BACKGROUND AND OBJECTIVE: Laser surgery does not provide haptic feedback for operating layer-by-layer and thereby preserving vulnerable anatomical structures like nerve tissue or blood vessels. Diffuse reflectance spectra can facilitate remote optical tissue differentiation. It is the aim of the study to use this technique on soft tissue samples, to set a technological basis for a remote optical feedback system for tissue-specific laser surgery. MATERIALS AND METHODS: Diffuse reflectance spectra (wavelength range: 350-650 nm) of ex vivo types of soft tissue (a total of 10,800 spectra) of the midfacial region of domestic pigs were remotely measured under reduced environmental light conditions and analyzed in order to differentiate between skin, mucosa, muscle, subcutaneous fat, and nerve tissue. We performed a principal components (PC) analysis (PCA) to reduce the number of variables. Linear discriminant analysis (LDA) was utilized for classification. For the tissue differentiation, we calculated the specificity and sensitivity by receiver operating characteristic (ROC) analysis and the area under curve (AUC). RESULTS: Six PCs were found to be adequate for tissue differentiation with diffuse reflectance spectra using LDA. All of the types of soft tissue could be differentiated with high specificity and sensitivity. Only the tissue pairs nervous tissue/fatty tissue and nervous tissue/mucosa showed a decline of differentiation due to bio-structural similarity. However, both of these tissue pairs could still be differentiated with a specificity and sensitivity of more than 90%. CONCLUSIONS: Analyzing diffuse reflectance spectroscopy with PCA and LDA allows for remote differentiation of biological tissue. Considering the limitations of the ex vivo conditions, the obtained results are promising and set a basis for the further development of a feedback system for tissue-specific laser surgery. (c) 2010 Wiley-Liss, Inc.
BACKGROUND AND OBJECTIVE: Laser surgery does not provide haptic feedback for operating layer-by-layer and thereby preserving vulnerable anatomical structures like nerve tissue or blood vessels. Diffuse reflectance spectra can facilitate remote optical tissue differentiation. It is the aim of the study to use this technique on soft tissue samples, to set a technological basis for a remote optical feedback system for tissue-specific laser surgery. MATERIALS AND METHODS: Diffuse reflectance spectra (wavelength range: 350-650 nm) of ex vivo types of soft tissue (a total of 10,800 spectra) of the midfacial region of domestic pigs were remotely measured under reduced environmental light conditions and analyzed in order to differentiate between skin, mucosa, muscle, subcutaneous fat, and nerve tissue. We performed a principal components (PC) analysis (PCA) to reduce the number of variables. Linear discriminant analysis (LDA) was utilized for classification. For the tissue differentiation, we calculated the specificity and sensitivity by receiver operating characteristic (ROC) analysis and the area under curve (AUC). RESULTS: Six PCs were found to be adequate for tissue differentiation with diffuse reflectance spectra using LDA. All of the types of soft tissue could be differentiated with high specificity and sensitivity. Only the tissue pairs nervous tissue/fatty tissue and nervous tissue/mucosa showed a decline of differentiation due to bio-structural similarity. However, both of these tissue pairs could still be differentiated with a specificity and sensitivity of more than 90%. CONCLUSIONS: Analyzing diffuse reflectance spectroscopy with PCA and LDA allows for remote differentiation of biological tissue. Considering the limitations of the ex vivo conditions, the obtained results are promising and set a basis for the further development of a feedback system for tissue-specific laser surgery. (c) 2010 Wiley-Liss, Inc.
Authors: Sandra Pampín-Suárez; José Luis Arce-Diego; Olga Tapia; Flor María Pérez-Campo; José Carlos Rodríguez-Rey; Félix Fanjul-Vélez Journal: Biomed Opt Express Date: 2021-12-07 Impact factor: 3.732
Authors: Alexander Engelhardt; Rajesh Kanawade; Christian Knipfer; Matthias Schmid; Florian Stelzle; Werner Adler Journal: BMC Med Res Methodol Date: 2014-07-16 Impact factor: 4.615