Predictive Modeling of Polymer Composites for Medical Implants Using Artificial Intelligence Techniques

Year : 2025 | Volume : 13 | Special Issue 06 | Page : 665 692
    By

    Samuthira Pandi V,

  • Aishwarya D,

  • Helina Rajini Suresh,

  • B. Thilakavathi,

  • Tupili Sangeetha,

  • Veeraiyah Thangasamy,

  • A. Anandh,

  1. Research Head, Centre for Advanced Wireless Integrated Technology, Chennai Institute of Technology, Chennai, Tamil Nadu, India
  2. Assistant Professor, Department of Computer science and Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
  3. Associate Professor, Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India
  4. Professor, Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, Thandalam, Chennai, Tamil Nadu, India
  5. Associate Professor, Department of Computer Science and Engineering, R. M. D Engineering College, R. S. M Nagar, Kavaraipettai, Chennai, Tamil Nadu, India
  6. Professor, Department of Electronics and Communication Engineering, V.S.B. Engineering College, Karur, Chennai, Tamil Nadu, India
  7. Associate Professor, Department of Computer science and Engineering, Kamaraj College of Engineering and Technology, Virudhunagar, Chennai, Tamil Nadu, India

Abstract

The use of polymers in biomaterials was now key to designing the next generation of medical implants, which need to be strong and also compatible with living tissue. Tests for biocompatibility, such as those done in the laboratory and by doing experiments on animals, require much time and many resources, so the need for computer-based approaches becomes clear. An artificial intelligence approach was provided in this study to determine how polymer composites are biocompatible according to main physical, chemical, and mechanical factors. A synthetic clinical dataset made up of surface roughness, elastic modulus, degradation rate, hydrophilicity, tensile strength, and cytotoxicity was created and examined. During data preprocessing, I normalized the data, coded all the categorical variables, and imputed the missing entries. The research team used support vector machines and ensemble methods to classify biological compatibility and tested convolutional neural networks (CNNs) on the images of cell morphology. A combination of feature importance analysis, correlation maps, and visual analytics allowed us to find out which attributes guide cellular interactions the most. These models were found to be reliable by measuring accuracy, precision, recall, and ROC-AUC. The results confirmed that AI systems could model the complex ways in which materials affect biological results. Making sure data was correct, algorithms could be understood, and their results interpretable helped ensure matter in clinical practice. This study shows that machine learning can help in designing better biomaterials, speeding up experiments, and promoting safer implants for different needs in regenerative medicine.

Keywords: Polymer matrix composites, AI-assisted material design, biomedical polymers, smart composites, artificial intelligence in biopolymers.

[This article belongs to Special Issue under section in Journal of Polymer and Composites (jopc)]

How to cite this article:
Samuthira Pandi V, Aishwarya D, Helina Rajini Suresh, B. Thilakavathi, Tupili Sangeetha, Veeraiyah Thangasamy, A. Anandh. Predictive Modeling of Polymer Composites for Medical Implants Using Artificial Intelligence Techniques. Journal of Polymer and Composites. 2025; 13(06):665-692.
How to cite this URL:
Samuthira Pandi V, Aishwarya D, Helina Rajini Suresh, B. Thilakavathi, Tupili Sangeetha, Veeraiyah Thangasamy, A. Anandh. Predictive Modeling of Polymer Composites for Medical Implants Using Artificial Intelligence Techniques. Journal of Polymer and Composites. 2025; 13(06):665-692. Available from: https://journals.stmjournals.com/jopc/article=2025/view=216175


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Special Issue Subscription Original Research
Volume 13
Special Issue 06
Received 20/05/2025
Accepted 26/06/2025
Published 07/07/2025
Publication Time 48 Days
490

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