Francis T Omigbodun, Norman Osa-Uwagboe, Amadi Gabriel Udu, Bankole I Oladapo
{"title":"利用机器学习优化用于骨植入的 PLA/cHAP 的机械性能和 3D 打印。","authors":"Francis T Omigbodun, Norman Osa-Uwagboe, Amadi Gabriel Udu, Bankole I Oladapo","doi":"10.3390/biomimetics9100587","DOIUrl":null,"url":null,"abstract":"<p><p>This study explores the fabrication and characterisation of 3D-printed polylactic acid (PLA) scaffolds reinforced with calcium hydroxyapatite (cHAP) for bone tissue engineering applications. By varying the cHAP content, we aimed to enhance PLA scaffolds' mechanical and thermal properties, making them suitable for load-bearing biomedical applications. The results indicate that increasing cHAP content improves the tensile and compressive strength of the scaffolds, although it also increases brittleness. Notably, incorporating cHAP at 7.5% and 10% significantly enhances thermal stability and mechanical performance, with properties comparable to or exceeding those of human cancellous bone. Furthermore, this study integrates machine learning techniques to predict the mechanical properties of these composites, employing algorithms such as XGBoost and AdaBoost. The models demonstrated high predictive accuracy, with R<sup>2</sup> scores of 0.9173 and 0.8772 for compressive and tensile strength, respectively. These findings highlight the potential of using data-driven approaches to optimise material properties autonomously, offering significant implications for developing custom-tailored scaffolds in bone tissue engineering and regenerative medicine. The study underscores the promise of PLA/cHAP composites as viable candidates for advanced biomedical applications, particularly in creating patient-specific implants with improved mechanical and thermal characteristics.</p>","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11504968/pdf/","citationCount":"0","resultStr":"{\"title\":\"Leveraging Machine Learning for Optimized Mechanical Properties and 3D Printing of PLA/cHAP for Bone Implant.\",\"authors\":\"Francis T Omigbodun, Norman Osa-Uwagboe, Amadi Gabriel Udu, Bankole I Oladapo\",\"doi\":\"10.3390/biomimetics9100587\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This study explores the fabrication and characterisation of 3D-printed polylactic acid (PLA) scaffolds reinforced with calcium hydroxyapatite (cHAP) for bone tissue engineering applications. By varying the cHAP content, we aimed to enhance PLA scaffolds' mechanical and thermal properties, making them suitable for load-bearing biomedical applications. The results indicate that increasing cHAP content improves the tensile and compressive strength of the scaffolds, although it also increases brittleness. Notably, incorporating cHAP at 7.5% and 10% significantly enhances thermal stability and mechanical performance, with properties comparable to or exceeding those of human cancellous bone. Furthermore, this study integrates machine learning techniques to predict the mechanical properties of these composites, employing algorithms such as XGBoost and AdaBoost. The models demonstrated high predictive accuracy, with R<sup>2</sup> scores of 0.9173 and 0.8772 for compressive and tensile strength, respectively. These findings highlight the potential of using data-driven approaches to optimise material properties autonomously, offering significant implications for developing custom-tailored scaffolds in bone tissue engineering and regenerative medicine. The study underscores the promise of PLA/cHAP composites as viable candidates for advanced biomedical applications, particularly in creating patient-specific implants with improved mechanical and thermal characteristics.</p>\",\"PeriodicalId\":8907,\"journal\":{\"name\":\"Biomimetics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11504968/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomimetics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.3390/biomimetics9100587\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomimetics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3390/biomimetics9100587","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
摘要
本研究探讨了骨组织工程应用中使用羟基磷灰石钙(cHAP)增强的三维打印聚乳酸(PLA)支架的制造和表征。通过改变 cHAP 的含量,我们旨在增强聚乳酸支架的机械和热性能,使其适用于承重生物医学应用。结果表明,增加 cHAP 的含量可提高支架的拉伸和压缩强度,但同时也会增加脆性。值得注意的是,加入 7.5% 和 10% 的 cHAP 能显著提高热稳定性和机械性能,其性能可与人类松质骨媲美或超过人类松质骨。此外,本研究还整合了机器学习技术,采用 XGBoost 和 AdaBoost 等算法来预测这些复合材料的机械性能。这些模型具有很高的预测准确性,抗压强度和抗拉强度的 R2 分别为 0.9173 和 0.8772。这些发现凸显了使用数据驱动方法自主优化材料特性的潜力,对开发骨组织工程和再生医学中的定制支架具有重要意义。这项研究强调了聚乳酸/HAP 复合材料作为先进生物医学应用的可行候选材料的前景,特别是在制造具有更好的机械和热特性的患者特异性植入物方面。
Leveraging Machine Learning for Optimized Mechanical Properties and 3D Printing of PLA/cHAP for Bone Implant.
This study explores the fabrication and characterisation of 3D-printed polylactic acid (PLA) scaffolds reinforced with calcium hydroxyapatite (cHAP) for bone tissue engineering applications. By varying the cHAP content, we aimed to enhance PLA scaffolds' mechanical and thermal properties, making them suitable for load-bearing biomedical applications. The results indicate that increasing cHAP content improves the tensile and compressive strength of the scaffolds, although it also increases brittleness. Notably, incorporating cHAP at 7.5% and 10% significantly enhances thermal stability and mechanical performance, with properties comparable to or exceeding those of human cancellous bone. Furthermore, this study integrates machine learning techniques to predict the mechanical properties of these composites, employing algorithms such as XGBoost and AdaBoost. The models demonstrated high predictive accuracy, with R2 scores of 0.9173 and 0.8772 for compressive and tensile strength, respectively. These findings highlight the potential of using data-driven approaches to optimise material properties autonomously, offering significant implications for developing custom-tailored scaffolds in bone tissue engineering and regenerative medicine. The study underscores the promise of PLA/cHAP composites as viable candidates for advanced biomedical applications, particularly in creating patient-specific implants with improved mechanical and thermal characteristics.