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

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Year : 2026 | Volume : 14 | 02 | Page :
    By

    T. Suguna,

  • G. Umashankar,

  • B. Rampriya,

  • M. Rajesh Kumar,

  • P. Venkatesh,

  • T Venkatakrishnamoorthy,

  1. Associate Professor, Department of Electronics and Communication Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India
  2. Associate Professor, Department of Biomedical Engineering, GRT Institute of Engineering and Technology, Tiruttani, Tamil Nadu, India
  3. Associate Professor, Department of Electrical and Electronics Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India
  4. Tutor, Mahatma Gandhi Medical Advanced Research Institute, Sri Balaji Vidyapeeth University, Pondicherry, India
  5. Assistant Professor, Department of Electrical and Electronics Engineering, School of Engineering, Mohan Babu University, Tirupati, Tamil Nadu, India
  6. Professor, Department of Electronics and Communication Engineering, Sasi Institute of Technology and Engineering, Tadepallegudem Krishna District, Andhra Pradesh, India

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: Polymer degradation; Medical devices; Multimodal machine learning; Natural language processing; Degradation trajectory analysis

How to cite this article:
T. Suguna, G. Umashankar, B. Rampriya, M. Rajesh Kumar, P. Venkatesh, T Venkatakrishnamoorthy. Quantitative Image-Based Assessment of Degradation Patterns in Polymer-Based Medical Implants. Journal of Polymer & Composites. 2026; 14(02):-.
How to cite this URL:
T. Suguna, G. Umashankar, B. Rampriya, M. Rajesh Kumar, P. Venkatesh, T Venkatakrishnamoorthy. Quantitative Image-Based Assessment of Degradation Patterns in Polymer-Based Medical Implants. Journal of Polymer & Composites. 2026; 14(02):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=242533


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Ahead of Print Subscription Original Research
Volume 14
02
Received 25/02/2026
Accepted 14/03/2026
Published 01/05/2026
Publication Time 65 Days
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