Automatic detection and classification of dental fluorosis in vivo using white light and fluorescence imagingCitation formats

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Automatic detection and classification of dental fluorosis in vivo using white light and fluorescence imaging. / Liu, Zhao; Goodwin, Michaela; Ellwood, Roger P.; Pretty, Iain A.; McGrady, Michael.

In: Journal of Dentistry, Vol. 74, No. S1, 07.2018, p. S34-S41.

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Liu, Zhao ; Goodwin, Michaela ; Ellwood, Roger P. ; Pretty, Iain A. ; McGrady, Michael. / Automatic detection and classification of dental fluorosis in vivo using white light and fluorescence imaging. In: Journal of Dentistry. 2018 ; Vol. 74, No. S1. pp. S34-S41.

Bibtex

@article{89ec5b933ccf4fc8a8162233cdca8416,
title = "Automatic detection and classification of dental fluorosis in vivo using white light and fluorescence imaging",
abstract = "Objectives: To assess a novel method of automatic fluorosis detection and classification from white light and fluorescent images. Methods: Dental images from 1,729 children living in two fluoridates and two non-fluoridated UK cities were utilised. A novel detection and classification algorithm was applied to each image and TF scores were obtained using thresholding criteria. These were compared to clinical reference standard images. Comparisons between reference and automated assessments were undertaken to record correct and incorrect classifications and the ability of the system to separate the fluoridated and non-fluoridated populations. Results: The automated system performed well and was able to differentiate the two populations (P < 0.0001) to the same degree as the reference standard. When using the highest score from the clinical assessment the agreement between automated and clinical assessments was 0.56 (Kappa SE = 0.0160, p < 0.0001). Conclusions: Assessment of dental fluorosis is typically undertaken by clinical examiners in epidemiological studies. The training and calibration of such examiners is complex and time consuming and the assessments are subject to bias – frequently because of the examiner's awareness of the water fluoridation status of subjects. The use of remote scoring using photographs has been advocated but still requires trained examiners. This study has shown that image-processing methodologies applied to white light and fluorescent images could automatically score fluorosis and statistically separate fluoridated and non-fluoridated areas. The system requires further refinement to manage confounding factors such as the presence of non-fluoride opacities and tooth stain.",
keywords = "Automation, Detection, Fluorosis, TF index",
author = "Zhao Liu and Michaela Goodwin and Ellwood, {Roger P.} and Pretty, {Iain A.} and Michael McGrady",
year = "2018",
month = "7",
doi = "10.1016/j.jdent.2018.04.021",
language = "English",
volume = "74",
pages = "S34--S41",
journal = "Journal of Dentistry",
issn = "0300-5712",
publisher = "Elsevier BV",
number = "S1",

}

RIS

TY - JOUR

T1 - Automatic detection and classification of dental fluorosis in vivo using white light and fluorescence imaging

AU - Liu, Zhao

AU - Goodwin, Michaela

AU - Ellwood, Roger P.

AU - Pretty, Iain A.

AU - McGrady, Michael

PY - 2018/7

Y1 - 2018/7

N2 - Objectives: To assess a novel method of automatic fluorosis detection and classification from white light and fluorescent images. Methods: Dental images from 1,729 children living in two fluoridates and two non-fluoridated UK cities were utilised. A novel detection and classification algorithm was applied to each image and TF scores were obtained using thresholding criteria. These were compared to clinical reference standard images. Comparisons between reference and automated assessments were undertaken to record correct and incorrect classifications and the ability of the system to separate the fluoridated and non-fluoridated populations. Results: The automated system performed well and was able to differentiate the two populations (P < 0.0001) to the same degree as the reference standard. When using the highest score from the clinical assessment the agreement between automated and clinical assessments was 0.56 (Kappa SE = 0.0160, p < 0.0001). Conclusions: Assessment of dental fluorosis is typically undertaken by clinical examiners in epidemiological studies. The training and calibration of such examiners is complex and time consuming and the assessments are subject to bias – frequently because of the examiner's awareness of the water fluoridation status of subjects. The use of remote scoring using photographs has been advocated but still requires trained examiners. This study has shown that image-processing methodologies applied to white light and fluorescent images could automatically score fluorosis and statistically separate fluoridated and non-fluoridated areas. The system requires further refinement to manage confounding factors such as the presence of non-fluoride opacities and tooth stain.

AB - Objectives: To assess a novel method of automatic fluorosis detection and classification from white light and fluorescent images. Methods: Dental images from 1,729 children living in two fluoridates and two non-fluoridated UK cities were utilised. A novel detection and classification algorithm was applied to each image and TF scores were obtained using thresholding criteria. These were compared to clinical reference standard images. Comparisons between reference and automated assessments were undertaken to record correct and incorrect classifications and the ability of the system to separate the fluoridated and non-fluoridated populations. Results: The automated system performed well and was able to differentiate the two populations (P < 0.0001) to the same degree as the reference standard. When using the highest score from the clinical assessment the agreement between automated and clinical assessments was 0.56 (Kappa SE = 0.0160, p < 0.0001). Conclusions: Assessment of dental fluorosis is typically undertaken by clinical examiners in epidemiological studies. The training and calibration of such examiners is complex and time consuming and the assessments are subject to bias – frequently because of the examiner's awareness of the water fluoridation status of subjects. The use of remote scoring using photographs has been advocated but still requires trained examiners. This study has shown that image-processing methodologies applied to white light and fluorescent images could automatically score fluorosis and statistically separate fluoridated and non-fluoridated areas. The system requires further refinement to manage confounding factors such as the presence of non-fluoride opacities and tooth stain.

KW - Automation

KW - Detection

KW - Fluorosis

KW - TF index

UR - http://www.scopus.com/inward/record.url?scp=85047060220&partnerID=8YFLogxK

U2 - 10.1016/j.jdent.2018.04.021

DO - 10.1016/j.jdent.2018.04.021

M3 - Article

AN - SCOPUS:85047060220

VL - 74

SP - S34-S41

JO - Journal of Dentistry

JF - Journal of Dentistry

SN - 0300-5712

IS - S1

ER -