Dr. Kartik Varadarajan Profile

Dr. Kartik Varadarajan

at Massachusetts General Hospital

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 22 March 2021 Presentation + Paper

Characterization of interfacial failure in cemented total joint replacements via self-sensing bone cement, electrical impedance tomography, and machine learning

H. Ghaednia, C. Owens, L. Keiderling, A. Hart, K. Varadarajan, J. Schwab, T. Tallman

Proceedings Volume 11593, 1159310 (2021) https://doi.org/10.1117/12.2581950

KEYWORDS: Cements, Bone, Tomography, Machine learning, Failure analysis, Electrical breakdown, Polymethylmethacrylate, Surgery, Tumor growth modeling, Smart materials

Read Abstract +

At an estimated cost of $8 billion annually in the United States, revision total joint replacement surgeries represent a substantial financial burden to the health care system. Fixation failures such as implant loosening and mechanical instability at the cement-to-bone interface are the main causes of long-term implant failure. Early and accurate diagnosis of cement-bone interface failure is critical to develop therapeutic strategies and reduce the risk of delayed revision surgery. Unfortunately, prevailing imaging modalities such as plain radiographs struggle to detect the precursors of implant failure and are often interpreted incorrectly. Our team has recently shown that modification of poly(methyl methacrylate) (PMMA) bone cement with low concentrations of conductive fillers makes it piezoresistive and therefore self-sensing. By electrical impedance tomography (EIT) it is possible to monitor load transfer across the PMMA using real-time electrical measurements, which are physiologically benign and would ultimately be cost-effective in a clinical setting. Herein, we demonstrate the use of machine learning with EIT in order to precisely characterize model failure events. In phantom tests, we introduce three defect types: loosening, vertical cracks, and horizontal cracks. We show that implant defect type can be classified with greater than 95% accuracy by combining principal component analysis (PCA) coupled with the k-nearest neighbor (KNN) algorithm and a two layer feed-forward neural network with three hidden neurons . These preliminary results show that the combination of smart materials, EIT, and machine learning may be a powerful tool for predicting and diagnosing failure in joint replacements.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years