A fast and reliable method for semi-automated planimetric quantification of dental plaque in clinical trials

TY - JOUR

T1 - A fast and reliable method for semi-automated planimetric quantification of dental plaque in clinical trials

AU - Rey, Yumi Chokyu Del

AU - Rikvold, Pernille Dukanovic

AU - Johnsen, Karina Kambourakis

AU - Schlafer, Sebastian

N1 - This article is protected by copyright. All rights reserved.

PY - 2023/3

Y1 - 2023/3

N2 - Aim: To develop a simple and reproducible method for semi-automated planimetric quantification of dental plaque. Materials and Methods: Plaque from 20 healthy volunteers was disclosed using erythrosine, and fluorescence images of the first incisors, first premolars, and first molars were recorded after 1, 7, and 14 days of de novo plaque formation. The planimetric plaque index (PPI) was determined using a semi-automated threshold-based image segmentation algorithm and compared with manually determined PPI and the Turesky modification of the Quigley–Hein plaque index (TM-QHPI). The decrease of tooth autofluorescence in plaque-covered areas was quantified as an index of plaque thickness (TI). Data were analysed by analysis of variance (ANOVA) and Pearson correlations. Results: The high contrast between teeth, disclosed plaque, and soft tissues in fluorescence images allowed for a fast threshold-based image segmentation. Semi-automated PPI is strongly correlated with manual planimetry (r = 0.92; p <.001) and TM-QHPI recordings (r = 0.88; p <.001), and may exhibit a higher discriminatory power than TM-QHPI due to its continuous scale. TI values corresponded to optically perceived plaque thickness, and no differences were observed over time (p >.05, ANOVA). Conclusions: The proposed semi-automated planimetric analysis based on fluorescence images is a simple and efficient method for dental plaque quantification in multiple images with reduced human input.

AB - Aim: To develop a simple and reproducible method for semi-automated planimetric quantification of dental plaque. Materials and Methods: Plaque from 20 healthy volunteers was disclosed using erythrosine, and fluorescence images of the first incisors, first premolars, and first molars were recorded after 1, 7, and 14 days of de novo plaque formation. The planimetric plaque index (PPI) was determined using a semi-automated threshold-based image segmentation algorithm and compared with manually determined PPI and the Turesky modification of the Quigley–Hein plaque index (TM-QHPI). The decrease of tooth autofluorescence in plaque-covered areas was quantified as an index of plaque thickness (TI). Data were analysed by analysis of variance (ANOVA) and Pearson correlations. Results: The high contrast between teeth, disclosed plaque, and soft tissues in fluorescence images allowed for a fast threshold-based image segmentation. Semi-automated PPI is strongly correlated with manual planimetry (r = 0.92; p <.001) and TM-QHPI recordings (r = 0.88; p <.001), and may exhibit a higher discriminatory power than TM-QHPI due to its continuous scale. TI values corresponded to optically perceived plaque thickness, and no differences were observed over time (p >.05, ANOVA). Conclusions: The proposed semi-automated planimetric analysis based on fluorescence images is a simple and efficient method for dental plaque quantification in multiple images with reduced human input.

KW - dental plaque index

KW - digital image processing

KW - light-induced fluorescence

KW - planimetry

KW - Erythrosine

KW - Reproducibility of Results

KW - Humans

KW - Dental Plaque/diagnostic imaging

KW - Dental Plaque Index

KW - Incisor

U2 - 10.1111/jcpe.13745

DO - 10.1111/jcpe.13745

M3 - Journal article

C2 - 36345833

SN - 0303-6979

VL - 50

SP - 331

EP - 338

JO - Journal of Clinical Periodontology

JF - Journal of Clinical Periodontology

IS - 3

ER -