The global medical implants market is currently valued at over €80 bn and is expected to exceed €120 bn by 2023, as millions of citizens receive implanted medical devices to improve their quality of life. Manufacturing errors, giving rise to imperceptible faults in the resultant biomaterials, can ultimately lead to complications, particularly in the case of implants. Failure of a medical implant results in a major economic burden to manufacturers, resulting in product recalls, reduction of market share and litigation, not to speak of the often debilitating impact on the patient’s quality of life. The current standard for biomaterial quality evaluation takes a point spectroscopic approach, making all-or-nothing decisions about the viability of a material based on the collection of a chemical spectrum from a single point on the material or the average of several points. However, using spectral imaging techniques we observed substantial heterogeneity across the surfaces of both pre-implant and explanted medical devices (removed from the spine after failure). However, the attenuated total reflectance Infrared (ATR-IR) and Raman chemical imaging techniques, while very informative, suffer from high set-up costs and difficulties with high-speed data acquisition and processing. These challenges have prevented their widespread adoption in industry. Moreover, these techniques do not facilitate straightforward analysis of the entire sample. Consequently, there is a need for a novel approach to provide chemical analysis of the entire biomaterial sample area in a reasonable time at a competitive cost.
An innovative system comprising (1) Vis-NIR spectral imaging instrumentation, (2) sample platform and (3) data driven algorithms for biomaterial quality prediction has been developed for biomaterial quality control. The proposed solution is rapid, cost effective and significantly more accurate than existing techniques. The databases and algorithms developed in this project also have innovation potential as they represent new information on biomaterial quality characterisation with significant industrial relevance.
