A miniaturized ultrasound transducer for monitoring full-mouth oral health: a preliminary study

Ultrasound identification of the cementoenamel junction and clinical correlation through ex vivo analysis

Accurately identifying periodontal landmarks via acoustic imaging is increasingly important. Here, we evaluated the accuracy of cementoenamel junction (CEJ) identification using ultrasound by comparing it to clinical methods in 153 extracted human teeth. The distance between the CEJ to a reference point was measured using two clinical methods (visual examination and tactile sensation) as well as ultrasound imaging. Statistical analyses were performed across all teeth and sub-groups, including tooth types (incisors, cuspids, and molars/premolars), and two classifications: A- vs. B- (visually detectable or undetectable CEJ, respectively); and CL-S vs. CL-D (shallow or deep cervical lesions). In A- or CL-S teeth, ultrasound measurements highly agreed with clinical measurements, showing a 1.72-mm 95% CI for A- and 1.99-mm 95% CI for CL-S compared to visual examination, and a 1.77-mm 95% CI for A- and a 2.10-mm 95% CI for CL-S compared to tactile sensation, respectively. For 80% of A- and 76% of CL-S teeth, the difference between ultrasound and visual examination was within ± 20%. For 81% of A- and 80% of CL-S teeth, the difference between ultrasound and tactile sensation was within ± 20%. The variance of ultrasound versus clinical CEJ identifications showed a significant correlation (r = 0.6607) to the cervical lesion depth. The errors between ultrasound and clinical measurements show no significant bias across different tooth types.

Nonionizing diagnostic imaging modalities for visualizing health and pathology of periodontal and peri-implant tissues

Radiographic examination has been an essential part of the diagnostic workflow in periodontology and implant dentistry. However, radiographic examination unavoidably involves ionizing radiation and its associated risks. Clinicians and researchers have invested considerable efforts in assessing the feasibility and capability of utilizing nonionizing imaging modalities to replace traditional radiographic imaging. Two such modalities have been extensively evaluated in clinical settings, namely, ultrasonography (USG) and magnetic resonance imaging (MRI). Another modality, optical coherence tomography (OCT), has been under investigation more recently. This review aims to provide an overview of the literature and summarize the usage of USG, MRI, and OCT in evaluating health and pathology of periodontal and peri-implant tissues. Clinical studies have shown that USG could accurately measure gingival height and crestal bone level, and classify furcation involvement. Due to physical constraints, USG may be more applicable to the buccal surfaces of the dentition even with an intra-oral probe. Clinical studies have also shown that MRI could visualize the degree of soft-tissue inflammation and osseous edema, the extent of bone loss at furcation involvement sites, and periodontal bone level. However, there was a lack of clinical studies on the evaluation of peri-implant tissues by MRI. Moreover, an MRI machine is very expensive, occupies much space, and requires more time than cone-beam computed tomography (CBCT) or intraoral radiographs to complete a scan. The feasibility of OCT to evaluate periodontal and peri-implant tissues remains to be elucidated, as there are only preclinical studies at the moment. A major shortcoming of OCT is that it may not reach the bottom of the periodontal pocket, particularly for inflammatory conditions, due to the absorption of near-infrared light by hemoglobin. Until future technological breakthroughs finally overcome the limitations of USG, MRI and OCT, the practical imaging modalities for routine diagnostics of periodontal and peri-implant tissues remain to be plain radiographs and CBCTs.

Use of ultrasound imaging for assessment of the periodontium: A systematic review

The objective of this study was to systematically review the literature regarding diagnostic applications of ultrasound imaging for evaluation of the periodontium in humans. The search was conducted on Medline, EMBASE, Web of Science, Scopus, Cochrane, and PubMed up to April 3, 2023. The studies included were exclusively human studies that assessed the periodontium with ultrasound (US) imaging (b-mode). Outcomes measured included alveolar bone level, alveolar bone thickness, gingival thickness, and blood flow quantification. References were imported to Covidence. Two reviewers conducted phases 1 and 2. The JBI risk assessment tool for cross-sectional studies was used. Extracted data included the transducer and measurements used and the study's outcomes. The search yielded 4892 studies after removing duplicates. From these, 25 studies were included and selected for extraction. Included studies retrieved outcomes from US examinations of the periodontal tissues. From the selected studies, 15 used US on natural teeth, 4 used US on implants, 2 used US on edentulous ridges, and 4 used color flow/power in US to evaluate the blood flow. The results of the present systematic review suggest that US might be a feasible and valuable diagnostic tool for the periodontium, with the potential to complement shortfalls of current radiographic technologies.

Three-dimensional mapping of the greater palatine artery location and physiology

OBJECTIVE

To develop a novel technique for localizing and reconstructing the greater palatine artery (GPA) using three-dimensional (3D) technology.

METHODS

A miniaturized intraoral ultrasound transducer was used to imaging landmarks including the GPA, gingival margin (GM), and palatal masticatory mucosa (PMM). A 5-mm-thick solid hydrogel couplant was integrated to replace traditional ultrasound gel and avoid bubbles when moving the transducer.

RESULTS

A panorama image provided the relative localization of landmarks including the GPA, PMM, and hard palate. Short- and long-axis imaging of GPA was performed in five subjects including 3D mapping of GPA branches and surrounding tissues in a volume of 10 mm × 8 mm × 10 mm. Full-mouth Doppler imaging was also demonstrated on both the dorsal and ventral tongue as well as buccal mucosa and sublingual region on two subjects.

CONCLUSIONS

This study can measure the vertical distance from the GM to the GPA and depth from PMM to GPA and visualize the GPA localization in a 3D manner, which is critical to evaluate the available volume of palatal donor tissues and avoid sectioning of GPA during surgical harvesting of the tissues. Finally, the transducer's small size facilitates full-mouth Doppler imaging with the potential to improve the assessment, diagnosis, and management of oral mucosa.

Deep learning assisted sparse array ultrasound imaging

This study aims to restore grating lobe artifacts and improve the image resolution of sparse array ultrasonography via a deep learning predictive model. A deep learning assisted sparse array was developed using only 64 or 16 channels out of the 128 channels in which the pitch is two or eight times the original array. The deep learning assisted sparse array imaging system was demonstrated on ex vivo porcine teeth. 64- and 16-channel sparse array images were used as the input and corresponding 128-channel dense array images were used as the ground truth. The structural similarity index measure, mean squared error, and peak signal-to-noise ratio of predicted images improved significantly (p < 0.0001). The resolution of predicted images presented close values to ground truth images (0.18 mm and 0.15 mm versus 0.15 mm). The gingival thickness measurement showed a high level of agreement between the predicted sparse array images and the ground truth images, as indicated with a bias of -0.01 mm and 0.02 mm for the 64- and 16-channel predicted images, respectively, and a Pearson's r = 0.99 (p < 0.0001) for both. The gingival thickness bias measured by deep learning assisted sparse array imaging and clinical probing needle was found to be <0.05 mm. Additionally, the deep learning model showed capability of generalization. To conclude, the deep learning assisted sparse array can reconstruct high-resolution ultrasound image using only 16 channels of 128 channels. The deep learning model performed generalization capability for the 64-channel array, while the 16-channel array generalization would require further optimization.