Osteoinductive biomaterials: Machine learning for prediction and interpretation.

Journal: Acta biomaterialia
PMID: 39178926

Abstract

Biomaterials with osteoinductivity are widely used for bone defect repair due to their unique structures and functions. Machine learning (ML) is pivotal in analyzing osteoinductivity and accelerating new material design. However, challenges include creating a comprehensive database of osteoinductive materials and dealing with low-quality, disparate data. As a standard for evaluating the osteoinductivity of biomaterials, ectopic ossification has been used. This paper compiles research findings from the past thirty years, resulting in a robust database validated by experts. To tackle issues of limited data samples, missing data, and high-dimensional sparsity, a data enhancement strategy is developed. This approach achieved an area under the curve (AUC) of 0.921, a precision of 0.839, and a recall of 0.833. Model interpretation identified key factors such as porosity, bone morphogenetic protein-2 (BMP-2), and hydroxyapatite (HA) proportion as crucial determinants of outcomes. Optimizing pore structure and material composition through partial dependence plot (PDP) analysis led to a new bone area ratio of 14.7 ± 7 % in animal experiments, surpassing the database average of 10.97 %. This highlights the significant potential of ML in the development and design of osteoinductive materials. STATEMENT OF SIGNIFICANCE: This study leverages machine learning to analyze osteoinductive biomaterials, addressing challenges in database creation and data quality. Our data enhancement strategy significantly improved model performance. By optimizing pore structure and material composition, we increased new bone formation rates, showcasing the vast potential of machine learning in biomaterial design.

Authors

  • Sicong Lin
    College of Biomedical Engineering, Sichuan University, Chengdu 610065, China; Provincial Engineering Research Center for Biomaterials Genome of Sichuan, Sichuan University, Chengdu 610065, China.
  • Yan Zhuang
    Medical Psychology Department, Taiyuan Mental Hospital, Taiyuan, China.
  • Ke Chen
    Department of Signal Processing, Tampere University of Technology, Finland.
  • Jian Lu
    Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China.
  • Kefeng Wang
    College of Biomedical Engineering, Sichuan University, Chengdu 610065, China; Provincial Engineering Research Center for Biomaterials Genome of Sichuan, Sichuan University, Chengdu 610065, China; National Engineering Research Centre for Biomaterials, Sichuan University, Chengdu 610065, China.
  • Lin Han
    Department of Radiology Center, The First Affiliated Hospital of Xinxiang Medical University, Xin Xiang, China.
  • Mufei Li
    Department of Computer Science, Xiamen University, Xiamen 361005, China.
  • Xiangfeng Li
    College of Biomedical Engineering, Sichuan University, Chengdu 610065, China; Provincial Engineering Research Center for Biomaterials Genome of Sichuan, Sichuan University, Chengdu 610065, China; National Engineering Research Centre for Biomaterials, Sichuan University, Chengdu 610065, China. Electronic address: hkdlixiangfeng@163.com.
  • Xiangdong Zhu
    College of Biomedical Engineering, Sichuan University, Chengdu 610065, China; Provincial Engineering Research Center for Biomaterials Genome of Sichuan, Sichuan University, Chengdu 610065, China; National Engineering Research Centre for Biomaterials, Sichuan University, Chengdu 610065, China. Electronic address: zhu_xd1973@scu.edu.cn.
  • Mingli Yang
    College of Biomedical Engineering, Sichuan University, Chengdu 610065, China; Provincial Engineering Research Center for Biomaterials Genome of Sichuan, Sichuan University, Chengdu 610065, China; National Engineering Research Centre for Biomaterials, Sichuan University, Chengdu 610065, China.
  • Guangfu Yin
    Department of Biomedical Engineering, College of Materials Science and Engineering, Sichuan University, Chengdu, 610065, China. yingf@scu.edu.cn.
  • Jiangli Lin
    Department of Biomedical Engineering, College of Materials Science and Engineering, Sichuan University, Chengdu, China.
  • Xingdong Zhang
    College of Biomedical Engineering, Sichuan University, Chengdu 610065, China; Provincial Engineering Research Center for Biomaterials Genome of Sichuan, Sichuan University, Chengdu 610065, China; National Engineering Research Centre for Biomaterials, Sichuan University, Chengdu 610065, China.

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