AI-guided nano-coated implants for personalized joint replacement

Huang Shengxiang , Zhou Weili , Zhang Zhuo

Abstract

Patient-specifics and the nature of the implant surfaces are essential to the long-term performance of joint replacement implants, but a traditional method of designing them continues to be based on generalized assumptions that cannot be tailored to one person. This paper is intended to introduce an artificial intelligence (AI)-based framework to optimalization of nano-coated orthopedic implants to match individual patient characteristics. To train prediction machine learning models, a complete dataset that includes characteristics of the patient, properties of the implant materials, nano-coating factors, and AI-based design indicators is created. An integrated output in terms of mechanical stability, wear resistance, and potential to allow integration of the implant is the index of implant performance. The findings show that AI-optimized designs are always superior relative to traditional designs in all forms of nano-coatings and carbon-based nano-coatings are especially more effective. The given strategy points out the potential of AI to learn the intricate nonlinear relationships between biological and nano-engineered variables, which creates a solid avenue toward personalized joint replacement and better implant survival.

Key Words

artificial intelligence; machine learning optimization; nano-coatings; orthopedic implants; personalized joint replacement

Address

Huang Shengxiang — Department of Pediatric Orthopedics, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University (Hunan Children's Hospital), Changsha, 410007, China
Zhou Weili, Zhang Zhuo — Department of Orthopedics, Changsha Third Hospital, 176 Laodong West Road, Changsha City

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