Machine learning classification of human joint tissue from diffuse reflectance spectroscopy data

Abstract

<i>Objective:</i> To assess if incorporation of DRS sensing into real-time robotic surgery systems has merit. DRS as a technology is relatively simple, cost-effective and provides a non-contact approach to tissue differentiation. <i>Methods:</i> Supervised machine learning analysis of diffuse reflectance spectra was performed to classify human joint tissue that was collected from surgical procedures. <i>Results:</i> We have used supervised machine learning in the classification of a DRS human joint tissue data set and achieved classification accuracy in excess of 99%. Sensitivity for the various classes were; cartilage 99.7%, subchondral 99.2%, meniscus 100% and cancellous 100%. Full wavelength range is required for maximum classification accuracy. The wavelength resolution must be larger than 8nm. A SNR better than 10:1 was required to achieve a classification accuracy greater than 50%. The 800-900nm wavelength range gave the greatest accuracy amongst those investigated <i>Conclusion:</i> DRS is a viable method for differentiating human joint tissue and has the potential to be incorporated into robotic orthopaedic surgery.