointegration over socket technology, the current rehabilitation procedures require long periods of restrictive load bearing prior which may be reduced with expedited skeletal attachment via electrical stimulation.  The goal of the osseointegrated intelligent implant design (OIID) system is to make the implant part of an electrical system to accelerate skeletal attachment and help prevent periprosthetic infection. To determine optimal electrode size and placement, we initiated proof of concept with computational modeling of the electric fields and current densities that arise during electrical stimulation of amputee residual limbs.  In order to provide insure patient safety, subjects with retrospective computed tomography scans were selected and three dimensional reconstructions were created using customized software programs to ensure anatomical accuracy (Seg3D and SCIRun) in an IRB and HIPAA approved study.  These software packages supported the development of patient specific models and allowed for interactive manipulation of electrode position and size. Preliminary results indicate that electric fields and current densities can be generated at the implant interface to achieve the homogenous electric field distributions   required to induce osteoblast migration, enhance skeletal fixation and may help prevent periprosthetic infections.  Based on the electrode configurations experimented with in the model, an external two band configuration will be advocated in the future.

There is a need to develop alternative prosthesis attachment due to limb loss attributed to vascular occlusive diseases and trauma. The goal of the work is to introduce an osseointegrated intelligent implant design system to increase skeletal fixation and reduce periprosthetic infection rates for patients needing osseointegrated technology.

The projected number of American amputees is expected to rise to 3.6 million by 2050.  Many of these individuals depend on artificial limbs to perform ...

By the year 2050, the projected number of amputees in the United States is expected to reach 3.6 million. Complications with traditional prosthetic limbs often result in asymmetric gait patterns and discomfort for amputees who hope to resume active lifestyles following injury. Current prosthetic options are generally inadequate.This is primarily due to the high incidence of pressure sores from non-uniform loading patterns. One way to overcome the functional limitations of traditional prosthetics is to engineer direct skeletal attachment between an implant and the host bone of the amputee. This technology known as osteointegration depends on direct skeletal fixation and a tight epidermal and dermal seal to prevent infection when successful, osteointegration drastically improves activity levels for amputees with high proximal amputations.This is shown in the videos provided by Dr.Ash off's team. In conjunction with the Esca organization. There are two major complications that prevent the widespread adoption of osteointegration.The first is a risk of bacterial adhesion and biofilm formation on orthopedic implants. Second, there is a risk of inadequate skeletal fixation in vivo as shown here with radiolucencies. To overcome these complications, the exo prosthesis may be used as a cathode to deliver electrical stimulation.This may result in positive bone remodeling, prevent bacterial attachment, and reduce the rehabilitation time required for full weight bearing. This video demonstrates how implants are designed using a novel OSSEOINTEGRATED intelligent implant design system, or OIID. Hi, I'm Brad Isaacson from the Bone and Joint Research Laboratory at the University of Utah in the Department of Bioengineering.Today, we will show you how osteo integrated implant design can be tested and evaluated with hypothetical modeling using computational computer programs. Our goal is to provide proof of concept of a new osteointegration rehabilitation tool, which uses electrical stimulation. When implemented properly, the tool will be able to reduce periprosthetic infections and increase skeletal attachment.With that in mind, let me show you how we can develop an integrated intelligent implant design system. CT scans are used for model reconstruction because they allow different tissue types to be clearly distinguished. The scans used for this were collected from the University of Utah Hospital and the Department of Veteran Affairs after obtaining IRB and HIPAA approval.These CT scans were first visually inspected and selected for the study based on the absence of metal implants, which can introduce image artifacts. Here we show a case of an amputee who would be unacceptable to computer model based upon the presence of an orthopedic implant. The amputee has an orthopedic implant inside the medullary canal, and when we scan through the CT slices, we see image distortion.However, we would advocate for a patient such as this. The amputee has clear distinction between tissue types crucial for anatomically accurate models. Excellent To begin.Model generation CT scan files are downloaded as DICOM images and loaded into the SEG 3D program as a new volume. A median filter is then used to smooth the imported volumes prior to determining geometrically defined tissue structures. Once filtering is complete, the tissue boundaries of the bone, bone marrow, organs, and adipose tissue are generated by thresholding.The CT images interactively and filtered once more. The musculature is obtained by manually setting seed points inside the threshold of muscle tissue, and by using a confidence connected filter to find all the tissue connected to the seed points. This step eliminates erroneous tissues that may be grouped together with the muscle based on similar absorbency from cts.The skin, which is impossible to discern reliably from the CT images is generated by dilating the outermost tissue, two millimeters based on average skin thickness. This produces a layer of homogeneous thickness that surrounds the full model. Segmentations are manually inspected, corrected to ensure accuracy and combined in a hierarchy to a single label map.Required for finite element analysis. Models are exported to ski run using a near raw raster data format or n NRD file developed by Gordon Delman at the Ski Institute. This is now the basic file type from many open source image processing software platforms.This file is exported and used as the mesh for finite element analysis. So far, we have manually inspected RCTs and combined them in a hierarchy O model. In the SEG 3D program there, we're able to define tissue geometry and space.Next, we'll load these models into ski, run our finite element package to define the electrode positioning and type. The reason for using finite element analysis is it provides numerical approximation for complex geometries such as our amputees residual limbs. To prepare for finite element analysis, the 16 centimeter implant is designed using custom software written in mat lab, then imported into ski run to serve as the orthopedic implant and cathode for electrical stimulation.The electric field must be maintained below one to 10 volts per centimeter, and the current density must be lower than two milliamps per square centimeter. This will induce osteoblast migration, prevent localized tissue heating, and ensure patient safety. This is indicated with the red dashed line.Small electrodes are designed to ensure patient comfort. Since the OIID device should not restrict movement or daily activities during rehabilitation. The ski run software package is used to design the electrode because it supports interactive electrode placement and simulation.A network is created and modules organized with specific mathematical functions for defining boundary conditions, tissue conductivities and mesh refinements, as well as for generating histograms of electrical metric distributions and recording filled data. The configurations for the electrodes consist of one patch electrode, two patch electrodes, one continuous band, and two continuous bands. External electrode bands, which are 1.6 centimeters thick are applied to the residual limb of the models generated from patient CT scans.Electrode patches, which are three centimeters thick, are placed in a strip covering approximately half the diameter of the residual limb. The inner cortical implant shown in red represents the osteo implant. This is set to diameter to allow for perfect implant fit and fill.Now that the electrodes have been placed, we'll perform the finite element analysis. The goal here is to maintain any electric field between one to 10 volts per centimeter to be an effective tool while maintaining a current density below two milliamps per centimeter squared to prevent tissue necrosis To perform finite element analysis, simulations are generated assuming that the electrical metrics can be calculated using a quasi static approach with no time dependency. Numerical approximation is used to compute the electrical potential through the subject's residual limb.The model is computed by solving poisson's equation and electrostatics for each tissue type generated from the SEG 3D segmentations. Boundary conditions are assigned in the model in which nodes are continuous between tissue types and to govern the path of current flow. Because the electrodes and the implant have much larger conductivities than the surrounding tissues, it is assumed that the implant or cathode is at a constant potential.Likewise, the surface electrodes are modeled with a constant potential difference from the percutaneous implant. Numerical simulation is used to compute the electric potential through the subject's residual limb to evaluate the efficacy of electrode configuration and sizing. Patient specific models are developed and the electrical potential around the implant interface is used to determine localized field strengths.The model is generated using a hexa hugel mesh that consists of approximately 1.8 million elements. The optimal element size for this experiment is selected based on a mesh sensitivity study that results in the less than 5%relative difference in voltage gradients. This ensures model accuracy.The elements are treated as peace wise, homogeneous omic and isotropic elements. Electrodes are incorporated in the finite element meshing and assigned a constant potential difference between the skin electrode and the osseointegrated implant. This selection is based on expected tissue resistivity.The potential is set between one and 4.5 volts based on the amputee geometry, resistivity of the tissues and electrode configuration using an iterative solver. The potentials in the finite element models are computed for four electrode configurations. This is done to determine the optimal design for amputees.Validation of finite element analysis is being performed with an iacuc approved small animal in vivo model. An implant is inserted into the medullary canal of a rabbit and a second electrode is placed on the musculature. This generates a subcutaneous electric field at the bone implant interface.All ski run finite element analysis as validated by needle electrodes placed between the two electrodes, which record the voltage gradients of the rabbit in vivo. The rabbit model study utilizes the same protocol as the OIID designated for human research. This is done by attaining computed tomography scans, developing 3D hierarchical models in SEG 3D and performing computational modeling with ski run.The goal is to develop the electrical metrics required to prove the efficacy of controlled electrical stimulation. This aims to reduce periprosthetic infections and increase implant fixation strengths. So the technology can be FDA approved and used for rehabilitating amputees.Successful completion of the detailed protocol will result in anatomically accurate amputee residual limbs developed from computed tomography scans. This will provide the initial guidelines for the OIID system. Users will develop an interactive interface to adjust electrode size and position for patient specific models.Altered electric fields and current densities will be the result of tissue geometry and electrode position. Therefore, models will require individual alterations to help persons with limb loss seeking osteointegration technology. The osseointegrated intelligent implant design system that we showed you today will help alleviate the classic problem with electrical stimulation, the inability to define the current pathway in the human body.Preliminary results indicate that the electric field at the bone implant interface may have the capabilities of reducing periprosthetic infections, increasing skeletal attachment, and be used as an effective tool for our clinicians in rehabilitation. So that's it. Thank you for watching and good luck with your osseointegration research.

bioelectric-analyses-an-osseointegrated-intelligent-implant-design

Research

JoVE Journal

Biology

Body segmental inertial properties are required for inverse dynamics modeling. Using an oscillation and reaction board technique, inertial properties of below-knee prostheses were measured. Using direct measures of prosthesis inertia in the inverse dynamics model of the prosthetic leg resulted in lower magnitudes of resultant joint forces and moments.

Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis

Neural-machine interfaces (NMI) have been developed to identify the user's locomotion mode. These NMIs are potentially useful for neural control of powered artificial legs, but have not been fully demonstrated. This paper presented (1) our designed engineering platform for easy implementation and development of neural control for powered lower limb prostheses and (2) an experimental setup and protocol in a laboratory environment to evaluate neurally-controlled artificial legs on patients with...

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

An improved method to mechanically test bone anchorage to candidate implant surfaces is presented. This method allows for alignment of the disruption force exactly perpendicular, or parallel, to the plane of the implant surface, and provides an accurate means to direct the disruption forces to an exact peri-implant region.

An Improved Mechanical Testing Method to Assess Bone-implant Anchorage

Here we present a protocol which characterizes the sense of agency developed over the control of sensate virtual or robotic prosthetic hands. Psychophysical questionnaires are employed to capture the explicit experience of agency, and time interval estimates (intentional binding) are employed to implicitly measure the sense of agency.

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

Optimal functional outcomes after bionic reconstruction in patients with global brachial plexus injury depend on a structured rehabilitation protocol. Surface electromyographic guided training may improve the amplitude, separation and consistency of EMG signals, which - after elective amputation of a functionless hand - control and drive a prosthetic hand.

Surface Electromyographic Biofeedback as a Rehabilitation Tool for Patients with Global Brachial Plexus Injury Receiving Bionic Reconstruction

Presented here is a protocol for the manufacturing of an implantable system for in vivo chronological recording of evoked and spontaneous electromyographic potentials. The system is applied to the investigation of reinnervation of laryngeal muscles following nerve injury.

An Implantable System For Chronic In Vivo Electromyography

The following manuscript describes a novel method for developing a biologic, closed loop neural feedback system termed the composite regenerative peripheral nerve interface (C-RPNI). This construct has the ability to integrate with peripheral nerves to amplify efferent motor signals while simultaneously providing afferent sensory feedback.

Fabrication of the Composite Regenerative Peripheral Nerve Interface (C-RPNI) in the Adult Rat

Here, we introduce a lightweight, cost-effective probe implant system for chronic electrophysiology in rodents optimized for ease of use, probe recovery, experimental versatility, and compatibility with behavior.

The DREAM Implant: A Lightweight, Modular, and Cost-Effective Implant System for Chronic Electrophysiology in Head-Fixed and Freely Behaving Mice

A protocol is presented for the installation and preparation of intramuscular electrodes, along with an animal-mountable, miniaturized measurement device for electromyography (EMG) analysis in locomotion studies. This system enables the wireless transmission of real-time EMG data for investigating locomotion recovery in stroke model rats.

A Real-Time Wearable Electromyography Measurement System for Small Animals

A major impediment to biochemical analyses of ribosomes containing nascent peptidyl-tRNAs has been the presence of other ribosomes in the same samples, ribosomes not involved in the translation of the specific mRNA sequence being analyzed. We developed a simple methodology to purify, exclusively, the ribosomes containing the nascent peptidyl-tRNA of interest.

Isolation of Translating Ribosomes Containing Peptidyl-tRNAs for Functional and Structural Analyses

Discover bioelectric analyses using CT scans and finite element modeling to design intelligent osseointegrated implants for amputees. This computational method establishes safe electrical parameters to enhance skeletal fixation and reduce infection risks.

Bioelectric Analyses of an Osseointegrated Intelligent Implant Design System for Amputees

Explore More Videos