Predicting Corrosion Damage in the Human Body Using Artificial Intelligence: In Vitro Progress and Future Applications Applications

Authors

Michael A. Kurtz, Clemson University
Ruoyu Yang, Clemson University
Mohan S. R. Elapolu, Clemson University
Audrey C. Wessinger, Clemson University
William Nelson, Clemson University
Kazzandra Alaniz, Clemson University
Rahul Rai, Clemson University
Jeremy L. Gilbert, Clemson University

Document Type

Article

Publication Date

7-2023

Publication Title

Orthopedic Clinics of North America

Volume

54

Issue

2

Publisher

Elsevier

DOI

https://doi.org/10.1016/j.ocl.2022.11.004

Abstract

Artificial intelligence (AI) is used in the clinic to improve patient care. While the successes illustrate the impact AI can have, few studies have led to improved clinical outcomes. A gap in translational studies, beginning at the basic science level, exists. In this review, we focus on how AI models implemented in non-orthopedic fields of corrosion science may apply to the study of orthopedic alloys. We first define and introduce fundamental AI concepts and models, as well as physiologically relevant corrosion damage modes. We then systematically review the corrosion/AI literature. Finally, we identify several AI models that may be Preprint implemented to study fretting, crevice, and pitting corrosion of titanium and cobalt chrome alloys.

Comments

The published version of this article can be found here: https://www.sciencedirect.com/science/article/abs/pii/S0030589822001730?via%3Dihub

Recommended Citation

Kurtz, Michael A.; Yang, Ruoyu; Elapolu, Mohan S. R.; Wessinger, Audrey C.; Nelson, William; Alaniz, Kazzandra; Rai, Rahul; and Gilbert, Jeremy L., "Predicting Corrosion Damage in the Human Body Using Artificial Intelligence: In Vitro Progress and Future Applications Applications" (2023). Publications. 23.
https://tigerprints.clemson.edu/bioengineering_pubs/23