Literature DB >> 25858484

Detection and quantification of dental plaque based on laser-induced autofluorescence intensity ratio values.

Betsy Joseph1, Chandra Sekhar Prasanth2, Jayaraj L Jayanthi3, Janam Presanthila1, Narayanan Subhash4.   

Abstract

The aim of this study was to evaluate the applicability of laser-induced autofluorescence (LIAF) spectroscopy to detect and quantify dental plaque. LIAF spectra were recorded in situ from dental plaque (0–3 grades of plaque index) in 300 patients with 404 nm diode laser excitation. The fluorescence intensity ratio of the emission peaks was calculated from the LIAF spectral data following which their scatter plots were drawn and the area under the receiver operating characteristics were calculated. The LIAF spectrum of clinically invisible grade-1 plaque showed a prominent emission peak at 510 nm with a satellite peak around 630 nm in contrast to grade 0 that has a single peak around 500 nm. The fluorescence intensity ratio (F510/F630) has a decreasing trend with increase in plaque grade and the ratio values show statistically significant differences (p<0.01) between different grades. An overall sensitivity and specificity of 100% each was achieved for discrimination between grade-0 and grade-1 plaque. The clinical significance of this study is that the diagnostic algorithm developed based on fluorescence spectral intensity ratio (F510/F630) would be useful to precisely identify minute amounts of plaque without the need for disclosing solutions and to convince patients of the need for proper oral hygiene and homecare practices.

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Mesh:

Year:  2015        PMID: 25858484     DOI: 10.1117/1.JBO.20.4.048001

Source DB:  PubMed          Journal:  J Biomed Opt        ISSN: 1083-3668            Impact factor:   3.170


  4 in total

1.  Diagnosis and staging of caries using spectral factors derived from the blue laser-induced autofluorescence spectrum.

Authors:  Ching-Chang Ko; Dong-Ho Yi; Dong Joon Lee; Jane Kwon; Franklin Garcia-Godoy; Yong Hoon Kwon
Journal:  J Dent       Date:  2017-10-06       Impact factor: 4.379

2.  Clinical validation and assessment of a modular fluorescent imaging system and algorithm for rapid detection and quantification of dental plaque.

Authors:  Keith Angelino; Pratik Shah; David A Edlund; Mrinal Mohit; Gregory Yauney
Journal:  BMC Oral Health       Date:  2017-12-28       Impact factor: 2.757

3.  Deep learning-based dental plaque detection on primary teeth: a comparison with clinical assessments.

Authors:  Wenzhe You; Aimin Hao; Shuai Li; Yong Wang; Bin Xia
Journal:  BMC Oral Health       Date:  2020-05-13       Impact factor: 2.757

4.  Autofluorescence Detection Method for Dental Plaque Bacteria Detection and Classification: Example of Porphyromonas gingivalis, Aggregatibacter actinomycetemcomitans, and Streptococcus mutans.

Authors:  Yung-Jhe Yan; Bo-Wen Wang; Chih-Man Yang; Ching-Yi Wu; Mang Ou-Yang
Journal:  Dent J (Basel)       Date:  2021-06-22
  4 in total

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