[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128306-en":3,"doc-seo-128306-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128306,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Dynamic Optimisation for Graded Tissue Scaffolds Using Machine Learning Techniques","Tissue scaffolds provide a promising solution for treating critical size bone defects by enabling long-term bone ingrowth. A dynamic optimisation framework is developed to customise functionally graded bone scaffolds to maximise bone ingrowth over a specified period. Machine learning is leveraged to improve design efficiency: two neural networks are embedded in a dynamic bone growth model, while another neural network guides a genetic algorithm for the optimisation process. Effectiveness is illustrated via sheep mandible reconstruction and validated against FE modeling using mechanical testing with tailored 3D printed PEK scaffolds, comparing uniform, lateral gradient and vertical gradient schemes.","Computer Methods in Applied Mechanics and Engineering 425 (2024) 116911  \nContents lists available at ScienceDirect  \nComputer Methods in Applied Mechanics and Engineering  \njournal [homepage:](homepage: www.elsevier.com/locate/cma)[ www.elsevier.com/locate/cma](homepage: www.elsevier.com/locate/cma)  \n| Dynamic optimisation for graded tissue learning techniques |  |  | scaffolds using machine |  |\n| --- | --- | --- | --- | --- |\n| Chi Wu a, Boyang Wana, Yanan Xu a, D S Abdullah Al Marufb, c, Kai Cheng d, William T Lewine, f, i, Jianguang Fang g, Hai Xin b, c, Jeremy M Crook e, f, h, i, Jonathan R Clark b, c, d, Grant P Steven a, Qing Lia, *\u003Cbr>a School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, NSW 2006, Australia\u003Cbr>b Integrated Prosthetics and Reconstruction, Department of Head and Neck Surgery, Chris O’Brien Lifehouse, Camperdown, NSW 2050, Australia c Central Clinical School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2050, Australia\u003Cbr>d Royal Prince Alfred Institute of Academic Surgery, Sydney Local Health District, Camperdown, NSW 2050, Australia e Arto Hardy Family Biomedical Innovation Hub, Chris O`Brien Lifehouse, Camperdown, NSW 2050, Australia f Sarcoma and Surgical Research Centre, Chris O’Brien Lifehouse, Camperdown, Australia\u003Cbr>g School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia h Intelligent Polymer Research Institute, AIIM Facility, The University of Wollongong, Wollongong, Australia i School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, Australia |  |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |  |\n| Keywords:\u003Cbr>Functionally-graded tissue scaffolds Dynamic topology optimisation Machine learning\u003Cbr>Sheep mandible\u003Cbr>Bone remodelling Homogenisation |  | Tissue scaffolds have emerged as a promising solution for treatment of critical size bone defects, offering significant advantages over conventional strategies. One of the key functionalities of bone scaffolds is their ability to promote long-term bone ingrowth effectively. To enhance this functionality, we develop a novel dynamic optimisation framework to customise bone scaffolds for achieving maximum bone ingrowth outcomes over a certain period in this study. To improve the design efficiency, we extensively leverage machine learning (ML) techniques within our proposed dynamic optimisation framework. Specifically, two neural networks are integrated into a dynamic bone growth model, and another neural network is coupled with a genetic algorithm for dynamic optimisation process. To demonstrate the effectiveness and efficiency of the approach, we employ a sheep mandible reconstruction for treating a critical size bone defect as an illustraive example. To validate the finite element (FE) model established, we first conduct a mechanical test on the sheep mandible assembled with a tailored 3D printed scaffold made of Polyetherketone (PEK) material. Then, we compare three different optimisation schemes, namely uniform design, lateral gradient design, and vertical gradient design, with an empirical design under the same biomechanical conditions. A 18.5 % enhancement is found in the long-term bone ingrowth when the optimised scaffold is adopted in comparison with the empirical design, which is attributed to the fine-tuning of strut sizes within lattice scaffold structures for facilitating bone regeneration in the gradient regions. This study proposes a novel design framework by combining ML and time-dependent topology optimisation, which provides a new methodology for developing innovative tissue scaffolds with better clinical outcomes. |  |  |\n\n* Corresponding author.  \nE-mail address: [qing.li@sydney.edu.au](qing.li@sydney.edu.au) (Q. Li).  \n[https://doi.org/10.1016/j.cma.2024.116911](https://doi.org/10.1016/j.cma.2024.116911)  \nReceived 4 October 2023; Received in revised fo","cbCaid74TI3gF0B4","https://ap.wps.com/l/cbCaid74TI3gF0B4","pdf",10030594,2,1,20,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Background on critical size bone defects\n## Limitations of conventional grafts\n## Role and design of bone scaffolds\n## Mechanical-property-driven vs bone-ingrowth-driven design","[{\"question\":\"What is the main goal of the dynamic optimisation framework for graded tissue scaffolds?\",\"answer\":\"To customise functionally graded bone scaffolds so they achieve maximum long-term bone ingrowth over a defined period.\"},{\"question\":\"How are machine learning techniques incorporated into the optimisation process?\",\"answer\":\"Two neural networks are integrated into a dynamic bone growth model, and another neural network is coupled with a genetic algorithm to drive dynamic optimisation.\"},{\"question\":\"What comparison schemes are evaluated using the sheep mandible reconstruction example?\",\"answer\":\"Uniform design, lateral gradient design, and vertical gradient design are compared with an empirical design under the same biomechanical conditions.\"}]","Dynamic Optimisation for Graded Tissue Scaffolds Using Machine Learning Techniques | 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is the main goal of the dynamic optimisation framework for graded tissue scaffolds?","Question",{"text":76,"@type":77},"To customise functionally graded bone scaffolds so they achieve maximum long-term bone ingrowth over a defined period.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are machine learning techniques incorporated into the optimisation process?",{"text":81,"@type":77},"Two neural networks are integrated into a dynamic bone growth model, and another neural network is coupled with a genetic algorithm to drive dynamic optimisation.",{"name":83,"@type":74,"acceptedAnswer":84},"What comparison schemes are evaluated using the sheep mandible reconstruction example?",{"text":85,"@type":77},"Uniform design, lateral gradient design, and vertical gradient design are compared with an empirical design under the same biomechanical 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