Comparison of red autofluorescing plaque and disclosed plaque—a cross-sectional study

Volgenant, C.M.C.; Mostajo, Mercedes Fernandez y; Rosema, N.A.M.; Weijden, Fridus A. Van der; Cate, J.M. ten; Veen, Monique H. van der

doi:10.1007/s00784-016-1761-z

Cited by 38 publications

(36 citation statements)

References 36 publications

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“…Quantitative light-induced fluorescence (QLF) is known as an imaging technique with the option to visualize present bacterial activity in carious lesions [11,47]. The method is based on autofluorescence of tooth hard tissues and quantifies the autofluorescence difference between sound and carious (pathogenic) enamel.…”

Section: Caries Diagnosismentioning

confidence: 99%

Dental Applications of Optical Coherence Tomography (OCT) in Cariology

et al. 2017

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Across all medical disciplines, therapeutic interventions are based on previously acquired diagnostic information. In cariology, which includes the detection and assessment of the disease "caries" and its lesions, as well as non-invasive to invasive treatment and caries prevention, visual inspection and radiology are routinely used as diagnostic tools. However, the specificity and sensitivity of these standard methods are still unsatisfactory and the detection of defects is often afflicted with a time delay. Numerous novel methods have been developed to improve the unsatisfactory diagnostic possibilities in this specialized medical field. These newer techniques have not yet found widespread acceptance in clinical practice, which might be explained by the generated numerical or color-coded output data that are not self-explanatory. With optical coherence tomography (OCT), an innovative image-based technique has become available that has considerable potential in supporting the routine assessment of teeth in the future. The received cross-sectional images are easy to interpret and can be processed. In recent years, numerous applications of OCT have been evaluated in cariology beginning with the diagnosis of different defects up to restoration assessment and their monitoring, or the visualization of individual treatment steps. Based on selected examples, this overview outlines the possibilities and limitations of this technique in cariology and restorative dentistry, which pertain to the most clinical relevant fields of dentistry.

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Section: Caries Diagnosismentioning

confidence: 99%

Dental Applications of Optical Coherence Tomography (OCT) in Cariology

et al. 2017

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“…Those images were translated into a combined dataset of three colour channels with 216 × 324 raw pixel intensity values in each of them. In total, three different experiments were performed on labels derived from plaque scoring systems such as Red Fluorescent Plaque Percentage (RF-PP) [18], Red Fluorescent modified Quigley-Hein index (RF-mQH) [19] and modified Sillness-Loe Plaque index (mSLP) [20].…”

Section: Datasetmentioning

confidence: 99%

“…Although both intraexaminer and inter-examiner reliability of manual assessment of QLF-images are shown to be high [19], this may become expensive and laborious if the number of images is large. Therefore, there is a need to automate this procedure by implementing a computer-based system for assessment of QLF-images.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks and Machine Learning – ICANN 2017

2017

Lecture Notes in Computer Science

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Abstract. Images are an important data source for diagnosis and treatment of oral diseases. The manual classification of images may lead to misdiagnosis or mistreatment due to subjective errors. In this paper an image classification model based on Convolutional Neural Network is applied to Quantitative Light-induced Fluorescence images. The deep neural network outperforms other state of the art shallow classification models in predicting labels derived from three different dental plaque assessment scores. The model directly benefits from multi-channel representation of the images resulting in improved performance when, besides the Red colour channel, additional Green and Blue colour channels are used.

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“…Additionally, disclosing agents can temporarily stain oral mucosa and the lips, which is a major esthetic issue. Techniques using laserinduced autofluorescence spectroscopy and digital imaging analysis using the HIS color space have also been described in the literature, but equipment cost and technique standardization are major drawbacks to the popularization of such methods [7][8][9]. Thus, there is a need to develop a costeffective and convenient technique to objectively detect and quantify dental plaque.…”

Section: Introductionmentioning

confidence: 99%

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

You

Hao

et al. 2020

Preprint

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Background: The objectives are to design an artificial intelligence (AI) model based on deep learning to detect dental plaque on primary teeth and evaluate the diagnostic accuracy of the AI model. Methods : A conventional neural network (CNN) framework was adopted, and a total of 886 photos of primary teeth taken by an intraoral camera(1280×960 pixels; TPC Ligang, Shenzhen, China) were used to train the AI model. To validate the clinical feasibility, 98 photos of primary teeth taken by the intraoral camera were assessed by the AI model. Additionally, tooth photos were acquired using a digital camera (3216×2136 pixels, Canon EOS 60D, Japan). One experienced pediatric dentist looked at these photos and marked the regions the photos containing dental plaque. After a week, the dentist drew the dental plaque area on the 98 photos taken by the digital camera a second time. In another round of comparison, 102 photos of primary teeth taken by the same intraoral camera to evaluate the diagnostic ability of each approach based on photos with lower-resolution (fewer pixels).The mean intersection-over-union (MIoU) metric was employed to indicate the detection accuracy.Results : The MIoU for the detection of dental plaque on the tested tooth photos was 0.726 ± 0.165.The MIoU of the dentist when diagnosing the 98 photos taken by the digital camera for the first time was 0.695 ± 0.269, while after one week, the MIoU of the dentist was 0.689 ± 0.253. Compared to the dentist, the AI model demonstrated a higher MIoU (0.736 ± 0.174), and the results did not change after a week. When both the dentist and the AI model assessed the 102 photos taken by the intraoral camera. the MIoU of the pediatric dentist was 0.652 ± 0.195, and the MIoU of the AI model was 0.724 ±0.159. The results of a paired t-test found no significant difference between the AI model and human specialist (P > .05) in diagnosing dental plaque on primary teeth.Conclusions : The AI model showed clinically acceptable performance in detecting dental plaque on primary teeth compared with an experienced pediatric dentist.The project was conceptualized by BX, AMH and YW. The project implementation was led by BX and 9 WZY. WZY wrote the first draft of the manuscript. BX and SL read and contributed to several versions of the manuscript. All the authors read and approved the final manuscript.

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Comparison of red autofluorescing plaque and disclosed plaque—a cross-sectional study

Cited by 38 publications

References 36 publications

Dental Applications of Optical Coherence Tomography (OCT) in Cariology

Dental Applications of Optical Coherence Tomography (OCT) in Cariology

Artificial Neural Networks and Machine Learning – ICANN 2017

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

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