Extended-wavelength diffuse reflectance spectroscopy with a machine-learning method for in vivo tissue classification
Research output: Contribution to journal › Article
OBJECTIVES: An extended-wavelength diffuse reflectance spectroscopy (EWDRS) technique was evaluated for its ability to differentiate between and classify different skin and tissue types in an in vivo pig model.
MATERIALS AND METHODS: EWDRS recordings (450-1550 nm) were made on skin with different degrees of pigmentation as well as on the pig snout and tongue. The recordings were used to train a support vector machine to identify and classify the different skin and tissue types.
RESULTS: The resulting EWDRS curves for each skin and tissue type had a unique profile. The support vector machine was able to classify each skin and tissue type with an overall accuracy of 98.2%. The sensitivity and specificity were between 96.4 and 100.0% for all skin and tissue types.
CONCLUSION: EWDRS can be used in vivo to differentiate between different skin and tissue types with good accuracy. Further development of the technique may potentially lead to a novel diagnostic tool for e.g. non-invasive tumor margin delineation.
|Research areas and keywords||
Subject classification (UKÄ) – MANDATORY
|Publication status||Published - 2019|
Related research output
Research output: Thesis › Doctoral Thesis (compilation)