Abstract
Laser surgery provides a number of advantages over conventional surgery. However, it implies large risks for sensitive tissue structures due to its characteristic non-tissue-specific ablation. The present study investigates the discrimination of nine different ex vivo tissue types by using uncorrected (raw) autofluorescence spectra for the development of a remote feedback control system for tissue-selective laser surgery. Autofluorescence spectra (excitation wavelength 377 ± 50 nm) were measured from nine different ex vivo tissue types, obtained from 15 domestic pig cadavers. For data analysis, a wavelength range between 450 nm and 650 nm was investigated. Principal Component Analysis (PCA) and Quadratic Discriminant Analysis (QDA) were used to discriminate the tissue types. ROC analysis showed that PCA, followed by QDA, could differentiate all investigated tissue types with AUC results between 1.00 and 0.97. Sensitivity reached values between 93% and 100% and specificity values between 94% and 100%. This ex vivo study shows a high differentiation potential for physiological tissue types when performing autofluorescence spectroscopy followed by PCA and QDA. The uncorrected autofluorescence spectra are suitable for reliable tissue discrimination and have a high potential to meet the challenges necessary for an optical feedback system for tissue-specific laser surgery.
Highlights
The advantages of laser surgery can be diminished by the lack of tactile feedback, which is crucial for controlling the ablation depth during laser surgery
We investigated the discrimination of nine ex vivo tissue types by using the uncorrected autofluorescence spectra in a wavelength range of 450–650 nm
Depending on the cross-validation run, i.e., which pig was excluded when a Principal Component Analysis (PCA) was calculated, 15–30 principal components (PCs) were determined to be optimal for tissue differentiation using Quadratic Discriminant Analysis (QDA)
Summary
The advantages of laser surgery can be diminished by the lack of tactile feedback, which is crucial for controlling the ablation depth during laser surgery. The approach of the present study is the investigation of a fast method for tissue discrimination, for use in a real-time process control for laser surgery. Light scattering is affected by tissue morphology, such as nuclear size, distribution, epithelial thickness and collagen content [8]. These properties are different and specific for each tissue type. To realize real-time process control for laser surgery, it is important to use diagnostic methods that are highly time-efficient. To meet this requirement, uncorrected (raw) autofluorescence spectra were used in this study for an optical, non-contact tissue differentiation
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