Differentiation of femur bone from surrounding soft tissue using laser-induced breakdown spectroscopy as a feedback system for smart laserosteotomy

Although laserosteotomes have become generally accepted devices in surgical applications, they still suffer from a lack of information about the type of tissue currently being ablated; as a result, critical structures of the body under or near the focal spot of the laser beam are prone to inadvertent ablation. The lack of information about the properties of the ablated tissue can be solved by connecting the laserosteotome to an optical detection setup which can differentiate various types of tissues, especially bone from connective soft tissues. This study examines the applicability of laser-induced breakdown spectroscopy (LIBS) as a potential technique to differentiate bone from surrounding soft tissue (fat and muscle). In this experiment, fresh porcine femur bone, muscle, and fat were used as hard and soft tissue samples. The beam of a nanosecond frequency-doubled Nd:YAG laser was used to ablate the tissue samples and generate the plasma. The plasma light emitted from the ablated spot, which corresponds to the recombination spectra of ionized atoms and molecules, was gathered with a collection optic (including a reflective light collector and a fiber optic) and sent to an Echelle spectrometer for resolving the atomic composition of the ablated sample. Afterwards, Discriminant Function Analysis (DFA) based on the ratio of the intensity of selected peak pairs was performed to classify three sample groups (bone, muscle, and fat). Lastly, the sensitivity, specificity, and accuracy of the proposed method were calculated. Sensitivity and specificity of 100 % and 99 % were achieved, respectively, to differentiate bone from surrounding soft tissue.