STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Polymer & Composites Original Research Special issue

Quantitative Image-Based Assessment of Degradation Patterns in Polymer-Based Medical Implants

  1. T. Suguna Department of Electronics and Communication Engineering, Panimalar Engineering College, Chennai
  2. G. Umashankar Department of Biomedical Engineering, GRT Institute of Engineering and Technology, Tiruttani
  3. B. Rampriya Department of Electrical and Electronics Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai
  4. M. Rajesh Kumar Mahatma Gandhi Medical Advanced Research Institute, Sri Balaji Vidyapeeth University
  5. P. Venkatesh Department of Electrical and Electronics Engineering, School of Engineering, Mohan Babu University, Tirupati
  6. T Venkatakrishnamoorthy Department of Electronics and Communication Engineering, Sasi Institute of Technology and Engineering, Tadepallegudem Krishna District

Abstract

Polymer-based medical devices are widely used in clinical practice, where long-term material degradation can compromise performance and patient safety. Traditional polymer degradation studies predominantly rely on laboratory-based experiments, which often fail to capture real-world operational and usage conditions. In this study, a multimodal, data-driven framework is proposed for the quantitative assessment of degradation patterns in polymer-based medical devices using publicly available clinical failure data. Structured operational parameters, including cumulative usage hours, device age, and downtime characteristics, are integrated with unstructured maintenance narratives through natural language processing techniques. Text-derived degradation indicators are extracted using term-frequency inverse document-frequency representations and topic modelling, enabling the identification of latent material degradation phenomena, such as surface wear and cracking. These indicators are fused with structured features and analysed using supervised machine learning and survival modelling to predict degradation-related failures and assess degradation trajectories. Experimental results demonstrate that multimodal models significantly outperform structured-only approaches, achieving an area under the receiver operating characteristic curve of up to 0.91. Feature importance and hazard ratio analyses confirm the material relevance of text-derived degradation indicators and their strong association with failure risk. The proposed methodology provides an interpretable and scalable approach for post-market assessment of polymer degradation in medical devices, complementing conventional material characterisation techniques and supporting data-driven lifecycle management.

Keywords

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