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

2019; Optica Publishing Group; Volume: 10; Issue: 8 Linguagem: Inglês

10.1364/boe.10.003889

2156-7085

Rajitha Gunaratne, Isaac Monteath, Joshua Goncalves, Raymond Sheh, C. N. Ironside, Michael Kapfer, Richard Chipper, W.B. Robertson, Riaz Ahmed Khan, Daniel P. Fick,

Infrared Thermography in Medicine

Objective: 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. Methods: Supervised machine learning analysis of diffuse reflectance spectra was performed to classify human joint tissue that was collected from surgical procedures. Results: 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 Conclusion: DRS is a viable method for differentiating human joint tissue and has the potential to be incorporated into robotic orthopaedic surgery.