Qualitative predictions of bone growth over optimally designed macro-textured implant surfaces obtained using NN-GA based machine learning framework. (September 2021)

• Record Type: Journal Article
• Authors: Ghosh, Rajdeep; Chanda, Souptick; Chakraborty, Debabrata
• Is Part Of: Medical engineering & physics. Volume 95(2021)
• Page Start: 64
• Page End: 75
• Publication Date: 2021-09
• Subjects: Macro-textures -- Uncemented implant -- Mechanobiology -- Tissue differentiation -- Neural networks -- Genetic algorithm; Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2021.08.002
• Languages: English
• ISSNs: 1350-4533

Abstract

Highlights: It aims to design optimal surface textures on implant to enhance its biologic fixation with the host bone. Three representative initial implant surface textures were chosen based on an earlier investigation. A combined finite element(FE)-neural network(NN)-genetic algorithm(GA) approach was implemented. NNs substitutes tedious FE analyses to predict bone growth while, optimization of the geometric features of implant surfaces has been solved using GA. Abstract: The surface features on implant surface can improve biologic fixation of the implant with the host bone leading to improved secondary (biological) implant stability. Application of finite element (FE) based mechanoregulatory schemes to estimate the amount of bone growth for a wide range of implant surface features is either manually intensive or computationally expensive. This study adopts an integrated approach combining FE, back-propagation neural network (BPNN) and genetic algorithm (GA) based search to evaluate optimum surface macro-textures from three representative implant models so as to enhance bone growth. Initial surface textures chosen for the implant models were based on an earlier investigation. Based on FE predicted dataset, a BPNN was formulated for faster prediction of bone growth. Using the BPNN predicted output, a GA-based search was carried out to maximize bone growth subject to clinically admissible micromotion at the bone-implant interface. The results from FE analysis and bone growth predictions from the BPNN were found to have strong correlation. The optimal osseointegration-maximized-textures (OMTs) obtained were found to offer enhanced biological fixation, as compared to that offered by the textures in the initial models. Results from the present study reveal that certain reduction in the dimension of ribs/grooves promotes bone growth. However, periodic patterns of ribs with higher and lower rib dimensions provide uniform stress environment at the interface thus promoting osseointegration.