Abstract
Near-Infrared Raman Spectroscopy (NIRS) has an excellent potential for a rapid, a less invasive and real time diagnosis of various human diseases. The objective of the present study was to apply NIRS for diagnosis of human heart valves and to develop a feasible algorithm to classify the valvular lesions. For Raman studies, a Ti:sapphire laser pumped by an argon laser provided 830 nm excitation. A spectrograph in conjunction with a liquid N2-cooled CCD detected Raman spectra. A total of 97 fragments of human heart valves were scanned and Raman results were compared with histopathology. Spectra were randomly separated into training and prospective groups. Raman data along with Principal Components Analysis (PCA) and Mahalanobis distance were used to model an algorithm for tissue classification, into two categories: normal (N), and calcified (C) heart valves. It has been found that, for N valves, the algorithm has sensitivities of 95%, 100% and specificities of 100%, 100% for training and prospective groups, respectively. For C valves the algorithm provided sensitivities of 100 and 100% and specificities of 95 and 100% for training and prospective groups, respectively. In conclusion, an algorithm has been developed and successfully applied for NIRS diagnosis of human heart valves with high sensitivities and specificities.