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Extended-wavelength diffuse reflectance spectroscopy with a machine-learning method for in vivo tissue classification

Dahlstrand, Ulf LU ; Sheikh, Rafi LU orcid ; Dybelius Ansson, Cu LU ; Memarzadeh, Khashayar LU ; Reistad, Nina LU orcid and Malmsjö, Malin LU orcid (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
; ; ; ; and
organization
publishing date
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
2025-10-14 12:37:39
@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}},
}