The repeatability of periodontal imaging with intraoral ultrasound scanning.

OBJECTIVE: Ultrasound is a non-invasive and low-cost diagnostic tool widely used in medicine. Recent studies have demonstrated that ultrasound imaging might have the potential to be used intraorally to assess the periodontium by comparing it to current imaging methods. This study aims to characterize the repeatability of intraoral periodontal ultrasound imaging. MATERIALS AND METHODS: Two hundred and twenty-three teeth were scanned from fourteen volunteers participating in this study. One operator conducted all the scans in each tooth thrice with a 20 MHz intraoral ultrasound. The repeatability of three measurements, alveolar bone crest to the cementoenamel junction (ABC-CEJ), gingival thickness (GT), and alveolar bone thickness (ABT), was calculated with intercorrelation coefficient (ICC). Measurements were also compared with mean absolute deviation (MAD), repeatability coefficient (RC), and descriptive statistics. RESULTS: ICC scores for intra-rater repeatability were 0.917(0.897,0.933), 0.849(0.816,0.878), and 0.790(0.746,0.898), MAD results were 0.610 mm (± 0.508), 0.224 (± 0.200), and 0.067 (± 0.060), and RC results were 0.648, 0.327, and 0.121 for ABC-CEJ, GT, and ABT measurements, respectively. CONCLUSION: Results of the present study pointed towards good or excellent repeatability of ultrasound as a measurement tool for periodontal structures. CLINICAL RELEVANCE: Clinicians could benefit from the introduction of a novel chairside diagnostic tool. Ultrasound is a non-invasive imaging assessment tool for the periodontium with promising results in the literature. Further validation, establishment of scanning protocols, and commercialization are still needed before ultrasound imaging is available for clinicians.

Measuring the alveolar bone level in adolescents: A comparison between ultrasound and cone beam computed tomography.

BACKGROUND: Cone beam computed tomography (CBCT) is an imaging modality, which is used routinely in orthodontic diagnosis and treatment planning but delivers much higher radiation than conventional dental radiographs. Ultrasound is a noninvasive imaging method that creates an image without ionizing radiation. AIM: To investigate the reliability of ultrasound and the agreement between ultrasound and CBCT in measuring the alveolar bone level (ABL) on the buccal/labial side of the incisors in adolescent orthodontic patients. DESIGN: One hundred and eighteen incisors from 30 orthodontic adolescent patients were scanned by CBCT with 0.3-mm voxel size and ultrasound at 20 MHz frequency. The ABL, distance from the cementoenamel junction (CEJ) to the alveolar bone crest (ABC), was measured twice to evaluate the agreement between ultrasound and CBCT. In addition, the intra- and inter-rater reliabilities in measuring the ABL by four raters were compared. RESULTS: The mean difference (MD) in the ABL between ultrasound and CBCT was -0.07 mm with 95% limit of agreement (LoA) from -0.47 to 0.32 mm for all teeth. For each jaw, the MDs between the ultrasound and CBCT were -0.18 mm (for mandible with 95% LoA from -0.53 to 0.18 mm) and 0.03 mm (for maxilla with 95% LoA from -0.28 to 0.35 mm). In comparison, ultrasound had higher intra-rater (ICC = 0.83-0.90) and inter-rater reliabilities (ICC = 0.97) in ABL measurement than CBCT (ICC = 0.56-0.78 for intra-rater and ICC = 0.69 for inter-rater reliabilities). CONCLUSION: CBCT parameters used in orthodontic diagnosis and treatment planning in adolescents may not be a reliable tool to assess the ABL for the mandibular incisors. On the contrary, ultrasound imaging, an ionizing radiation-free, inexpensive, and portable diagnostic tool, has potential to be a reliable diagnostic tool in assessing the ABL in adolescent patients.

Ultrasonic mapping of midpalatal suture - An ex-vivo study.

OBJECTIVE: Rapid maxillary expansion is a common orthodontic procedure to correct maxillary constriction. Assessing the midpalatal suture (MPS) expansion plays a crucial role in treatment planning to determine its effectiveness. The objectives of this preliminary investigation are to demonstrate a proof of concept that the palatal bone underlying the rugae can be clearly imaged by ultrasound (US) and the reconstructed axial view of the US image accurately maps the MPS patency. METHODS: An ex-vivo US scanning was conducted on the upper jawbones of two piglet's carcasses before and after the creation of bone defects, which simulated the suture opening. The planar images were processed to enhance bone intensity distribution before being orderly stacked to fuse into a volume. Graph-cut segmentation was applied to delineate the palatal bone to generate a bone volume. The accuracy of the reconstructed bone volume and the suture opening was validated by the micro-computed tomography (µCT) data used as the ground truth and compared with cone beam computed tomography (CBCT) data as the clinical standard. Also included in the comparison is the rugae thickness. Correlation and Bland-Altman plots were used to test the agreement between the two methods: US versus µCT/CBCT. RESULTS: The reconstruction of the US palatal bone volumes was accurate based on surface topography comparison with a mean error of 0.19 mm for pre-defect and 0.15 mm and 0.09 mm for post-defect models of the two samples, respectively when compared with µCT volumes. A strong correlation (R2 ≥ 0.99) in measuring MPS expansion was found between US and µCT/CBCT with MADs of less than 0.05 mm, 0.11 mm and 0.23 mm for US, µCT and CBCT, respectively. CONCLUSIONS: It was possible to axially image the MPS opening and rugae thickness accurately using high-frequency ultrasound. CLINICAL SIGNIFICANCE: This study introduces an ionizing radiation-free, low-cost, and portable technique to accurately image a difficult part of oral cavity anatomy. The advantages of conceivable visualization could promise a successful clinical examination of MPS to support the predictable treatment outcome of maxillary transverse deficiency.

Estimating Crestal Thickness of Alveolar Bones on Intra-oral Ultrasonograms.

OBJECTIVE: Alveolar crestal bone thickness and level provide important diagnostic and prognostic information for orthodontic treatment, periodontal disease management and dental implants. Ionizing radiation-free ultrasound has emerged as a promising clinical tool in imaging oral tissues. However, the ultrasound image is distorted when the wave speed of the tissue of interest is different from the mapping speed of the scanner and, therefore, the subsequent dimension measurements are not accurate. This study was aimed at deriving a correction factor that can be applied to the measurements to correct for discrepancy caused by speed variation. METHODS: The factor is a function of the speed ratio and the acute angle that the segment of interest makes with the beam axis perpendicular to the transducer. The phantom and cadaver experiments were designed to validate the method. DISCUSSION: The comparisons agree well with absolute errors not more than 4.9%. Dimension measurements on ultrasonographs can be properly corrected by applying the correction factor without recourse to the raw signals. CONCLUSION: The correction factor has reduced the measurement discrepancy on the acquired ultrasonographs for the tissue whose speed is different from the scanner's mapping speed.

Ultrasound Imaging of the Periodontium Complex: A Reliability Study.

Background: Ultrasonography is a noninvasive, low-cost diagnostic tool widely used in medicine. Recent studies have demonstrated that ultrasound imaging might have the potential to be used intraorally to assess periodontal biomarkers. Objectives: To evaluate the reliability of interlandmark distance measurements on intraoral ultrasound images of the periodontal tissues. Materials and Methods: Sixty-four patients from the graduate periodontics (n = 33) and orthodontics (n = 31) clinics were recruited. A 20 MHz handheld intraoral ultrasound transducer was used to scan maxillary and mandibular incisors, canines, and premolars. Distances between the alveolar bone crest and cementoenamel junction (ABC-CEJ), gingival thickness (GT), and alveolar bone thickness (ABT) were measured by 3 raters. The intercorrelation coefficient (ICC) and mean absolute deviation (MAD) were calculated among and between the raters. Raters also scored images according to quality. Results: The ICC scores for intrarater reliability were 0.940 (0.932-0.947), 0.953 (0.945-0.961), and 0.859 (0.841-0.876) for ABC-CEJ, GT, and ABT, respectively. The intrarater MAD values were 0.023 (±0.019) mm, 0.014 (±0.005) mm, and 0.005 (±0.003) mm, respectively. The ICC scores for interrater reliability were 0.872 (95% CI: 0.836-0.901), 0.958 (95% CI: 0.946-0.968), and 0.836 (95% CI: 0.789-0.873) for ABC-CEJ, GT, and ABT, respectively. The interrater MAD values were 0.063 (±0.029) mm, 0.023 (±0.018) mm, and 0.027 (±0.012) mm, respectively. Conclusions: The present study showed the high reliability of ultrasound in both intrarater and interrater assessments. Results suggest there might be a potential use of intraoral ultrasound to assess periodontium.

Temporomandibular joint segmentation in MRI images using deep learning.

OBJECTIVES: Temporomandibular joint (TMJ) internal derangements (ID) represent the most prevalent temporomandibular joint disorder (TMD) in the population and its diagnosis typically relies on magnetic resonance imaging (MRI). TMJ articular discs in MRIs usually suffer from low resolution and contrast, and it is difficult to identify them. In this study, we applied two convolutional neural networks (CNN) to delineate mandibular condyle, articular eminence, and TMJ disc in MRI images. METHODS: The models were trained on MRI images from 100 patients and validated on images from 40 patients using 2D slices and 3D volume as input, respectively. Data augmentation and five-fold cross-validation scheme were applied to further regularize the models. The accuracy of the models was then compared with four raters having different expertise in reading TMJ-MRI images to evaluate the performance of the models. RESULTS: Both models performed well in segmenting the three anatomical structures. A Dice coefficient of about 0.7 for the articular disc, more than 0.9 for the mandibular condyle, and Hausdorff distance of about 2mm for the articular eminence were achieved in both models. The models reached near-expert performance for the segmentation of TMJ articular disc and performed close to the expert in the segmentation of mandibular condyle and articular eminence. They also surpassed non-experts in segmenting the three anatomical structures. CONCLUSION: This study demonstrated that CNN-based segmentation models can be a reliable tool to assist clinicians identifying key anatomy on TMJ-MRIs. The approach also paves the way for automatic diagnosis of TMD. CLINICAL SIGNIFICANCE: Accurately locating the articular disc is the hardest and most crucial step in the interpretation of TMJ-MRIs and consequently in the diagnosis of TMJ-ID. Automated software that assists in locating the articular disc and its surrounding structures would improve the reliability of TMJ-MRI interpretation, save time and assist in reader training. It will also serve as a foundation for additional automated analysis of pathology in TMJ structures to aid in TMD diagnosis.

Localization of cementoenamel junction in intraoral ultrasonographs with machine learning.

OBJECTIVE: Our goal was to automatically identify the cementoenamel junction (CEJ) location in ultrasound images using deep convolution neural networks (CNNs). METHODS: Three CNNs were evaluated using 1400 images and data augmentation. The training and validation were performed by an experienced nonclinical rater with 1000 and 200 images, respectively. Four clinical raters with different levels of experience with ultrasound tested the networks using the other 200 images. In addition to the comparison of the best approach with each rater, we also employed the simultaneous truth and performance level estimation (STAPLE) algorithm to estimate a ground truth based on all labelings by four clinical raters. The final CEJ location estimate was obtained by taking the first moment of the posterior probability computed using the STAPLE algorithm. The study also computed the machine learning-measured CEJ-alveolar bone crest distance. RESULTS: Quantitative evaluations of the 200 images showed that the comparison of the best approach with the STAPLE-estimate yielded a mean difference (MD) of 0.26 mm, which is close to the comparison with the most experienced nonclinical rater (MD=0.25 mm) but far better than the comparison with clinical raters (MD=0.27-0.33 mm). The machine learning-measured CEJ-alveolar bone crest distances correlated strongly (R = 0.933, p < 0.001) with the manual clinical labeling and the measurements were in good agreement with the 95% Bland-Altman's lines of agreement between -0.68 and 0.57 mm. CONCLUSIONS: The study demonstrated the feasible use of machine learning methodology to localize CEJ in ultrasound images with clinically acceptable accuracy and reliability. Likelihood-weighted ground truth by combining multiple labels by the clinical experts compared favorably with the predictions by the best deep CNN approach. CLINICAL SIGNIFICANCE: Identification of CEJ and its distance from the alveolar bone crest play an important role in the evaluation of periodontal status. Machine learning algorithms can learn from complex features in ultrasound images and have potential to provide a reliable and accurate identification in subsecond. This will greatly assist dental practitioners to provide better point-of-care to patients and enhance the throughput of dental care.

Three-dimensional periodontal investigations using a prototype handheld ultrasound scanner with spatial positioning reading sensor.

AIM: To demonstrate the feasibility of the 3D ultrasound periodontal tissue reconstruction of the lateral area of a porcine mandible using standard 2D ultrasound equipment and spatial positioning reading sensors. MATERIAL AND METHOD: Periodontal 3D reconstructions were performed using a free-hand prototype based on a 2D US scanner and a spatial positioning reading sensor. For automated data processing, deep learning algorithms were implemented and trained using semi-automatically seg-mented images by highly specialized imaging professionals. RESULTS: US probe movement analysis showed that non-parallel 2D frames were acquired during the scanning procedure. Comparing 3 different 3D periodontal reconstructions of the same porcine mandible, the accuracy ranged between 0.179 mm and 0.235 mm. CONCLUSION: The present study demonstrated the diagnostic potential of 3D reconstruction using a free-hand 2D US scanner with spatial positioning readings. The use of auto-mated data processing with deep learning algorithms makes the process practical in the clinical environment for assessment of periodontal soft tissues.

Automated integration of facial and intra-oral images of anterior teeth.

BACKGROUND AND OBJECTIVE: Digital smile design is the technique that dentists use to analyze, design, and visualize therapeutic results on a computing workstation prior to actual treatment. Despite it being a crucial step in digital smile design, the process of labeling and integrating the information in facial and intra-oral images is laborious. Therefore, this study aims to develop an automated photo integrating system to facilitate this process. METHODS: The teeth in intra-oral images were distinguished by their curvature and finely segmented using an active contour model. The facial keypoints were detected by a sophisticated facial landmark detector algorithm; these keypoints were then overlaid on the corresponding intra-oral image by extracting the contour of the teeth in the facial and intra-oral photographs. With this system, the tooth width-to-height ratios, smile line, and facial midline were automatically marked in the intra-oral image. The accuracy of the proposed segmentation algorithm was evaluated by applying it to 50 images with 274 maxillary anterior teeth. RESULTS: The proposed algorithm recognized 96.0% (263/274) of teeth in our selected image set. The results were then compared to those obtained by applying manual segmentation to the remaining 263 recognized teeth. With a 95% confidence interval, a Jaccard index of 0.928 ± 0.081, average distance of 0.128 ± 0.109 mm, and Hausdorff distance between the results and ground truth of 0.461 ± 0.495 mm were achieved. CONCLUSIONS: The results of this study show that the proposed automated system can eliminate the need for dentists to employ a laborious image integration process. It also has the potential for broad applicability in the field of dentistry.

Mussel-Inspired Adhesive Double-Network Hydrogel for Intraoral Ultrasound Imaging.

Periodontal diseases could be diagnosed through intraoral ultrasound imaging with the advantages of simple operation procedures, low cost, and low safety risks. A couplant is normally placed between transducers and tissues for better ultrasound image quality. If applied intraorally, the couplants should possess good stability in water and robust mechanical properties, as well as strong adhesiveness to transducers and tissues. However, commercial couplants, such as Aquaflex (AF) cannot fulfill these requirements. In this work, inspired by the mussel adhesion mechanism, we reported a poly(vinyl alcohol)-polyacrylamide-polydopamine (PVA-PAM-PDA) hydrogel synthesized by incorporating PDA into the PAM-PVA double-network for intraoral ultrasound imaging. The hydrogel maintains good stability in water as well as exceptional mechanical properties and can adhere to different substrates (i.e., metal, glass, and porcine skin) without losing the original adhesion strength after multiple adhesion-strip cycles. Besides, when applied to porcine mandibular incisor imaging, the PVA-PAM-PDA hydrogel possesses good image quality for diagnosis as AF does. This work provides practical insights into the fabrication of multifunctional hydrogel-based interfaces between human tissues and medical devices for disease diagnosis applications.

Polyacrylamide/Alginate double-network tough hydrogels for intraoral ultrasound imaging.

HYPOTHESIS: Intraoral ultrasound is a safer and economical approach to image dento-periodontal tissues and diagnose periodontal diseases compared with X-ray. A gel pad is often used as a couplant between the transducer and oral tissue to delay the ultrasound signals for better identification. However, the current commercial couplant, such as Aquaflex gel pad (AF), face many challenges, including low stability in water, poor mechanical properties, low coefficient of friction, and potential cytotoxicity issues. Polyacrylamide/sodium alginate (PAM/Alginate) double-network (DN) tough hydrogel could address these issues as the potential couplant for intraoral ultrasound imaging. EXPERIMENTS: Different critical properties required for intraoral ultrasound imaging, including stability in water, mechanical properties, frictional properties, ultrasound properties and biocompatibility of PAM/Alginate DN tough hydrogels were evaluated and compared with those of AF. FINDINGS: The PAM/Alginate DN hydrogel not only possesses better stability in water as well as improved mechanical properties and higher coefficients of friction than AF but also can provide similar ultrasound image quality as AF does. Moreover, the PAM/Alginate DN hydrogel shows lower cytotoxicity to both cancer (Hela) and fibroblast cells (MRC-5). With all these significant features, such tough hydrogels serve as a proof-of-concept ultrasound couplant with great potential in intraoral ultrasound imaging.

Fully Automated Segmentation of Alveolar Bone Using Deep Convolutional Neural Networks from Intraoral Ultrasound Images.

Delineation of alveolar bone aids the diagnosis and treatment of periodontal diseases. In current practice, conventional 2D radiography and 3D cone-beam computed tomography (CBCT) imaging are used as the non-invasive approaches to image and delineate alveolar bone structures. Recently, high-frequency ultrasound imaging is proposed as an alternative to conventional imaging methods to prevent the harmful effects of ionizing radiation. However, the manual delineation of alveolar bone from ultrasound imaging is time-consuming and subject to inter and intraobserver variability. This study proposes to use a convolutional neural network-based machine learning framework to automatically segment the alveolar bone from ultrasound images. The proposed method consists of a homomorphic filtering based noise reduction and a u-net machine learning framework for automated delineation. The proposed method was evaluated over 15 ultrasound images of tooth acquired from procine specimens. The comparisons against manual ground truth delineations performed by three experts in terms of mean Dice score and Hausdorff distance values demonstrate that the proposed method yielded an improved performance over a recent state of the art graph cuts based method.

Comparison of ultrasound imaging and cone-beam computed tomography for examination of the alveolar bone level: A systematic review.

BACKGROUND AND OBJECTIVE: The current methods to image alveolar bone in humans include intraoral 2D radiography and cone-beam computed tomography (CBCT). However, these methods expose the subject to ionizing radiation. Therefore, ultrasound imaging has been investigated as an alternative technique, as it is both non-invasive and free from ionizing radiation. In order to assess the validity and reliability of ultrasonography in visualizing alveolar bone, a systematic review was conducted comparing ultrasound imaging to CBCT for examination of the alveolar bone level. STUDY DESIGN: Seven databases were searched. Studies addressing examination of alveolar bone level via CBCT and ultrasound were selected. Risk of bias under Cochrane guidelines was used as a methodological quality assessment tool. RESULTS: All the four included studies were ex vivo studies that used porcine or human cadaver samples. The alveolar bone level was measured by the distance from the alveolar bone crest to certain landmarks such as cemento-enamel junction or gingival margin. The risk of bias was found as low. The mean difference between ultrasound and CBCT measurements ranged from 0.07 mm to 0.68 mm, equivalent to 1.6% - 8.8%. CONCLUSIONS: There is currently preliminary evidence to support the use of ultrasonography as compared to CBCT for the examination of alveolar bone level. Further studies comparing ultrasound to gold standard methods would be necessary to help validate the accuracy of ultrasonography as a diagnostic technique in periodontal imaging.

Imaging the Cemento-Enamel Junction Using a 20-MHz Ultrasonic Transducer.

The cemento-enamel junction (CEJ), which is the intersection between enamel and cementum, is an important landmark in the diagnosis of periodontal disease. Pulse-echo ultrasound was used to image the CEJs of six porcine lower central incisors with a single 20-MHz transducer. A notch was longitudinally created on the enamel as a stable marker, from which the CEJ was measured. Data were acquired along the tooth's axis at 0.4-mm intervals. Time-distance data were bandpass-filtered to enhance signal-to-noise ratio and record density was increased fourfold to 0.1-mm spacing by a frequency-distance interpolation scheme. Reflections from the CEJ were unambiguously identified along with those from enamel, dentin and cementum. The notch-CEJ distances measured by the ultrasound and micro-computed tomography methods correlated strongly (r = 0.996, p < 0.05) and were in good agreement with the 95% lines of agreement between -0.49 and 0.17 mm, as statistically determined by Bland-Altman analysis. The results indicate the potential of ultrasound to be a reliable and non-ionizing technique to image the CEJ.

High-Resolution Ultrasonic Imaging of Dento-Periodontal Tissues Using a Multi-Element Phased Array System.

Intraoral ultrasonography uses high-frequency mechanical waves to study dento-periodontium. Besides the advantages of portability and cost-effectiveness, ultrasound technique has no ionizing radiation. Previous studies employed a single transducer or an array of transducer elements, and focused on enamel thickness and distance measurement. This study used a phased array system with a 128-element array transducer to image dento-periodontal tissues. We studied two porcine lower incisors from a 6-month-old piglet using 20-MHz ultrasound. The high-resolution ultrasonographs clearly showed the cross-sectional morphological images of the hard and soft tissues. The investigation used an integration of waveform analysis, travel-time calculation, and wavefield simulation to reveal the nature of the ultrasound data, which makes the study novel. With the assistance of time-distance radio-frequency records, we robustly justified the enamel-dentin interface, dentin-pulp interface, and the cemento-enamel junction. The alveolar crest level, the location of cemento-enamel junction, and the thickness of alveolar crest were measured from the images and compared favorably with those from the cone beam computed tomography with less than 10% difference. This preliminary and fundamental study has reinforced the conclusions from previous studies, that ultrasonography has great potential to become a non-invasive diagnostic imaging tool for quantitative assessment of periodontal structures and better delivery of oral care.

Broadband ultrasound attenuation measurement of long bone using peak frequency of the echoes.

The traditional peak frequency formulation requires the knowledge of the signal wavelet. We improved and applied the method to estimate attenuation for homogeneous silicon rubber and bovine cortical bone without recourse to the wavelet assumption. The estimated values for rubber and bone samples are 6.59 +/- 0.28 dB/MHz/cm and 4.59 +/- 1.09 dB/MHz/cm, respectively, as compared with 6.33 +/- 0.19 dB/ MHz/cm and 5.00 +/- 1.10 dB/MHz/cm, respectively, by the spectral ratio method.