Predictive modeling of nano-enhanced cartilage regeneration using machine learning

Zou Shu , Li Jiameng , Zhou Weili , Wu Yue

Abstract

The regeneration of cartilage is a significant clinical problem because self-healing of this tissue is less pronounced and because of its complicated mechanobiological activity. Recent breakthroughs in nanotechnology have shown that nanoparticle-modified scaffolds can be greatly useful in cartilage repair through the mechanical strength, bioactivity, and cellular reactions. Weakly linear interactions between nanoparticle properties, scaffold behavior, biological environment and mechanical stimulation however, render conventional trial-and-error optimization time consuming and costly. This paper is an attempt to build a machine learning-inspired predictive model to model and optimize the results of nano-enhanced cartilage regeneration. A file with numerous samples was developed, including the most important input variables, including the type of nanoparticles, size, and concentration, scaffold pore structure and elasticity, cell seeding density, culture time and level of mechanical stimulation. The main output variable created as a cartilage regeneration index is a composite index of tissue quality, extracellular matrix formation and functional recovery. Trained supervised machine learning models were used to embrace the multifaceted nonlinear associations between inputs and regeneration performance. The findings show that the specified predictive models are capable of providing accurate estimates of the cartilage regeneration results in a broad design space. Important findings derived during feature importance analysis are that culture time, nanoparticle concentration, mechanical stimulation, and scaffold porosity are supreme factors determining regeneration efficiency. The constructed framework presents a potent surrogate modeling platform that is able to inform the rational design and optimization of nano-engineered cartilage scaffolds as well as drastically save on the experimental work. This paper presents the possibility of machine learning being a useful decision-support tool in the field of advanced cartilage tissue engineering and regenerative medicine.

Key Words

nanocomposite scaffolds; nano-enhanced cartilage regeneration; machine learning; predictive modeling; tissue engineering

Address

Zou Shu — Department of orthopedics, The Fourth Hospital of Changsha,No. 200, Section 4, Jinxing North Road, Wangcheng District, Changsha City, Hunan Province, Changsha 412002, Hunan, China
Li Jiameng — Department of Spine Surgery, The Fourth Hospital of Changsha, No. 200, Section 4, Jinxing North Road, Wangcheng District, Changsha City, Hunan Province, Changsha 412002, Hunan, China
Zhou Weili — Department of Joint Surgery, Changsha Third Hospital, No. 176 Laodong West Road, Changsha City
Wu Yue — Department of Orthopedics, Beijing Chaoyang Hospital, affiliated with Capital Medical University, No. 8, Gongti South Road, Chaoyang District, Beijing

PDF Viewer

Preview is limited to the first 3 pages. Sign in to access the full PDF.

← Back to Volume 20, No. 1