Extended-wavelength diffuse reflectance spectroscopy with a machine-learning method for in vivo tissue classification
(2019) In PLoS ONE 14(10).- Abstract
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... (More)
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.
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- author
- Dahlstrand, Ulf LU ; Sheikh, Rafi LU ; Dybelius Ansson, Cu LU ; Memarzadeh, Khashayar LU ; Reistad, Nina LU and Malmsjö, Malin LU
- organization
- publishing date
- 2019
- type
- Contribution to journal
- publication status
- published
- subject
- in
- PLoS ONE
- volume
- 14
- issue
- 10
- article number
- e0223682
- publisher
- Public Library of Science (PLoS)
- external identifiers
-
- pmid:31600296
- scopus:85073109432
- ISSN
- 1932-6203
- DOI
- 10.1371/journal.pone.0223682
- language
- English
- LU publication?
- yes
- id
- 744fb561-8d4a-4940-8aeb-2f177d17ef35
- date added to LUP
- 2019-10-14 20:00:32
- date last changed
- 2024-08-08 08:15:37
@article{744fb561-8d4a-4940-8aeb-2f177d17ef35, abstract = {{<p>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.</p><p>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.</p><p>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.</p><p>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.</p>}}, author = {{Dahlstrand, Ulf and Sheikh, Rafi and Dybelius Ansson, Cu and Memarzadeh, Khashayar and Reistad, Nina and Malmsjö, Malin}}, issn = {{1932-6203}}, language = {{eng}}, number = {{10}}, publisher = {{Public Library of Science (PLoS)}}, series = {{PLoS ONE}}, title = {{Extended-wavelength diffuse reflectance spectroscopy with a machine-learning method for in vivo tissue classification}}, url = {{http://dx.doi.org/10.1371/journal.pone.0223682}}, doi = {{10.1371/journal.pone.0223682}}, volume = {{14}}, year = {{2019}}, }