Automated classification of tissue by type using real-time spectroscopy

Each tissue type has a unique spectral signature (e.g. liver looks distinct from bowel due to differences in both absorbance and in the way the tissue scatters light). While differentiation between normal tissues and tumors is not trivial, automated discrimination among normal tissue types (e.g. nerve, artery, vein, muscle) is feasible and clinically important, as many medical errors in medicine involve the misidentification of normal tissues. In this study, we have found that spectroscopic differentiation of tissues can be successfully applied to tissue samples (kidney and uterus) and model systems (fruit). Such optical techniques may usher in use of optical tissue diagnosis, leading to automated and portable diagnostic devices which can identify tissues, and guide use of medical instruments, such as during ablation or biopsy.