High-Frequency Intraoral Ultrasonography for Periodontal Tissue Characterization: A Pilot Study Exploring Echogenic Signatures and Inflammation Detection

High-Frequency Intraoral Ultrasonography for Periodontal Tissue Characterization: A Pilot Study Exploring Echogenic Signatures and Inflammation Detection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article High-Frequency Intraoral Ultrasonography for Periodontal Tissue Characterization: A Pilot Study Exploring Echogenic Signatures and Inflammation Detection Laurent Estrade, Alexis Delpierre, Victor Rimbaud, Frédéric Denis, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7093377/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Dec, 2025 Read the published version in BMC Oral Health → Version 1 posted 13 You are reading this latest preprint version Abstract Background To assess the feasibility and diagnostic potential of high-frequency intraoral ultrasonography for the real-time evaluation and characterization of periodontal tissues, including tissue echogenicity under both healthy and inflammatory conditions. Methods This prospective single-center pilot study included 13 patients diagnosed with periodontitis. Each participant underwent a standardized examination combining high frequency ultrasonographic imaging (20 MHz). A total of 1,987 ultrasonic ultrasonography measurements were recorded. Ultrasonographic images were analyzed using ImageJ to assess the mean and standard deviation of pixel intensity across several periodontal tissue types. Statistical analyses included Kruskal–Wallis testing, linear discriminant analysis (LDA), and K-means clustering to explore echogenicity-based tissue differentiation. Results Ultrasound imaging enabled visualization of periodontal structures (enamel, cementum, alveolar bone, connective tissue, inflammatory tissue). Mean echogenicity values differed significantly across tissue types (p < 0.001), with enamel showing the highest mean pixel intensity (200 ± 15) and inflammatory tissue the lowest (50 ± 10). LDA achieved partial tissue separation, while K-means clustering identified six distinct echogenicity-based clusters. Real-time imaging detected subgingival calculus and deep tissue inflammation in 8 of 13 patients. Conclusion High-frequency intraoral ultrasonography is a feasible, non-invasive method for real-time periodontal tissue characterization. Echogenicity provides a robust marker for tissue differentiation and inflammation detection, but further studies with larger cohorts are needed to validate its clinical utility and automate diagnostic processes. Trial registration : The study was approved by the Institutional Ethics Committee (Approval No. 22.04642.000161) and registered on the International Clinical Trials Registry Platform (ICTRP) under the identifier NCT05809427 (07/07/2023). high-frequency ultrasonography periodontal tissues echogenicity inflammation detection non-invasive diagnostics periodontal pocket Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Background The tooth is a specialized anatomical structure whose functions are mechanical (mastication), sensory, phonatory, and aesthetic. It is composed of both mineralized and non-mineralized tissues. The crown—the visible portion in the oral cavity—is covered with enamel, an extremely hard and acellular tissue. The root is covered with cementum, a tissue analogous to bone, which plays a key role in anchoring the tooth within the alveolar socket. The periodontium, comprising gingiva, periodontal ligament, cementum, and alveolar bone, supports and protects teeth (1). The gingiva is the visible part of the periodontium and is subdivided into free gingiva, attached gingiva, and interdental papilla. It is covered by a stratified squamous keratinized (or parakeratinized) epithelium, supported by an underlying connective tissue that is richly vascularized. At the interface between the gingival epithelium and the tooth surface lies the junctional epithelium, a key structure in the defense against bacterial aggression (2,3). The alveolodental ligament is a fibrous connective tissue located between the dental root and the alveolar bone, composed of collagen fibers organized into various bundles. It not only ensures the anchorage of the tooth but also contributes to the absorption of masticatory forces and proprioceptive perception. Cementum covers the dental root and serves as an attachment site for periodontal fibers. Finally, the alveolar bone surrounds the root and dynamically adapts to functional stresses and inflammatory conditions. Although histologically distinct, these tissues function in an integrated manner to maintain periodontal health. Their alteration, particularly in chronic inflammatory contexts, leads to a cascade of destructive processes affecting the tooth's supporting tissues (gingiva, periodontal ligament, cementum, alveolar bone) (1,2,4). Periodontal diseases, such as gingivitis and periodontitis, are major public health concerns due to their prevalence and destructive impact (2). Clinical periodontal assessment relies on manual probing and radiography, both of which have limitations. Manual probing is subjective and may cause discomfort (3), while intraoral radiographs provide only two-dimensional views and cannot assess soft tissues or early inflammation (5). Clinically, the periodontal pocket is defined as a pathological deepening of the gingival sulcus, resulting from the destruction of the tooth’s supporting tissues. Its measurement is based on precise anatomical landmarks (6). Radiographic examination is a key complement to clinical assessment in periodontology. It is primarily used to evaluate the condition of the alveolar bone, identify horizontal or vertical bone loss, and detect potential periapical lesions, calcifications, or subgingival calculus deposits. Intraoral radiographs (periapical, bitewing) are the most commonly used in daily practice due to their high resolution and their ability to provide detailed views of local dental and bony structures. However, they offer only a two-dimensional representation of a three-dimensional anatomical situation, which can limit interpretation—especially when bone defects are located on the palatal or lingual surfaces (7,8). Nevertheless, radiographic examination has several important limitations in the context of periodontal assessment. First, it does not allow direct visualization of soft tissues (gingiva, junctional epithelium, periodontal ligament), nor of active inflammation that often precedes observable bone loss. Second, radiographic images do not permit measurement of periodontal pocket depth, nor do they reliably detect early signs of bone demineralization before significant loss has occurred. Additionally, anatomical superimpositions and variations in projection angulation can affect the accuracy of interpretation (4,7,8). In this context, ultrasonographic imaging emerges as a promising technology. High-frequency ultrasonography offers non-invasive, real-time imaging of periodontal structures without ionizing radiation, including soft tissues such as the marginal gingiva, junctional epithelium, and periodontal ligament. This opens the door to a more objective and dynamic assessment of pocket depth, tissue structure, and inflammation (9–14). Ultrasound imaging is not limited to the visualization of superficial soft tissues; it also enables fine assessment of key anatomical interfaces, such as the cementoenamel junction, the alveolar bone surface, and root contours. Recent advancements in probe technology (20–40 MHz) enable high-resolution visualization of bone interfaces and soft tissues, as well as actual periodontal pocket depth, and even to detect early signs of bone irregularities or initial attachment loss. This ability to objectively quantify periodontal damage in real time provides a significant advantage for both diagnosis and therapeutic monitoring (9,11,13–16). Moreover, ultrasonography allows for the functional assessment of periodontal tissues. The integration of color Doppler or Power Doppler modules enables the evaluation of gingival vascularization, a parameter directly correlated with active inflammation. The Doppler signal reveals hypervascularization in areas affected by gingivitis or active periodontitis, thus providing a dynamic indicator of the inflammatory state. Conversely, a reduction in vascular signal may reflect stabilization or remission following treatment. This approach represents a significant advancement over conventional methods, which offer only a static structural snapshot without information on the biological activity of the tissues (11,12). Ultimately, these innovations could lead to the development of personalized three-dimensional maps of the periodontium, integrating both morphological and functional data, and paving the way for precision periodontal medicine based on quantitative, objective, and dynamic information (5,15,16). Within this framework, our team previously validated the relevance of ultrasound imaging for periodontal pocket measurement in a preclinical animal model (pig) (17). Building upon this work, the present study aims to qualify and characterize periodontal structures and tissues under both healthy and inflammatory (periodontitis) conditions. This pilot study evaluates the feasibility of high-frequency intraoral ultrasonography for characterizing periodontal tissues and detecting inflammation in patients with periodontitis, building on prior preclinical validation in a porcine model. Following successful validation of ultrasound imaging for periodontal pocket measurement in a porcine model, this pilot study extends the application to human subjects to assess clinical feasibility and tissue characterization under healthy and inflammatory conditions. 2. Methods 2.1 Overview This pilot study was designed as a prospective, single-center clinical trial. Thirteen patients were enrolled in this pilot study to assess feasibility, based on prior studies suggesting that 10–15 participants provide sufficient data for initial validation of imaging techniques (18). Each participant underwent a dedicated ultrasonographic examination using a high-frequency probe to acquire ultrasound images of the periodontal tissues. This was followed by a standard periodontal examination. All assessments were conducted at the Department of Oral Medicine and Surgery, Tours University Hospital (Tours, France). The study was approved by the Institutional Ethics Committee (Approval No. 22.04642.000161) and registered on the International Clinical Trials Registry Platform (ICTRP) under the identifier NCT05809427. 2.2 Recruitment Participants seeking periodontal evaluation were recruited from the Department of Oral Medicine and Surgery at Tours University Hospital. Prior to inclusion in the study, all participants received detailed information about the research protocol and provided written informed consent. 2.3 Inclusion criteria The inclusion criteria were: Patient: 1. ≥ 18 years of age 2. Participants covered by or entitled to social security 3. Written informed consent obtained from the participant or participant’s legal representative 4. Ability for participant to comply with the requirements of the study Teeth: 5. Minimum of 14 teeth 6. Presence of at least one tooth with a pathological periodontal pocket (> or = 4 mm). 2.4 Exclusion criteria The exclusion criteria were: 1. Surgical procedure performed in the area to be scanned 2. Osteosynthesis material 3. Patients following any measures of legal presentation 4. Pregnancy, breastfeeding 5. Inclusion in another therapeutic trial 2.5 Study Visit Organization Each participant attended a single study visit, lasting approximately 60 minutes, during which all protocol procedures were performed by an experienced operator (a dentist trained in the use of the experimental device). The order of assessments was standardized: ultrasonographic evaluation was always performed first, followed by traditional periodontal examination. The following steps were completed sequentially during the visit: 1. Review of eligibility criteria and signing of the informed consent form 2. Collection of demographic data 3. Ultrasonographic imaging of the periodontium with automated measurements using artificial intelligence 4. Manual periodontal probing of all present teeth 5. Documentation of any adverse events or device malfunctions 2.6 Ethical Considerations and Data Confidentiality All data were anonymized using randomly generated patient codes and stored in a secure database in compliance with applicable regulations (General Data Protection Regulation – GDPR, and the French Jardé Law). No personally identifiable information was shared with industrial partners. Statistical analyses were performed in a blinded manner. 2.7 Ultrasound Device The ultrasound device, developed by Carestream Dental in collaboration with the Odontology Department at Tours University Hospital, GREMAN UMR 7347 and U1253 iBraiN, uses a 20 MHz transducer with an axial resolution of 100 µm and a lateral resolution of 150 µm. This device is specially dedicated to the exploration of periodontal tissues and to obtaining quality live images of the human periodontium. Images were acquired using a water-based coupling gel to optimize acoustic transmission. The ultrasonic flow can be visualized on a paired smartphone or tablet hosting the mobile application. 2.8 Standardized Acquisition Procedure The patient was seated in a semi-recumbent position in the dental chair, with the mouth slightly open and relaxed. The ultrasound probe was gently inserted by the operator. Image acquisition followed a systematic sequence: · Scanning of the buccal surfaces of the maxillary arch (from tooth 17 to 27), then of the mandibular arch (from tooth 47 to 37), tooth by tooth, using gentle translational movements (2–3 seconds per tooth) · Scanning of the lingual surfaces following the same sequence · Probe alignment parallel to the long axis of each tooth to ensure measurement reproducibility · Maintenance of a perpendicular angle between the probe head and the tissue surface to minimize deformation artifacts Images were acquired in real time. 2.9 Protocol Workflow and Sequence of Procedures Each participant attended a single visit lasting approximately 60 minutes, during which all steps of the evaluation protocol were conducted in a strictly standardized sequence to ensure measurement reliability and patient safety. 2.10 Protocol Steps 1. Reception and Consent o Verification of inclusion and exclusion criteria o Detailed explanation of the study protocol to the patient o Signing of the informed consent form 2. Patient Installation o Placement in a semi-recumbent position in the dental chair o Preparation of the oral cavity and application of coupling gel 3. Ultrasonographic Imaging o Scanning of all buccal and then lingual surfaces following the standardized procedure o Automatic acquisition, time-stamping, and storage of ultrasound images 4. Manual Periodontal Probing o Performed immediately after ultrasonography to avoid gingival trauma that could alter image quality o Manual recording of probing depths on the patient chart 5. Clinical Data Collection and Feedback o Documentation of any adverse events, patient feedback, and practitioner observations This sequence was chosen to preserve tissue integrity prior to ultrasonographic assessment, as manual probing may induce microtrauma or bleeding that could compromise image quality and automated measurements. Moreover, the non-invasive nature of ultrasound made it suitable as the initial diagnostic step without altering the baseline condition of the periodontium. 2.11 Tissue Characterization Based on Echogenicity Beyond simple measurement of periodontal pocket depth, high-frequency ultrasound imaging enables more detailed analysis of periodontal soft tissues through their echogenic signature. This analysis was integrated into our study to explore the potential of ultrasonography for tissue characterization under both healthy and inflammatory conditions. Echogenicity is defined as the ability of a tissue to reflect ultrasound waves, visually represented by grayscale values on the image (0 = black, 255 = white). This property depends on tissue density and microstructural organization (e.g., fiber orientation, presence of inflammatory infiltrate, vascularization). Two parameters were used for analysis: · Mean pixel intensity, representing the overall echogenicity of the tissue · Standard deviation of pixel intensity, indicating the degree of intra-tissue homogeneity or variability Echogenicity measurements and image interpretation were performed using the ImageJ software. Ultrasound images were saved in JPEG format for evaluation. Each image was converted to 8-bit grayscale. A region of interest (ROI) was defined for each tissue type, and echogenicity analysis was carried out by measuring pixel intensity values (0 = black, 255 = white). ROIs were manually delineated by a trained dentist using anatomical landmarks (e.g., cementoenamel junction, alveolar bone crest) and standardized to 50x50 pixels to ensure consistent echogenicity measurements across tissue types. The mean echogenicity (brightness) and the standard deviation (intra-tissue variability) were recorded in an Excel spreadsheet for each periodontal tissue type: keratinized gingiva, periodontal ligament, connective tissue, enamel, cementum, alveolar bone, and—in cases where present—inflammatory tissue. Outliers were identified using the interquartile range method and excluded from analysis. The main objectives were to assess: · Whether each tissue exhibits a distinct, reproducible, and consistent echogenic signature · Whether the presence of inflammation (e.g., periodontitis) results in a significant change in echogenicity, enabling visual or automated detection This approach aims to lay the groundwork for automated detection of tissue status (healthy vs. pathological) based on raw image data—representing a major advancement in non-invasive periodontal diagnostics. 2.12 Statistical Analysis A descriptive analysis was first conducted. For each tissue type, the global mean (µ) and standard deviation (σ) of individual mean echogenicity values were calculated, along with a 95% confidence interval. Echogenicity distributions were visually compared across tissues using histograms. To assess whether echogenicity values differed significantly between tissues, a tissue separation analysis based on signal intensity was performed. Normality of the data was evaluated using the Shapiro–Wilk test. Given the non-normal distribution of the data, the non-parametric Kruskal–Wallis test was applied to compare mean echogenicity values between groups. All statistical analyses were conducted using RStudio (version 2024.04.1). To further explore the discriminative potential of echogenicity profiles, a linear discriminant analysis (LDA) was performed. In addition, a K-means clustering algorithm (k = 6) was applied to evaluate the intrinsic coherence of tissue-specific echogenicity signatures and to test whether distinct tissue types naturally form separate clusters in an unsupervised setting, without prior labeling. 3. Results Thirteen subjects were included in the study, comprising 5 women and 8 men, with a mean age of 53 years. Fewer ultrasound measurements (1,987 vs. 2,088 manual measurements) were recorded due to occasional signal loss in areas with poor probe contact or acoustic shadowing from calculus. The average duration of manual probing was 6 minutes (interquartile range: 5–6 minutes), whereas the mean duration of ultrasonic probing was 19 minutes (interquartile range: 16–21 minutes). 3.1 Ultrasound Imaging of the Periodontium Ultrasonographic imaging enables real-time visualization of all periodontal structures, including the oral epithelium, alveolodental ligament, cementum, and alveolar bone. Connective tissue can also be identified (Fig. 1 ). Image interpretation highlights the following features: A hyper-echogenic line corresponding to the strong reflection from dense structures such as enamel or alveolar bone An underlying heterogeneous and grainy band, corresponding to fibrous connective tissue (attached gingiva); this area shows moderate echogenicity, suggesting good tissue organization A darker area in deeper regions, where signal attenuation indicates higher bone density Variations in ultrasound images may be observed depending on the clinical condition. Ultrasound imaging can reveal inflammatory changes in the deeper layers of gingival tissue. Supragingival calculusmay also be visible, as well as subgingival calculus, which can be detected on the images. All these features are illustrated in Fig. 1 . Finally, image quality may vary due to fluctuations in the ultrasound signal and tissue interface characteristics. 3.2 Tissue Characterization A total of 228 high-resolution ultrasound images of the periodontal region were analyzed. The following tissues were identified: enamel, cementum, alveolar bone, oral epithelium, connective tissue, and inflammatory tissue. Each region of interest (ROI) was manually delineated by a qualified operator (dentist) and then reviewed and confirmed by a second practitioner with expertise in periodontology and intraoral ultrasonography. In cases of disagreement, the two operators reached a consensus after discussion. A detailed descriptive analysis was performed on the echogenicity distributions for each tissue type (Fig. 2 ). ANOVA was not used due to non-normality of the data, as determined by the Shapiro–Wilk test, which revealed significantly non-normal distributions for the majority of tissues (p < 0.05). Therefore, non-parametric tests were deemed appropriate. A Kruskal–Wallis test was performed to assess overall differences in echogenicity among tissues. The result was highly significant (p < 0.001), confirming that mean echogenicity values differ significantly across tissue types. These results statistically validate echogenicity as a relevant differential marker for periodontal tissue structures (Fig. 3 ). An echogenicity profile was established for each tissue, representing the average grayscale intensity per tissue type (Fig. 4 ). When data were projected onto the discriminant axis, a clear separation was observed between tissues with extreme echogenicity values—such as inflammatory tissue (low signal) and enamel (high signal)—while partial overlap was noted among intermediate tissues (cementum, connective tissue, bone, oral epithelium). These findings indicate that echogenicity carries discriminative information; however, a single parameter is insufficient to fully differentiate all tissue types with certainty. Tissues with intermediate densities exhibited partial overlap, suggesting that additional parameters—such as texture or morphological features—could enhance inter-tissue discrimination (Fig. 5 ). K-means clustering, applied without prior labeling, allowed partial grouping of the data into six clusters. The overall silhouette score was moderate, reflecting the similarity of echogenic profiles among certain tissues. Nonetheless, inflammatory tissue and enamel formed clearly distinct clusters, confirming their unique echogenic signatures (Fig. 6 ). This unsupervised clustering approach demonstrated that tissues could be grouped according to their signal profiles, even without labels, indicating that mean intensity retains a discriminative fingerprint. These results support the potential of echogenicity as a central parameter in future strategies for tissue segmentation or diagnostic assistance in periodontology. 4. Discussion Manual probing, while widely used, is subjective and prone to errors due to probe angulation and tissue deformation (2). One of the advantages of automated measurements derived from imaging is the potential to obtain clinically relevant values relative to histological reference points. Ultrasonography overcomes these limitations by providing objective, real-time imaging of periodontal structures, including soft tissues and inflammation (19). This pilot study was limited by its small sample size (n = 13) and single-center design, which may restrict generalizability. Additionally, echogenicity overlap among intermediate-density tissues (e.g., cementum, connective tissue) suggests the need for additional parameters like texture analysis. Image analysis can enable the detection and even quantification of subgingival calculus and tissue inflammation, supporting both diagnosis and periodontal treatment monitoring. However, ultrasonographic imaging is subject to unique artifacts, such as acoustic shadowing, wherein a structure appears falsely hypoechogenic (darker) due to the presence of a strongly echogenic structure in the ultrasound beam path (13). High-frequency ultrasonography offers potential for non-invasive periodontal diagnostics, enabling early detection of inflammation and calculus. Its integration into routine practice could improve diagnostic accuracy and patient comfort. Periodontal disease diagnosis should increasingly take into account the presence of deep tissue inflammation in order to better align clinical and histological findings, thus improving the characterization of disease states (acute, chronic, early-stage, etc.) (11). This study demonstrates that mean echogenicity enables a first-level, robust differentiation of periodontal tissues. Highly contrasted tissues such as enamel and inflamed tissue are readily distinguishable. Further improvements are expected by integrating additional variables, such as texture features, tissue depth, and spatial position. These results highlight the diagnostic potential of intraoral ultrasonography. While discrimination is excellent for highly contrasted tissues, it remains limited for those with intermediate signal intensities. Incorporating texture and spatial indicators may enhance discrimination of all tissue types. The characterization of periodontal tissues by ultrasound imaging holds significant promise for the automated detection of inflammation, while also providing details on its severity and precise anatomical location based on echogenicity differences. Combining this with tissue texture analysis may further refine these observations. Continued studies are warranted to enhance tissue analysis via ultrasonography. Automated analysis of such data using artificial intelligence also represents a promising avenue for improving periodontal disease diagnosis—potentially shifting the diagnostic paradigm by enabling real-time imaging of all soft tissue structures. Future studies should validate these findings in larger, multicenter cohorts and explore automated tissue segmentation using machine learning algorithms to enhance diagnostic precision. 5. Perspectives The medical device evaluated in this study demonstrates clear clinical relevance and feasibility for routine use in daily practice. From a clinical standpoint, real-time acquisition of ultrasound images offers multiple advantages. Ultrasonographic imaging relies on non-ionizing technology, making it non-invasive and well-suited for clinical exploration. It is also painless for patients and allows for direct, real-time measurements without inducing bleeding (20). Moreover, this type of probing is non-invasive and atraumatic to periodontal tissues. Unlike manual probing, it does not promote bacterial dissemination within the periodontal pocket (12). Real-time imaging of periodontal structures is achievable with this method (21). Importantly, the development and implementation of an intraoral probe equipped with artificial intelligence capable of automatically detecting and recognizing key periodontal tissue types would represent a major advancement. Such a tool would offer significant added value from a diagnostic perspective, with numerous benefits for the patient—including increased comfort, reduced procedure time, and minimized risk of infection. Machine learning algorithms, such as convolutional neural networks, could be trained on echogenicity and texture data to automate the detection of inflammatory tissue and quantify its severity, reducing operator dependency. 6. Conclusion High-frequency intraoral ultrasonography enables robust differentiation of periodontal tissues based on echogenicity, with clear discrimination of enamel and inflammatory tissue. This non-invasive method shows promise for real-time periodontal diagnostics, particularly for inflammation detection. However, limitations such as small sample size and echogenicity overlap among intermediate tissues necessitate further validation in larger clinical studies. Future research should focus on integrating machine learning for automated tissue analysis and exploring in vivo applications. Declarations Ethics Approval: The study was approved by the Institutional Ethics Committee (Approval No. 22.04642.000161) and registered on the International Clinical Trials Registry Platform (ICTRP) under the identifier NCT05809427. This study was conducted in accordance with the Declaration of Helsinki. Consent to Participate: Individual written consent was used for this study. Conflicts of Interest: The authors declare no conflicts of interest. Fundings: This research received no external funding. The study was self-supported, but company Carestream Dental provided free materials to be used in the study. Author Contribution LE and MR wrote the main manuscript text. FD, GR and MR contribute to the methodology. AD and VR contribute to review the manuscript. All authors reviewed the manuscript. Data availability: All data are present in the manuscript, and raw data can be requested from the corresponding author for genuine reasons. References Farci F, Soni A. Histology, Tooth. StatPearls [Internet]. 2023 Jun 26 [cited 2025 Jun 19]; Available from: https://www.ncbi.nlm.nih.gov/books/NBK572055/ Lang NP, Bartold PM. Periodontal health. J Periodontol [Internet]. 2018 Jun 1 [cited 2022 Oct 20];89:S9–16. Available from: https://onlinelibrary.wiley.com/doi/full/10.1002/JPER.16-0517 Lang NP, Joss A, Tonetti MS. Monitoring disease during supportive periodontal treatment by bleeding on probing. Periodontol 2000 [Internet]. 1996 Oct 1 [cited 2022 Oct 20];12(1):44–8. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1600-0757.1996.tb00080.x Nanci A, Bosshardt DD. Structure of periodontal tissues in health and disease. Vol. 40, Periodontology 2000. 2006. p. 11–28. Lang O, Yaya-Stupp D, Traynis I, Cole-Lewis H, Bennett CR, Lyles CR, et al. Using generative AI to investigate medical imagery models and datasets. EBioMedicine. 2024 Apr 1;102. Karayiannis A, Lang NP, Joss A, Nyman S. Bleeding on probing as it relates to probing pressure and gingival health in patients with a reduced but healthy periodontium. J Clin Periodontol [Internet]. 1992 Aug 1 [cited 2022 Oct 20];19(7):471–5. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1600-051X.1992.tb01159.x Wolf DL, Lamster IB. Contemporary Concepts in the Diagnosis of Periodontal Disease. Dent Clin North Am [Internet]. 2011 Jan 1 [cited 2022 Oct 19];55(1):47–61. Available from: http://www.dental.theclinics.com/article/S0011853210000868/fulltext Corbet EF, Ho DKL, Lai SML. Radiographs in periodontal disease diagnosis and management. Vol. 54, Australian Dental Journal. Blackwell Publishing; 2009. p. S27–43. Chifor R, Badea AF, Chifor I, Mitrea DA, Crisan M, Badea ME. Periodontal evaluation using a non-invasive imaging method (ultrasonography). Med Pharm Rep [Internet]. 2019 [cited 2023 Dec 4];92(Suppl No 3):20–32. Available from: https://pubmed.ncbi.nlm.nih.gov/31989105/ Development and application of an ultrasonic imaging system for dental diagnosis - Fukukita − 1985 - Journal of Clinical Ultrasound - Wiley Online Library [Internet]. [cited 2022 Oct 19]. Available from: https://onlinelibrary.wiley.com/doi/10.1002/1097-0096(199010)13:8%3C597::AID-JCU1870130818%3E3.0.CO;2-H Renaud M, Delpierre A, Becquet H, Mahalli R, Savard G, Micheneau P, et al. Intraoral Ultrasonography for Periodontal Tissue Exploration: A Review. Diagnostics (Basel) [Internet]. 2023 Feb 1 [cited 2023 Nov 2];13(3). Available from: https://pubmed.ncbi.nlm.nih.gov/36766470/ Renaud M, Gette M, Delpierre A, Calle S, Levassort F, Denis F, et al. Intraoral Ultrasonography for the Exploration of Periodontal Tissues: A Technological Leap for Oral Diagnosis. Diagnostics (Basel) [Internet]. 2024 Jul 1 [cited 2025 Jun 13];14(13). Available from: https://pubmed.ncbi.nlm.nih.gov/39001225/ Salmon B, Le Denmat D. Intraoral ultrasonography: development of a specific high-frequency probe and clinical pilot study. Clinical Oral Investigations 2011 16:2 [Internet]. 2011 Mar 5 [cited 2022 Oct 17];16(2):643–9. Available from: https://link.springer.com/article/10.1007/s00784-011-0533-z Tsiolis FI, Needleman IG, Griffiths GS. Periodontal ultrasonography. J Clin Periodontol [Internet]. 2003 Oct 1 [cited 2022 Oct 17];30(10):849–54. Available from: https://onlinelibrary.wiley.com/doi/full/10.1034/j.1600-051X.2003.00380.x Parihar AS, Narang S, Tyagi S, Narang A, Dwivedi S, Katoch V, et al. Artificial Intelligence in Periodontics: A Comprehensive Review. Vol. 16, Journal of Pharmacy and Bioallied Sciences. Wolters Kluwer Medknow Publications; 2024. p. S1956–8. Pitchika V, Büttner M, Schwendicke F. Artificial intelligence and personalized diagnostics in periodontology: A narrative review. Periodontology 2000. John Wiley and Sons Inc; 2024. Delpierre A, Slimane BA, Estrade L, Manget J, Callé S, Levassort F, et al. Sulcus measurement by high frequency ultrasound imaging ex-vivo: an exploratory study. Med Ultrason [Internet]. 2025 Apr 29 [cited 2025 May 30]; Available from: https://pubmed.ncbi.nlm.nih.gov/40349376/ Julious SA. Sample size of 12 per group rule of thumb for a pilot study. Pharm Stat [Internet]. 2005 Oct 1 [cited 2025 Jul 8];4(4):287–91. Available from: https://onlinelibrary.wiley.com/doi/full/10.1002/pst.185 BP F, CE H, CQ H, JS G, AM P, TE R. Reproducibility of Manual Periodontal Probing Following a Comprehensive Standardization and Calibration Training Program. J Oral Biol (Northborough) [Internet]. 2022 [cited 2023 Dec 4];8(1). Available from: https://pubmed.ncbi.nlm.nih.gov/36225716/ Chan HL, Wang HL, Fowlkes JB, Giannobile W V., Kripfgans OD. Non-ionizing real-time ultrasonography in implant and oral surgery: A feasibility study. Clin Oral Implants Res [Internet]. 2017 Mar 1 [cited 2022 Oct 17];28(3):341–7. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/clr.12805 Kositsky A, Gonçalves BAM, Stenroth L, Barrett RS, Diamond LE, Saxby DJ. Reliability and Validity of Ultrasonography for Measurement of Hamstring Muscle and Tendon Cross-Sectional Area. Ultrasound Med Biol [Internet]. 2020 Jan 1 [cited 2023 Dec 4];46(1):55–63. Available from: https://pubmed.ncbi.nlm.nih.gov/31668942/ Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Dec, 2025 Read the published version in BMC Oral Health → Version 1 posted Editorial decision: Revision requested 03 Sep, 2025 Reviews received at journal 01 Sep, 2025 Reviews received at journal 31 Aug, 2025 Reviews received at journal 29 Aug, 2025 Reviewers agreed at journal 24 Aug, 2025 Reviewers agreed at journal 23 Aug, 2025 Reviewers agreed at journal 21 Aug, 2025 Reviewers agreed at journal 21 Aug, 2025 Reviewers invited by journal 19 Aug, 2025 Editor invited by journal 15 Jul, 2025 Editor assigned by journal 12 Jul, 2025 Submission checks completed at journal 12 Jul, 2025 First submitted to journal 10 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7093377","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504845401,"identity":"17178690-41bf-41ba-a12f-c3b2e23e4a4b","order_by":0,"name":"Laurent Estrade","email":"","orcid":"","institution":"Tours University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Laurent","middleName":"","lastName":"Estrade","suffix":""},{"id":504845402,"identity":"f9bb7678-32cd-434d-94cc-6dc0003dfca2","order_by":1,"name":"Alexis Delpierre","email":"","orcid":"","institution":"Tours University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Alexis","middleName":"","lastName":"Delpierre","suffix":""},{"id":504845403,"identity":"304f995d-7a22-42a9-ac3b-fb9c4ef9c863","order_by":2,"name":"Victor Rimbaud","email":"","orcid":"","institution":"Tours University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Victor","middleName":"","lastName":"Rimbaud","suffix":""},{"id":504845404,"identity":"7fe09f38-4505-430a-830b-6991c6e12c37","order_by":3,"name":"Frédéric Denis","email":"","orcid":"","institution":"Tours University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Frédéric","middleName":"","lastName":"Denis","suffix":""},{"id":504845405,"identity":"3743c585-7933-4191-9ef6-ae7d0c158b47","order_by":4,"name":"Gaël Y Rochefort","email":"","orcid":"","institution":"Tours University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Gaël","middleName":"Y","lastName":"Rochefort","suffix":""},{"id":504845406,"identity":"818e324d-4754-4e3f-90ac-e86a0186fab3","order_by":5,"name":"Matthieu Renaud","email":"data:image/png;base64,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","orcid":"","institution":"Tours University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Matthieu","middleName":"","lastName":"Renaud","suffix":""}],"badges":[],"createdAt":"2025-07-10 13:08:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7093377/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7093377/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12903-025-07485-y","type":"published","date":"2025-12-11T15:59:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90301212,"identity":"0e13b05d-53d8-4c86-8870-3d7f3969679d","added_by":"auto","created_at":"2025-09-01 08:57:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":393770,"visible":true,"origin":"","legend":"\u003cp\u003eUltrasound image of periodontal tissues. (A/Enamel, B/Cemento-enamel junction, C/alveolar bone, D/connective tissue, E/keratinized gingiva, F/sulcular space)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7093377/v1/69a20720c10d9e8e159b1f85.png"},{"id":90302084,"identity":"ec716be3-c6cd-41d7-b22c-a8016649ff29","added_by":"auto","created_at":"2025-09-01 09:05:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":414084,"visible":true,"origin":"","legend":"\u003cp\u003eTissue-Specific Descriptive Statistical Analysis\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7093377/v1/2df8768a3fb061a9421851f8.png"},{"id":90301215,"identity":"d2db5486-2489-4106-85ea-f40bec41b238","added_by":"auto","created_at":"2025-09-01 08:57:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":148434,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of mean echogenicities by tissue\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7093377/v1/1f5bbe92e75781d6324cea1f.png"},{"id":90301217,"identity":"145176f7-caf8-418c-a251-153a87881374","added_by":"auto","created_at":"2025-09-01 08:57:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":202450,"visible":true,"origin":"","legend":"\u003cp\u003eUltrasound signatures as a function of tissue (gray scale)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7093377/v1/b2546a1aaacd25ce5b78e531.png"},{"id":90301219,"identity":"8c8b8853-7970-495a-9513-7c36a8423a6a","added_by":"auto","created_at":"2025-09-01 08:57:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":126996,"visible":true,"origin":"","legend":"\u003cp\u003eData projected onto the discriminant axis\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7093377/v1/e5271206a1ed38872c455c72.png"},{"id":90301222,"identity":"ca07cce0-9c57-4d1e-9ee5-1a5cc4b1be4c","added_by":"auto","created_at":"2025-09-01 08:57:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":673484,"visible":true,"origin":"","legend":"\u003cp\u003eGrouping of data into 6 classes: 0/ Inflammatory tissue; 1/ Enamel; 2/ Connective tissue or keratinized gingiva; 3/ Alveolar bone or cementum; 4/ other intermediate cluster not clearly identified; 5/ Mixed group or noise\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7093377/v1/888ec44aeb0d78f94e634520.png"},{"id":98244768,"identity":"a555b48b-4aa6-412d-8ff4-e0850198c989","added_by":"auto","created_at":"2025-12-15 16:14:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2037214,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7093377/v1/51ed4752-d114-44b9-86ce-55f1644ee297.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"High-Frequency Intraoral Ultrasonography for Periodontal Tissue Characterization: A Pilot Study Exploring Echogenic Signatures and Inflammation Detection","fulltext":[{"header":"1. Background","content":"\u003cp\u003eThe tooth is a specialized anatomical structure whose functions are mechanical (mastication), sensory, phonatory, and aesthetic. It is composed of both mineralized and non-mineralized tissues. The crown\u0026mdash;the visible portion in the oral cavity\u0026mdash;is covered with enamel, an extremely hard and acellular tissue. The root is covered with cementum, a tissue analogous to bone, which plays a key role in anchoring the tooth within the alveolar socket. The periodontium, comprising gingiva, periodontal ligament, cementum, and alveolar bone, supports and protects teeth (1). The gingiva is the visible part of the periodontium and is subdivided into free gingiva, attached gingiva, and interdental papilla. It is covered by a stratified squamous keratinized (or parakeratinized) epithelium, supported by an underlying connective tissue that is richly vascularized. At the interface between the gingival epithelium and the tooth surface lies the junctional epithelium, a key structure in the defense against bacterial aggression (2,3).\u003c/p\u003e\u003cp\u003eThe alveolodental ligament is a fibrous connective tissue located between the dental root and the alveolar bone, composed of collagen fibers organized into various bundles. It not only ensures the anchorage of the tooth but also contributes to the absorption of masticatory forces and proprioceptive perception. Cementum covers the dental root and serves as an attachment site for periodontal fibers. Finally, the alveolar bone surrounds the root and dynamically adapts to functional stresses and inflammatory conditions. Although histologically distinct, these tissues function in an integrated manner to maintain periodontal health. Their alteration, particularly in chronic inflammatory contexts, leads to a cascade of destructive processes affecting the tooth's supporting tissues (gingiva, periodontal ligament, cementum, alveolar bone) (1,2,4). Periodontal diseases, such as gingivitis and periodontitis, are major public health concerns due to their prevalence and destructive impact (2).\u003c/p\u003e\u003cp\u003eClinical periodontal assessment relies on manual probing and radiography, both of which have limitations. Manual probing is subjective and may cause discomfort (3), while intraoral radiographs provide only two-dimensional views and cannot assess soft tissues or early inflammation (5). Clinically, the periodontal pocket is defined as a pathological deepening of the gingival sulcus, resulting from the destruction of the tooth\u0026rsquo;s supporting tissues. Its measurement is based on precise anatomical landmarks (6).\u003c/p\u003e\u003cp\u003eRadiographic examination is a key complement to clinical assessment in periodontology. It is primarily used to evaluate the condition of the alveolar bone, identify horizontal or vertical bone loss, and detect potential periapical lesions, calcifications, or subgingival calculus deposits. Intraoral radiographs (periapical, bitewing) are the most commonly used in daily practice due to their high resolution and their ability to provide detailed views of local dental and bony structures. However, they offer only a two-dimensional representation of a three-dimensional anatomical situation, which can limit interpretation\u0026mdash;especially when bone defects are located on the palatal or lingual surfaces (7,8).\u003c/p\u003e\u003cp\u003eNevertheless, radiographic examination has several important limitations in the context of periodontal assessment. First, it does not allow direct visualization of soft tissues (gingiva, junctional epithelium, periodontal ligament), nor of active inflammation that often precedes observable bone loss. Second, radiographic images do not permit measurement of periodontal pocket depth, nor do they reliably detect early signs of bone demineralization before significant loss has occurred. Additionally, anatomical superimpositions and variations in projection angulation can affect the accuracy of interpretation (4,7,8).\u003c/p\u003e\u003cp\u003eIn this context, ultrasonographic imaging emerges as a promising technology. High-frequency ultrasonography offers non-invasive, real-time imaging of periodontal structures without ionizing radiation, including soft tissues such as the marginal gingiva, junctional epithelium, and periodontal ligament. This opens the door to a more objective and dynamic assessment of pocket depth, tissue structure, and inflammation (9\u0026ndash;14). Ultrasound imaging is not limited to the visualization of superficial soft tissues; it also enables fine assessment of key anatomical interfaces, such as the cementoenamel junction, the alveolar bone surface, and root contours.\u003c/p\u003e\u003cp\u003eRecent advancements in probe technology (20\u0026ndash;40 MHz) enable high-resolution visualization of bone interfaces and soft tissues, as well as actual periodontal pocket depth, and even to detect early signs of bone irregularities or initial attachment loss. This ability to objectively quantify periodontal damage in real time provides a significant advantage for both diagnosis and therapeutic monitoring (9,11,13\u0026ndash;16).\u003c/p\u003e\u003cp\u003eMoreover, ultrasonography allows for the functional assessment of periodontal tissues. The integration of color Doppler or Power Doppler modules enables the evaluation of gingival vascularization, a parameter directly correlated with active inflammation. The Doppler signal reveals hypervascularization in areas affected by gingivitis or active periodontitis, thus providing a dynamic indicator of the inflammatory state. Conversely, a reduction in vascular signal may reflect stabilization or remission following treatment. This approach represents a significant advancement over conventional methods, which offer only a static structural snapshot without information on the biological activity of the tissues (11,12).\u003c/p\u003e\u003cp\u003eUltimately, these innovations could lead to the development of personalized three-dimensional maps of the periodontium, integrating both morphological and functional data, and paving the way for precision periodontal medicine based on quantitative, objective, and dynamic information (5,15,16).\u003c/p\u003e\u003cp\u003eWithin this framework, our team previously validated the relevance of ultrasound imaging for periodontal pocket measurement in a preclinical animal model (pig) (17). Building upon this work, the present study aims to qualify and characterize periodontal structures and tissues under both healthy and inflammatory (periodontitis) conditions.\u003c/p\u003e\u003cp\u003eThis pilot study evaluates the feasibility of high-frequency intraoral ultrasonography for characterizing periodontal tissues and detecting inflammation in patients with periodontitis, building on prior preclinical validation in a porcine model. Following successful validation of ultrasound imaging for periodontal pocket measurement in a porcine model, this pilot study extends the application to human subjects to assess clinical feasibility and tissue characterization under healthy and inflammatory conditions.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cem\u003e2.1 Overview\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis pilot study was designed as a prospective, single-center clinical trial. Thirteen patients were enrolled in this pilot study to assess feasibility, based on prior studies suggesting that 10\u0026ndash;15 participants provide sufficient data for initial validation of imaging techniques (18). Each participant underwent a dedicated ultrasonographic examination using a high-frequency probe to acquire ultrasound images of the periodontal tissues. This was followed by a standard periodontal examination. All assessments were conducted at the Department of Oral Medicine and Surgery, Tours University Hospital (Tours, France). The study was approved by the Institutional Ethics Committee (Approval No. 22.04642.000161) and registered on the International Clinical Trials Registry Platform (ICTRP) under the identifier NCT05809427.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.2 \u0026nbsp;\u003c/em\u003e\u003cem\u003eRecruitment\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eParticipants seeking periodontal evaluation were recruited from the Department of Oral Medicine and Surgery at Tours University Hospital. Prior to inclusion in the study, all participants received detailed information about the research protocol and provided written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.3\u0026nbsp;\u0026nbsp;\u003c/em\u003e\u003cem\u003eInclusion criteria\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe inclusion criteria were:\u003c/p\u003e\n\u003cp\u003ePatient:\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026ge; 18 years of age\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp;\u0026nbsp;Participants covered by or entitled to social security\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp;\u0026nbsp;Written informed consent obtained from the participant or participant\u0026rsquo;s legal representative\u003c/p\u003e\n\u003cp\u003e4.\u0026nbsp; \u0026nbsp;\u0026nbsp;Ability for participant to comply with the requirements of the study\u003c/p\u003e\n\u003cp\u003eTeeth:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e5.\u0026nbsp; \u0026nbsp;\u0026nbsp;Minimum of 14 teeth\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e6.\u0026nbsp; \u0026nbsp;\u0026nbsp;Presence of at least one tooth with a pathological periodontal pocket (\u0026gt; or = 4 mm).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.4\u0026nbsp;\u0026nbsp;\u003c/em\u003e\u003cem\u003eExclusion criteria\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe exclusion criteria were:\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp;\u0026nbsp;Surgical procedure performed in the area to be scanned\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp;\u0026nbsp;Osteosynthesis material\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp;\u0026nbsp;Patients following any measures of legal presentation\u003c/p\u003e\n\u003cp\u003e4. \u0026nbsp; \u0026nbsp;Pregnancy, breastfeeding\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e5. \u0026nbsp; \u0026nbsp;Inclusion in another therapeutic trial\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.5 \u0026nbsp;\u003c/em\u003e\u003cem\u003eStudy Visit Organization\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEach participant attended a single study visit, lasting approximately 60 minutes, during which all protocol procedures were performed by an experienced operator (a dentist trained in the use of the experimental device).\u003c/p\u003e\n\u003cp\u003eThe order of assessments was standardized: ultrasonographic evaluation was always performed first, followed by traditional periodontal examination.\u003c/p\u003e\n\u003cp\u003eThe following steps were completed sequentially during the visit:\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp;\u0026nbsp;Review of eligibility criteria and signing of the informed consent form\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp;\u0026nbsp;Collection of demographic data\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp;\u0026nbsp;Ultrasonographic imaging of the periodontium with automated measurements using artificial intelligence\u003c/p\u003e\n\u003cp\u003e4. \u0026nbsp; \u0026nbsp;Manual periodontal probing of all present teeth\u003c/p\u003e\n\u003cp\u003e5. \u0026nbsp; \u0026nbsp;Documentation of any adverse events or device malfunctions\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.6 \u0026nbsp;\u003c/em\u003e\u003cem\u003eEthical Considerations and Data Confidentiality\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;All data were anonymized using randomly generated patient codes and stored in a secure database in compliance with applicable regulations (General Data Protection Regulation \u0026ndash; GDPR, and the French Jard\u0026eacute; Law). No personally identifiable information was shared with industrial partners. Statistical analyses were performed in a blinded manner.\u003c/p\u003e\n\u003cp\u003e2.7 \u0026nbsp;Ultrasound Device\u003c/p\u003e\n\u003cp\u003eThe ultrasound device, developed by Carestream Dental in collaboration with the Odontology Department at Tours University Hospital, GREMAN UMR 7347 and U1253 iBraiN, uses a 20 MHz transducer with an axial resolution of 100 \u0026micro;m and a lateral resolution of 150 \u0026micro;m. This device is specially dedicated to the exploration of periodontal tissues and to obtaining quality live images of the human periodontium. Images were acquired using a water-based coupling gel to optimize acoustic transmission. The ultrasonic flow can be visualized on a paired smartphone or tablet hosting the mobile application.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.8 \u0026nbsp;\u003c/em\u003e\u003cem\u003eStandardized Acquisition Procedure\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe patient was seated in a semi-recumbent position in the dental chair, with the mouth slightly open and relaxed. The ultrasound probe was gently inserted by the operator. Image acquisition followed a systematic sequence:\u003c/p\u003e\n\u003cp\u003e\u0026middot; Scanning of the buccal surfaces of the maxillary arch (from tooth 17 to 27), then of the mandibular arch (from tooth 47 to 37), tooth by tooth, using gentle translational movements (2\u0026ndash;3 seconds per tooth)\u003c/p\u003e\n\u003cp\u003e\u0026middot; Scanning of the lingual surfaces following the same sequence\u003c/p\u003e\n\u003cp\u003e\u0026middot; Probe alignment parallel to the long axis of each tooth to ensure measurement reproducibility\u003c/p\u003e\n\u003cp\u003e\u0026middot; Maintenance of a perpendicular angle between the probe head and the tissue surface to minimize deformation artifacts\u003c/p\u003e\n\u003cp\u003eImages were acquired in real time.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.9\u0026nbsp;\u0026nbsp;\u003c/em\u003e\u003cem\u003eProtocol Workflow and Sequence of Procedures\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEach participant attended a single visit lasting approximately 60 minutes, during which all steps of the evaluation protocol were conducted in a strictly standardized sequence to ensure measurement reliability and patient safety.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.10\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u0026nbsp;Protocol Steps\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e1. \u003cstrong\u003eReception and Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eo Verification of inclusion and exclusion criteria\u003c/p\u003e\n\u003cp\u003eo Detailed explanation of the study protocol to the patient\u003c/p\u003e\n\u003cp\u003eo Signing of the informed consent form\u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003ePatient Installation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eo Placement in a semi-recumbent position in the dental chair\u003c/p\u003e\n\u003cp\u003eo Preparation of the oral cavity and application of coupling gel\u003c/p\u003e\n\u003cp\u003e3. \u003cstrong\u003eUltrasonographic Imaging\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eo Scanning of all buccal and then lingual surfaces following the standardized procedure\u003c/p\u003e\n\u003cp\u003eo Automatic acquisition, time-stamping, and storage of ultrasound images\u003c/p\u003e\n\u003cp\u003e4. \u003cstrong\u003eManual Periodontal Probing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eo Performed immediately after ultrasonography to avoid gingival trauma that could alter image quality\u003c/p\u003e\n\u003cp\u003eo Manual recording of probing depths on the patient chart\u003c/p\u003e\n\u003cp\u003e5. \u003cstrong\u003eClinical Data Collection and Feedback\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eo Documentation of any adverse events, patient feedback, and practitioner observations\u003c/p\u003e\n\u003cp\u003eThis sequence was chosen to preserve tissue integrity prior to ultrasonographic assessment, as manual probing may induce microtrauma or bleeding that could compromise image quality and automated measurements. Moreover, the non-invasive nature of ultrasound made it suitable as the initial diagnostic step without altering the baseline condition of the periodontium.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.11\u0026nbsp;\u003c/em\u003e\u003cem\u003eTissue Characterization Based on Echogenicity\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBeyond simple measurement of periodontal pocket depth, high-frequency ultrasound imaging enables more detailed analysis of periodontal soft tissues through their echogenic signature. This analysis was integrated into our study to explore the potential of ultrasonography for tissue characterization under both healthy and inflammatory conditions.\u003c/p\u003e\n\u003cp\u003eEchogenicity is defined as the ability of a tissue to reflect ultrasound waves, visually represented by grayscale values on the image (0 = black, 255 = white). This property depends on tissue density and microstructural organization (e.g., fiber orientation, presence of inflammatory infiltrate, vascularization).\u003c/p\u003e\n\u003cp\u003eTwo parameters were used for analysis:\u003c/p\u003e\n\u003cp\u003e\u0026middot; Mean pixel intensity, representing the overall echogenicity of the tissue\u003c/p\u003e\n\u003cp\u003e\u0026middot; Standard deviation of pixel intensity, indicating the degree of intra-tissue homogeneity or variability\u003c/p\u003e\n\u003cp\u003eEchogenicity measurements and image interpretation were performed using the ImageJ software. Ultrasound images were saved in JPEG format for evaluation. Each image was converted to 8-bit grayscale. A region of interest (ROI) was defined for each tissue type, and echogenicity analysis was carried out by measuring pixel intensity values (0 = black, 255 = white). ROIs were manually delineated by a trained dentist using anatomical landmarks (e.g., cementoenamel junction, alveolar bone crest) and standardized to 50x50 pixels to ensure consistent echogenicity measurements across tissue types. The mean echogenicity (brightness) and the standard deviation (intra-tissue variability) were recorded in an Excel spreadsheet for each periodontal tissue type: keratinized gingiva, periodontal ligament, connective tissue, enamel, cementum, alveolar bone, and\u0026mdash;in cases where present\u0026mdash;inflammatory tissue. Outliers were identified using the interquartile range method and excluded from analysis.\u003c/p\u003e\n\u003cp\u003eThe main objectives were to assess:\u003c/p\u003e\n\u003cp\u003e\u0026middot; Whether each tissue exhibits a distinct, reproducible, and consistent echogenic signature\u003c/p\u003e\n\u003cp\u003e\u0026middot; Whether the presence of inflammation (e.g., periodontitis) results in a significant change in echogenicity, enabling visual or automated detection\u003c/p\u003e\n\u003cp\u003eThis approach aims to lay the groundwork for automated detection of tissue status (healthy vs. pathological) based on raw image data\u0026mdash;representing a major advancement in non-invasive periodontal diagnostics.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.12\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u0026nbsp;Statistical Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA descriptive analysis was first conducted. For each tissue type, the global mean (\u0026micro;) and standard deviation (\u0026sigma;) of individual mean echogenicity values were calculated, along with a 95% confidence interval. Echogenicity distributions were visually compared across tissues using histograms.\u003c/p\u003e\n\u003cp\u003eTo assess whether echogenicity values differed significantly between tissues, a tissue separation analysis based on signal intensity was performed. Normality of the data was evaluated using the Shapiro\u0026ndash;Wilk test. Given the non-normal distribution of the data, the non-parametric Kruskal\u0026ndash;Wallis test was applied to compare mean echogenicity values between groups. All statistical analyses were conducted using RStudio (version 2024.04.1).\u003c/p\u003e\n\u003cp\u003eTo further explore the discriminative potential of echogenicity profiles, a linear discriminant analysis (LDA) was performed. In addition, a K-means clustering algorithm (k = 6) was applied to evaluate the intrinsic coherence of tissue-specific echogenicity signatures and to test whether distinct tissue types naturally form separate clusters in an unsupervised setting, without prior labeling.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eThirteen subjects were included in the study, comprising 5 women and 8 men, with a mean age of 53 years. Fewer ultrasound measurements (1,987 vs. 2,088 manual measurements) were recorded due to occasional signal loss in areas with poor probe contact or acoustic shadowing from calculus. The average duration of manual probing was 6 minutes (interquartile range: 5\u0026ndash;6 minutes), whereas the mean duration of ultrasonic probing was 19 minutes (interquartile range: 16\u0026ndash;21 minutes).\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Ultrasound Imaging of the Periodontium\u003c/h2\u003e\u003cp\u003eUltrasonographic imaging enables real-time visualization of all periodontal structures, including the oral epithelium, alveolodental ligament, cementum, and alveolar bone. Connective tissue can also be identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eImage interpretation highlights the following features:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eA hyper-echogenic line corresponding to the strong reflection from dense structures such as enamel or alveolar bone\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAn underlying heterogeneous and grainy band, corresponding to fibrous connective tissue (attached gingiva); this area shows moderate echogenicity, suggesting good tissue organization\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eA darker area in deeper regions, where signal attenuation indicates higher bone density\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eVariations in ultrasound images may be observed depending on the clinical condition.\u003c/p\u003e\u003cp\u003eUltrasound imaging can reveal inflammatory changes in the deeper layers of gingival tissue. Supragingival calculusmay also be visible, as well as subgingival calculus, which can be detected on the images. All these features are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eFinally, image quality may vary due to fluctuations in the ultrasound signal and tissue interface characteristics.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Tissue Characterization\u003c/h2\u003e\u003cp\u003eA total of 228 high-resolution ultrasound images of the periodontal region were analyzed. The following tissues were identified: enamel, cementum, alveolar bone, oral epithelium, connective tissue, and inflammatory tissue. Each region of interest (ROI) was manually delineated by a qualified operator (dentist) and then reviewed and confirmed by a second practitioner with expertise in periodontology and intraoral ultrasonography. In cases of disagreement, the two operators reached a consensus after discussion.\u003c/p\u003e\u003cp\u003eA detailed descriptive analysis was performed on the echogenicity distributions for each tissue type (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eANOVA was not used due to non-normality of the data, as determined by the Shapiro\u0026ndash;Wilk test, which revealed significantly non-normal distributions for the majority of tissues (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Therefore, non-parametric tests were deemed appropriate.\u003c/p\u003e\u003cp\u003eA Kruskal\u0026ndash;Wallis test was performed to assess overall differences in echogenicity among tissues. The result was highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming that mean echogenicity values differ significantly across tissue types. These results statistically validate echogenicity as a relevant differential marker for periodontal tissue structures (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAn echogenicity profile was established for each tissue, representing the average grayscale intensity per tissue type (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). When data were projected onto the discriminant axis, a clear separation was observed between tissues with extreme echogenicity values\u0026mdash;such as inflammatory tissue (low signal) and enamel (high signal)\u0026mdash;while partial overlap was noted among intermediate tissues (cementum, connective tissue, bone, oral epithelium).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThese findings indicate that echogenicity carries discriminative information; however, a single parameter is insufficient to fully differentiate all tissue types with certainty. Tissues with intermediate densities exhibited partial overlap, suggesting that additional parameters\u0026mdash;such as texture or morphological features\u0026mdash;could enhance inter-tissue discrimination (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eK-means clustering, applied without prior labeling, allowed partial grouping of the data into six clusters. The overall silhouette score was moderate, reflecting the similarity of echogenic profiles among certain tissues. Nonetheless, inflammatory tissue and enamel formed clearly distinct clusters, confirming their unique echogenic signatures (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThis unsupervised clustering approach demonstrated that tissues could be grouped according to their signal profiles, even without labels, indicating that mean intensity retains a discriminative fingerprint.\u003c/p\u003e\u003cp\u003eThese results support the potential of echogenicity as a central parameter in future strategies for tissue segmentation or diagnostic assistance in periodontology.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eManual probing, while widely used, is subjective and prone to errors due to probe angulation and tissue deformation (2). One of the advantages of automated measurements derived from imaging is the potential to obtain clinically relevant values relative to histological reference points. Ultrasonography overcomes these limitations by providing objective, real-time imaging of periodontal structures, including soft tissues and inflammation (19).\u003c/p\u003e\u003cp\u003eThis pilot study was limited by its small sample size (n\u0026thinsp;=\u0026thinsp;13) and single-center design, which may restrict generalizability. Additionally, echogenicity overlap among intermediate-density tissues (e.g., cementum, connective tissue) suggests the need for additional parameters like texture analysis.\u003c/p\u003e\u003cp\u003eImage analysis can enable the detection and even quantification of subgingival calculus and tissue inflammation, supporting both diagnosis and periodontal treatment monitoring. However, ultrasonographic imaging is subject to unique artifacts, such as acoustic shadowing, wherein a structure appears falsely hypoechogenic (darker) due to the presence of a strongly echogenic structure in the ultrasound beam path (13).\u003c/p\u003e\u003cp\u003eHigh-frequency ultrasonography offers potential for non-invasive periodontal diagnostics, enabling early detection of inflammation and calculus. Its integration into routine practice could improve diagnostic accuracy and patient comfort. Periodontal disease diagnosis should increasingly take into account the presence of deep tissue inflammation in order to better align clinical and histological findings, thus improving the characterization of disease states (acute, chronic, early-stage, etc.) (11).\u003c/p\u003e\u003cp\u003eThis study demonstrates that mean echogenicity enables a first-level, robust differentiation of periodontal tissues. Highly contrasted tissues such as enamel and inflamed tissue are readily distinguishable. Further improvements are expected by integrating additional variables, such as texture features, tissue depth, and spatial position. These results highlight the diagnostic potential of intraoral ultrasonography. While discrimination is excellent for highly contrasted tissues, it remains limited for those with intermediate signal intensities. Incorporating texture and spatial indicators may enhance discrimination of all tissue types.\u003c/p\u003e\u003cp\u003eThe characterization of periodontal tissues by ultrasound imaging holds significant promise for the automated detection of inflammation, while also providing details on its severity and precise anatomical location based on echogenicity differences. Combining this with tissue texture analysis may further refine these observations. Continued studies are warranted to enhance tissue analysis via ultrasonography. Automated analysis of such data using artificial intelligence also represents a promising avenue for improving periodontal disease diagnosis\u0026mdash;potentially shifting the diagnostic paradigm by enabling real-time imaging of all soft tissue structures. Future studies should validate these findings in larger, multicenter cohorts and explore automated tissue segmentation using machine learning algorithms to enhance diagnostic precision.\u003c/p\u003e"},{"header":"5. Perspectives","content":"\u003cp\u003eThe medical device evaluated in this study demonstrates clear clinical relevance and feasibility for routine use in daily practice. From a clinical standpoint, real-time acquisition of ultrasound images offers multiple advantages. Ultrasonographic imaging relies on non-ionizing technology, making it non-invasive and well-suited for clinical exploration. It is also painless for patients and allows for direct, real-time measurements without inducing bleeding (20).\u003c/p\u003e\u003cp\u003eMoreover, this type of probing is non-invasive and atraumatic to periodontal tissues. Unlike manual probing, it does not promote bacterial dissemination within the periodontal pocket (12). Real-time imaging of periodontal structures is achievable with this method (21).\u003c/p\u003e\u003cp\u003eImportantly, the development and implementation of an intraoral probe equipped with artificial intelligence capable of automatically detecting and recognizing key periodontal tissue types would represent a major advancement. Such a tool would offer significant added value from a diagnostic perspective, with numerous benefits for the patient\u0026mdash;including increased comfort, reduced procedure time, and minimized risk of infection.\u003c/p\u003e\u003cp\u003eMachine learning algorithms, such as convolutional neural networks, could be trained on echogenicity and texture data to automate the detection of inflammatory tissue and quantify its severity, reducing operator dependency.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eHigh-frequency intraoral ultrasonography enables robust differentiation of periodontal tissues based on echogenicity, with clear discrimination of enamel and inflammatory tissue. This non-invasive method shows promise for real-time periodontal diagnostics, particularly for inflammation detection. However, limitations such as small sample size and echogenicity overlap among intermediate tissues necessitate further validation in larger clinical studies. Future research should focus on integrating machine learning for automated tissue analysis and exploring in vivo applications.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics Approval:\u003c/h2\u003e\n\u003cp\u003eThe study was approved by the Institutional Ethics Committee (Approval No. 22.04642.000161) and registered on the International Clinical Trials Registry Platform (ICTRP) under the identifier NCT05809427. This study was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003ch2\u003eConsent to Participate:\u003c/h2\u003e\n\u003cp\u003eIndividual written consent was used for this study.\u003c/p\u003e\n\u003ch2\u003eConflicts of Interest:\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003ch2\u003eFundings:\u003c/h2\u003e\n\u003cp\u003eThis research received no external funding. The study was self-supported, but company Carestream Dental provided free materials to be used in the study.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eLE and MR wrote the main manuscript text. FD, GR and MR contribute to the methodology. AD and VR contribute to review the manuscript. All authors reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eData availability:\u003c/h2\u003e\n\u003cp\u003eAll data are present in the manuscript, and raw data can be requested from the corresponding author for genuine reasons.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFarci F, Soni A. Histology, Tooth. StatPearls [Internet]. 2023 Jun 26 [cited 2025 Jun 19]; Available from: https://www.ncbi.nlm.nih.gov/books/NBK572055/\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLang NP, Bartold PM. Periodontal health. J Periodontol [Internet]. 2018 Jun 1 [cited 2022 Oct 20];89:S9\u0026ndash;16. Available from: https://onlinelibrary.wiley.com/doi/full/10.1002/JPER.16-0517\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLang NP, Joss A, Tonetti MS. Monitoring disease during supportive periodontal treatment by bleeding on probing. Periodontol 2000 [Internet]. 1996 Oct 1 [cited 2022 Oct 20];12(1):44\u0026ndash;8. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1600-0757.1996.tb00080.x\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNanci A, Bosshardt DD. Structure of periodontal tissues in health and disease. Vol. 40, Periodontology 2000. 2006. p. 11\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLang O, Yaya-Stupp D, Traynis I, Cole-Lewis H, Bennett CR, Lyles CR, et al. Using generative AI to investigate medical imagery models and datasets. EBioMedicine. 2024 Apr 1;102.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarayiannis A, Lang NP, Joss A, Nyman S. Bleeding on probing as it relates to probing pressure and gingival health in patients with a reduced but healthy periodontium. J Clin Periodontol [Internet]. 1992 Aug 1 [cited 2022 Oct 20];19(7):471\u0026ndash;5. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1600-051X.1992.tb01159.x\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWolf DL, Lamster IB. Contemporary Concepts in the Diagnosis of Periodontal Disease. Dent Clin North Am [Internet]. 2011 Jan 1 [cited 2022 Oct 19];55(1):47\u0026ndash;61. Available from: http://www.dental.theclinics.com/article/S0011853210000868/fulltext\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCorbet EF, Ho DKL, Lai SML. Radiographs in periodontal disease diagnosis and management. Vol. 54, Australian Dental Journal. Blackwell Publishing; 2009. p. S27\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChifor R, Badea AF, Chifor I, Mitrea DA, Crisan M, Badea ME. Periodontal evaluation using a non-invasive imaging method (ultrasonography). Med Pharm Rep [Internet]. 2019 [cited 2023 Dec 4];92(Suppl No 3):20\u0026ndash;32. Available from: https://pubmed.ncbi.nlm.nih.gov/31989105/\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDevelopment and application of an ultrasonic imaging system for dental diagnosis - Fukukita \u0026minus;\u0026thinsp;1985 - Journal of Clinical Ultrasound - Wiley Online Library [Internet]. [cited 2022 Oct 19]. Available from: https://onlinelibrary.wiley.com/doi/10.1002/1097-0096(199010)13:8%3C597::AID-JCU1870130818%3E3.0.CO;2-H\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRenaud M, Delpierre A, Becquet H, Mahalli R, Savard G, Micheneau P, et al. Intraoral Ultrasonography for Periodontal Tissue Exploration: A Review. Diagnostics (Basel) [Internet]. 2023 Feb 1 [cited 2023 Nov 2];13(3). Available from: https://pubmed.ncbi.nlm.nih.gov/36766470/\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRenaud M, Gette M, Delpierre A, Calle S, Levassort F, Denis F, et al. Intraoral Ultrasonography for the Exploration of Periodontal Tissues: A Technological Leap for Oral Diagnosis. Diagnostics (Basel) [Internet]. 2024 Jul 1 [cited 2025 Jun 13];14(13). Available from: https://pubmed.ncbi.nlm.nih.gov/39001225/\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSalmon B, Le Denmat D. Intraoral ultrasonography: development of a specific high-frequency probe and clinical pilot study. Clinical Oral Investigations 2011 16:2 [Internet]. 2011 Mar 5 [cited 2022 Oct 17];16(2):643\u0026ndash;9. Available from: https://link.springer.com/article/10.1007/s00784-011-0533-z\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTsiolis FI, Needleman IG, Griffiths GS. Periodontal ultrasonography. J Clin Periodontol [Internet]. 2003 Oct 1 [cited 2022 Oct 17];30(10):849\u0026ndash;54. Available from: https://onlinelibrary.wiley.com/doi/full/10.1034/j.1600-051X.2003.00380.x\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eParihar AS, Narang S, Tyagi S, Narang A, Dwivedi S, Katoch V, et al. Artificial Intelligence in Periodontics: A Comprehensive Review. Vol. 16, Journal of Pharmacy and Bioallied Sciences. Wolters Kluwer Medknow Publications; 2024. p. S1956\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePitchika V, B\u0026uuml;ttner M, Schwendicke F. Artificial intelligence and personalized diagnostics in periodontology: A narrative review. Periodontology 2000. John Wiley and Sons Inc; 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDelpierre A, Slimane BA, Estrade L, Manget J, Call\u0026eacute; S, Levassort F, et al. Sulcus measurement by high frequency ultrasound imaging ex-vivo: an exploratory study. Med Ultrason [Internet]. 2025 Apr 29 [cited 2025 May 30]; Available from: https://pubmed.ncbi.nlm.nih.gov/40349376/\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJulious SA. Sample size of 12 per group rule of thumb for a pilot study. Pharm Stat [Internet]. 2005 Oct 1 [cited 2025 Jul 8];4(4):287\u0026ndash;91. Available from: https://onlinelibrary.wiley.com/doi/full/10.1002/pst.185\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBP F, CE H, CQ H, JS G, AM P, TE R. Reproducibility of Manual Periodontal Probing Following a Comprehensive Standardization and Calibration Training Program. J Oral Biol (Northborough) [Internet]. 2022 [cited 2023 Dec 4];8(1). Available from: https://pubmed.ncbi.nlm.nih.gov/36225716/\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChan HL, Wang HL, Fowlkes JB, Giannobile W V., Kripfgans OD. Non-ionizing real-time ultrasonography in implant and oral surgery: A feasibility study. Clin Oral Implants Res [Internet]. 2017 Mar 1 [cited 2022 Oct 17];28(3):341\u0026ndash;7. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/clr.12805\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKositsky A, Gon\u0026ccedil;alves BAM, Stenroth L, Barrett RS, Diamond LE, Saxby DJ. Reliability and Validity of Ultrasonography for Measurement of Hamstring Muscle and Tendon Cross-Sectional Area. Ultrasound Med Biol [Internet]. 2020 Jan 1 [cited 2023 Dec 4];46(1):55\u0026ndash;63. Available from: https://pubmed.ncbi.nlm.nih.gov/31668942/\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"high-frequency ultrasonography, periodontal tissues, echogenicity, inflammation detection, non-invasive diagnostics, periodontal pocket","lastPublishedDoi":"10.21203/rs.3.rs-7093377/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7093377/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the feasibility and diagnostic potential of high-frequency intraoral ultrasonography for the real-time evaluation and characterization of periodontal tissues, including tissue echogenicity under both healthy and inflammatory conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis prospective single-center pilot study included 13 patients diagnosed with periodontitis. Each participant underwent a standardized examination combining high frequency ultrasonographic imaging (20 MHz). A total of 1,987 ultrasonic ultrasonography measurements were recorded. Ultrasonographic images were analyzed using ImageJ to assess the mean and standard deviation of pixel intensity across several periodontal tissue types. Statistical analyses included Kruskal–Wallis testing, linear discriminant analysis (LDA), and K-means clustering to explore echogenicity-based tissue differentiation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUltrasound imaging enabled visualization of periodontal structures (enamel, cementum, alveolar bone, connective tissue, inflammatory tissue). Mean echogenicity values differed significantly across tissue types (p \u0026lt; 0.001), with enamel showing the highest mean pixel intensity (200 ± 15) and inflammatory tissue the lowest (50 ± 10). LDA achieved partial tissue separation, while K-means clustering identified six distinct echogenicity-based clusters. Real-time imaging detected subgingival calculus and deep tissue inflammation in 8 of 13 patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigh-frequency intraoral ultrasonography is a feasible, non-invasive method for real-time periodontal tissue characterization. Echogenicity provides a robust marker for tissue differentiation and inflammation detection, but further studies with larger cohorts are needed to validate its clinical utility and automate diagnostic processes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration\u003c/strong\u003e: The study was approved by the Institutional Ethics Committee (Approval No. 22.04642.000161) and registered on the International Clinical Trials Registry Platform (ICTRP) under the identifier NCT05809427 (07/07/2023).\u003c/p\u003e","manuscriptTitle":"High-Frequency Intraoral Ultrasonography for Periodontal Tissue Characterization: A Pilot Study Exploring Echogenic Signatures and Inflammation Detection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 08:57:39","doi":"10.21203/rs.3.rs-7093377/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-03T07:20:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-01T12:23:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-31T20:01:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-29T13:17:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"47920225819638637151924416169860484310","date":"2025-08-24T08:13:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136398641516744236527730077490155490501","date":"2025-08-23T11:09:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"219919421312016012253755444986739139488","date":"2025-08-22T00:18:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"91513306798311111326620013867187945339","date":"2025-08-21T20:44:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-19T13:50:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-15T15:33:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-12T11:05:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-12T11:04:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Oral Health","date":"2025-07-10T13:04:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"99dc19c4-0ca0-4375-8b87-639cdd068bf6","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:08:49+00:00","versionOfRecord":{"articleIdentity":"rs-7093377","link":"https://doi.org/10.1186/s12903-025-07485-y","journal":{"identity":"bmc-oral-health","isVorOnly":false,"title":"BMC Oral Health"},"publishedOn":"2025-12-11 15:59:11","publishedOnDateReadable":"December 11th, 2025"},"versionCreatedAt":"2025-09-01 08:57:39","video":"","vorDoi":"10.1186/s12903-025-07485-y","vorDoiUrl":"https://doi.org/10.1186/s12903-025-07485-y","workflowStages":[]},"version":"v1","identity":"rs-7093377","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7093377","identity":"rs-7093377","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}