High-Frequency Ultrasound Imaging for Examination of Early Dental Caries

Overview of Ultrasound in Dentistry for Advancing Research Methodology and Patient Care Quality with Emphasis on Periodontal/Peri-implant Applications

BACKGROUND

Ultrasound is a non-invasive, cross-sectional imaging technique emerging in dentistry. It is an adjunct tool for diagnosing pathologies in the oral cavity that overcomes some limitations of current methodologies, including direct clinical examination, 2D radiographs, and cone beam computerized tomography. Increasing demand for soft tissue imaging has led to continuous improvements on transducer miniaturization and spatial resolution. The aims of this study are (1) to create a comprehensive overview of the current literature of ultrasonic imaging relating to dentistry, and (2) to provide a view onto investigations with immediate, intermediate, and long-term impact in periodontology and implantology.

METHODS

A rapid literature review was performed using two broad searches conducted in the PubMed database, yielding 576 and 757 citations, respectively. A rating was established within a citation software (EndNote) using a 5-star classification. The broad search with 757 citations allowed for high sensitivity whereas the subsequent rating added specificity.

RESULTS

A critical review of the clinical applications of ultrasound in dentistry was provided with a focus on applications in periodontology and implantology. The role of ultrasound as a developing dental diagnostic tool was reviewed. Specific uses such as soft and hard tissue imaging, longitudinal monitoring, as well as anatomic and physiological evaluation were discussed.

CONCLUSIONS

Future efforts should be directed towards the transition of ultrasonography from a research tool to a clinical tool. Moreover, a dedicated effort is needed to introduce ultrasonic imaging to dental education and the dental community to ultimately improve the quality of patient care.

Teeth Lesion Detection Using Deep Learning and the Internet of Things Post-COVID-19

With a view of the post-COVID-19 world and probable future pandemics, this paper presents an Internet of Things (IoT)-based automated healthcare diagnosis model that employs a mixed approach using data augmentation, transfer learning, and deep learning techniques and does not require physical interaction between the patient and physician. Through a user-friendly graphic user interface and availability of suitable computing power on smart devices, the embedded artificial intelligence allows the proposed model to be effectively used by a layperson without the need for a dental expert by indicating any issues with the teeth and subsequent treatment options. The proposed method involves multiple processes, including data acquisition using IoT devices, data preprocessing, deep learning-based feature extraction, and classification through an unsupervised neural network. The dataset contains multiple periapical X-rays of five different types of lesions obtained through an IoT device mounted within the mouth guard. A pretrained AlexNet, a fast GPU implementation of a convolutional neural network (CNN), is fine-tuned using data augmentation and transfer learning and employed to extract the suitable feature set. The data augmentation avoids overtraining, whereas accuracy is improved by transfer learning. Later, support vector machine (SVM) and the K-nearest neighbors (KNN) classifiers are trained for lesion classification. It was found that the proposed automated model based on the AlexNet extraction mechanism followed by the SVM classifier achieved an accuracy of 98%, showing the effectiveness of the presented approach.

Dental diagnosis for inlay restoration using an intraoral optical coherence tomography system: A case report

PATIENTS

The patient was a 32-year-old man who underwent amalgam restoration of the mandibular right second molar. An amalgam restoration fracture was diagnosed by intraoral optical coherence tomography (OCT), and pulp exposure was examined during cavity preparation. Subsequently, a definitive ceramic restoration was fabricated, and the marginal fit in the oral cavity was evaluated using the OCT system.

DISCUSSION

The existing OCT system cannot acquire images inside the oral cavity because of the large probe size. However, the proposed intraoral OCT system can access the prostheses in the mandibular right second molar. Therefore, dental diagnosis for restoration treatment with dental prosthesis fracture, marginal gap, and pulp exposure after tooth preparation is possible using the proposed intraoral OCT system.

CONCLUSIONS

The use of the intraoral OCT system improved dental diagnosis by allowing the dentist to confirm quantitative values through cross-sectional images, rather than that by determining a treatment plan after visual dental diagnosis.

Multi-class deep learning segmentation and automated measurements in periodontal sonograms of a porcine model

OBJECTIVES

Ultrasound emerges as a complement to cone-beam computed tomography in dentistry, but struggles with artifacts like reverberation and shadowing. This study seeks to help novice users recognize soft tissue, bone, and crown of a dental sonogram, and automate soft tissue height (STH) measurement using deep learning.

METHODS

In this retrospective study, 627 frames from 111 independent cine loops of mandibular and maxillary premolar and incisors collected from our porcine model (N = 8) were labeled by a reader. 274 premolar sonograms, including data augmentation, were used to train a multi class segmentation model. The model was evaluated against several test sets, including premolar of the same breed (n = 74, Yucatan) and premolar of a different breed (n = 120, Sinclair). We further proposed a rule-based algorithm to automate STH measurements using predicted segmentation masks.

RESULTS

The model reached a Dice similarity coefficient of 90.7±4.39%, 89.4±4.63%, and 83.7±10.5% for soft tissue, bone, and crown segmentation, respectively on the first test set (n = 74), and 90.0±7.16%, 78.6±13.2%, and 62.6±17.7% on the second test set (n = 120). The automated STH measurements have a mean difference (95% confidence interval) of -0.22 mm (-1.4, 0.95), a limit of agreement of 1.2 mm, and a minimum ICC of 0.915 (0.857, 0.948) when compared to expert annotation.

CONCLUSION

This work demonstrates the potential use of deep learning in identifying periodontal structures on sonograms and obtaining diagnostic periodontal dimensions.

Ultrasound Imaging in Dentistry: A Literature Overview

(1) Background: the frequency with which diagnostic tests are prescribed with exposure to ionizing radiation, a cause of biological damage, has been studied, and with much more attention, patients are subjected to these diagnostic tests for diagnosis and follow-up. This review aimed, given the recent developments of this technology, to evaluate the possible use of ultrasound in different branches of dentistry. The possibility of applying ionizing-radiation-free diagnostic exams in dentistry, overcoming the limits of this application, has led scientific research in this area to obtain interesting results that bode well for the future. (2) Methods: a search for articles on the application of ultrasounds in dentistry was performed using the PubMed electronic database. (3) Results: only 32 studies were included, and these clearly stated that this examination is widely usable and in great progress. (4) Conclusions: regarding the modern application techniques of this diagnostic test, it is essential to consider technological evolution as an objective to reduce the damage and side effects of necessary diagnostic tests. The use of ultrasound in dentistry can represent a valid radiation-free alternative, in certain contexts, to the other most used exams.

Real-Time Monitoring and Quantitative Evaluation of Resin In-Filtrant Repairing Enamel White Spot Lesions Based on Optical Coherence Tomography

The aim of the present study was to explore the feasibility of real-time monitoring and quantitative guiding the repair of enamel white spot lesions (WSLs) with resin infiltration by optical coherence tomography (OCT). Seven New Zealand rabbits were treated with 37% phosphoric acid etchant for 15 min to establish the model of enamel demineralization chalk spots of upper incisors, which were repaired by Icon resin infiltrant. OCT, stereo microscope (SM) imaging, scanning electron microscope (SEM) imaging and hematoxylin eosin (HE) staining were used to image each operation step. The changes of WSLs of enamel before and in the process of restoration with resin infiltrant showed specific performance in OCT images, which were consistent with the corresponding results of stereomicroscope and SEM. OCT can non-invasively and accurately image the whole process of repairing enamel demineralization layer with resin infiltration real-time, which can effectively guide the clinical use of resin infiltrant to repair enamel WSLs and be used as an imaging tool to evaluate the process and effect of restoration with resin infiltrant at the same time.

In vitro comparison of high-definition US, CBCT and periapical radiography in the diagnosis of proximal and recurrent caries

OBJECTIVE

To assess the in vitro performance of high-definition (HD) US, CBCT and periapical radiography for the visibility of proximal and recurrent caries in teeth with and without restoration.

METHODS

A total of 240 molar teeth were divided into eight groups each comprised of 30 teeth. Control groups consisted of teeth without caries (Group 1-4; N = 120), whereas diseased groups consisted of teeth with proximal caries (Group 5-8; N = 120 teeth). Finally, a total of four image sets were obtained as follows: i) PSP periapical radiography, ii) CBCT 0.075 mm voxel size, iii) CBCT 0.2 mm voxel size and iv) HD US images. The image sets were viewed separately by four observers by using a 5-point confidence scale. Intraclass correlation coefficients were calculated. The areas under the ROC curves were compared using chi-square tests. Significance level was set at α = 0.05.

RESULTS

Intraobserver agreement for both readings for the four observers ranged between 0.848 and 0.988 for CBCT (0.075 mm) images; 0.658 and 0.952 for CBCT (0.2 mm) images; 0.555 and 0.810 for periapical images; 0.427 and 0.676 for US images. Highest AUC values were found for CBCT (0.075 mm) images and lowest for US images. Statistically significant differences were found among CBCT (0.075 mm) images and US images (p < 0.001), CBCT (0.2 mm) images and US images (p < 0.001) and periapical images and US images (p < 0.001) for the detection of proximal caries.

CONCLUSION

Periapical and CBCT images outperformed HD US imaging in the detection of proximal dental caries.