Near-Infrared Imaging for Detecting Caries and Structural Deformities in Teeth

Angelino, Keith; Edlund, David; Shah, Pratik

doi:10.1109/jtehm.2017.2695194

Cited by 29 publications

(15 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 11 , 12 Many existing studies indicate that near-infrared (NIR) light looks more promising than visible light for the development of novel dental optical imaging techniques. 13 – 18 As the only Food and Drug Administration (FDA)-approved NIR fluorescence dye for clinical usage in fluorescent angiography, indocyanine green has been widely used in many other fields, like cancer or dental imaging. 11 , 19 – 27 Our previous studies have successfully demonstrated ICG-NIR fluorescence (ICG-NIRF) dental imaging in both human-extracted tooth and rat models.…”

Section: Introductionmentioning

confidence: 99%

Mouthwash as a non-invasive method of indocyanine green delivery for near-infrared fluorescence dental imaging

Zaid

et al. 2022

J. Biomed. Opt.

View full text Add to dashboard Cite

. Significance: X-ray imaging serves as the mainstream imaging in dentistry, but it involves risk of ionizing radiation. Aim: This study presents the feasibility of indocyanine green-assisted near-infrared fluorescence (ICG-NIRF) dental imaging with 785-nm NIR laser in the first (ICG-NIRF-I: 700 to 1000 nm) and second (ICG-NIRF-II: 1000 to 1700 nm) NIR wavelengths. Approach: Sprague Dawley rats with different postnatal days were used as animal models. ICG, as a fluorescence agent, was delivered to dental structures by subcutaneous injection (SC) and oral administration (OA). Results: For SC method, erupted and unerupted molars could be observed from ICG-NIRF images at a short imaging time ( ). ICG-NIRF-II could achieve a better image contrast in unerupted molars at 24 h after ICG injection. The OA could serve as a non-invasive method for ICG delivery; it could also cause the glow-in-dark effect in unerupted molars. For erupted molars, OA can be considered as mouthwash and exhibits outstanding performance for delivery of ICG dye; erupted molar structures could be observed at a short imaging time ( ) and low ICG dose ( ). Conclusions: Overall, ICG-NIRF with mouthwash could perform in-vivo dental imaging in two NIR wavelengths at a short time and low ICG dose.

show abstract

Section: Introductionmentioning

confidence: 99%

Mouthwash as a non-invasive method of indocyanine green delivery for near-infrared fluorescence dental imaging

Zaid

et al. 2022

J. Biomed. Opt.

View full text Add to dashboard Cite

show abstract

“…Other research has detailed an approach using an RGB oral endoscope image [8,9]; however, most approaches cannot provide a detailed structure of the tooth, especially the tooth root, and therefore struggle to provide caries diagnosis. Compared to oral endoscope imaging, dental radiographs provide greater image quality and detect detailed structure deformity in the tooth [10]; therefore, the dental radiographic image is the most well-known approach, and is preferable for the detection of caries in the early stage.…”

Section: Introductionmentioning

confidence: 99%

Deep Fusion Feature Extraction for Caries Detection on Dental Panoramic Radiographs

2021

View full text Add to dashboard Cite

Caries is the most well-known disease and relates to the oral health of billions of people around the world. Despite the importance and necessity of a well-designed detection method, studies in caries detection are still limited and show a restriction in performance. In this paper, we proposed a computer-aided diagnosis (CAD) method to detect caries among normal patients using dental radiographs. The proposed method mainly consists of two processes: feature extraction and classification. In the feature extraction phase, the chosen 2D tooth image was employed to extract deep activated features using a deep pre-trained model and geometric features using mathematic formulas. Both feature sets were then combined, called fusion feature, to complement each other defects. Then, the optimal fusion feature set was fed into well-known classification models such as support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), Naïve Bayes (NB), and random forest (RF) to determine the best classification model that fit the fusion features set and perform the most preeminent result. The results show 91.70%, 90.43%, and 92.67% for accuracy, sensitivity, and specificity, respectively. The proposed method has outperformed the previous state-of-the-art and shows promising results when none of the measured factors is less than 90%; therefore, the method is promising for dentists and capable of wide-scale implementation caries detection in hospitals.

show abstract

“…In the recent times, the medical image processing provides various detection techniques for detecting the dental caries in an early stage [1]. The dental decay or tooth decay is generally referred to as dental caries, and this disease takes place in the hard tissue of the tooth due to the action of the micro-organisms.…”

Section: Introductionmentioning

confidence: 99%

“…When the dental caries is larger in size, they can be viewed by naked eye, whereas, smaller lesions are difficult to identify. In the primary level of diagnosis, all the tooth surfaces of the patient are inspected by utilizing a light source, dental mirror and explorer [5, 6, 7]. Most of the diagnosis is made with the Dental radiographs (X-rays), and they are efficient in exhibiting the dental caries that takes place between the teeth [19, 20].…”

Section: Introductionmentioning

confidence: 99%

“…As per tabulated in Table 1 many researchers contributed their valuable insights in the field of dental caries detection using image processing tools likewise in 2017, Angelino et al. [1] proffered NIR (850 nm) LED imaging system by interlinking NIR source as well as intraoral camera in order to evaluate the low sensitivity regions of the teeth. There is non-ionizing and safe approach in the range of NIR, while detecting the translucency of the teeth.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Algorithmic analysis for dental caries detection using an adaptive neural network architecture

Patil

Kulkarni

Bhise

2019

Heliyon

View full text Add to dashboard Cite

Objectives AI techniques have lifelong impact in biomedics and widely accepted outcomes. The sole objective of the study is to evaluate accurate detection of caries using feature extraction and classification of the dental images along with amalgamation Adaptive Dragonfly algorithm (DA) algorithm and Neural Network (NN) classifier. Materials and methods Here proposed caries detection model is designed for detecting the tooth cavities in an accurate manner. This methodology has two main phases; feature extraction and classification. In all total 120 images database is split into three sets, randomly for evaluating the performance. Further, this categorization of the test cases aids in ensuring the enhancement of the performance. In each of the test cases, there are 40 caries images the investigation in the performance of the proposed caries detection model was done in terms of accuracy, sensitivity, specificity, and precision, FPR, FNR, NPV, FDR, F1Score and MCC. Results Here MPCA with Nonlinear Programming and Adaptive DA, the proposed model is termed as MNP-ADA. The performance of the proposed MPCA-ADA model is evaluated by comparing it over the other existing feature extraction models. MPCA–ADA over the conventional classifier models like PCA-ADA, LDA-ADA and ICA-ADA in terms of performance parameters and MCC for all the test types and have superior results than the existing ones. Conclusion The research work emphasizes the prospective efficacy of IP and NN algorithms for the detection and diagnosis of dental caries. A novel and improved model shows substantially worthy performance in distinguishing dental caries using image processing techniques.

show abstract

Near-Infrared Imaging for Detecting Caries and Structural Deformities in Teeth

Cited by 29 publications

References 28 publications

Mouthwash as a non-invasive method of indocyanine green delivery for near-infrared fluorescence dental imaging

Mouthwash as a non-invasive method of indocyanine green delivery for near-infrared fluorescence dental imaging

Deep Fusion Feature Extraction for Caries Detection on Dental Panoramic Radiographs

Algorithmic analysis for dental caries detection using an adaptive neural network architecture

Contact Info

Product

Resources

About