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. 2021 Sep 13;101(3):278–285. doi: 10.1177/00220345211035684

Ultrasonographic Tissue Perfusion in Peri-implant Health and Disease

S Barootchi 1,2,✉, L Tavelli 1,2, J Majzoub 1, HL Chan 1, HL Wang 1, OD Kripfgans 3,4
PMCID: PMC8982012  PMID: 34515570

Abstract

Color flow ultrasonography has played a crucial role in medicine for its ability to assess dynamic tissue perfusion and blood flow variations as an indicator of a pathologic condition. While this feature of ultrasound is routinely employed in various medical fields, its intraoral application for the assessment of tissue perfusion at diseased versus healthy dental implants has never been explored. We tested the hypothesis that quantified tissue perfusion of power Doppler ultrasonography correlates with the clinically assessed inflammation of dental implants. Specifically, we designed a discordant-matched case-control study in which patients with nonadjacent dental implants with different clinical diagnoses (healthy, peri-implant mucositis, or peri-implantitis) were scanned and analyzed with real-time ultrasonography. Forty-two posterior implants in 21 patients were included. Ultrasound scans were obtained at the implant regions of midbuccal, mesial/distal (averaged as interproximal), and transverse to compute the velocity- and power-weighted color pixel density from color velocity (CV) and color power (CP), respectively. Linear mixed effect models were then used to assess the relationship between the clinical diagnoses and ultrasound CV and CP. Overall, the results strongly suggested that ultrasound’s quantified CV and CP directly correlate with the clinical diagnosis of dental implants at health, peri-implant mucositis, and peri-implantitis. This study showed for the first time that ultrasound color flow can be applicable in the diagnosis of peri-implant disease and can act as a valuable tool for evaluating the degree of clinical inflammation at implant sites.

Keywords: blood flow velocity, ultrasonography, dental implant, peri-implantitis, peri-implant disease, evidence-based dentistry

Introduction

Implant therapy has become the standard treatment for replacement of missing teeth. Nevertheless, with its popularity, there has been a rise in aesthetic and biological complications and the emergence of peri-implant diseases (Derks et al. 2016a; Renvert et al. 2018; Wang et al. 2021). The 2017 World Workshop on peri-implant diseases and conditions (Berglundh et al. 2018) recognized peri-implant mucositis and peri-implantitis as inflammatory conditions affecting the implant soft tissues, with the latter involving the peri-implant hard tissues and loss of the implant’s supporting bone (Schwarz et al. 2018).

Despite numerous attempts and research on different therapeutic approaches, the literature has yet to find a predictable and effective solution for management of a peri-implant disease, particularly with advanced and severe cases (Faggion et al. 2014; Tomasi et al. 2019; Barootchi, Ravida et al. 2020). Thus, there has been a growing emphasis on maintaining implant health and the early diagnosis and treatment of a peri-implant disease (Jepsen et al. 2015; Heitz-Mayfield et al. 2020).

The clinical diagnosis of an implant currently relies on the measurement of probing depth and inspection for signs of localized clinical inflammation (erythema, bleeding/suppuration on probing, etc.). This, with the information provided by 2-dimensional (2D) radiography, is typically used for the diagnosis and determination of the case definition of an implant’s health status (Berglundh et al. 2018; Renvert et al. 2018).

Despite simplicity and versatility of a periodontal probe, its diagnostic accuracy for the assessment of inflammation, to dental implants in particular, is limited by several factors and is greatly subjective (van der Velden and Jansen 1981; Karayiannis et al. 1992; Abrahamsson and Soldini 2006; Serino et al. 2013; Hashim et al. 2018; Renvert et al. 2018). Moreover, even with accurate probing (with light force, <0.25 N), bleeding on probing and suppuration are binary observations, assessed at a single time point. Periapical radiography may bear its own limitations as well (superimposition of structures and static anatomic information).

For decades in the medical arena, real-time ultrasonography has played an invaluable role in the diagnosis and evaluation of a variety of conditions (Moore and Copel 2011; Oglat et al. 2018). A remarkable advantage of ultrasound is its ability to noninvasively and instantaneously assess dynamic tissue perfusion (the passage of blood through a circulatory system or contrast agent through lymphatics) and, therefore, blood perfusion through color flow ultrasonography as an indicator of the degree of inflammation (Hernandez-Andrade et al. 2007; Welsh et al. 2019). While color flow/power ultrasonography has been utilized in various medical fields, its diagnostic application in the oral cavity relative to dental implants has never been explored. Thus, the aim of this preliminary study was to assess if the quantified tissue perfusion of color flow/power ultrasonography correlates with the clinical diagnosis of dental implants in health and disease.

Materials and Methods

Study Design and Registration

The current study was designed as a discordant-pairs case-control cross-sectional analysis. Specifically, patients having 2 nonadjacent posterior dental implants with different clinical diagnoses were assessed at a single time point, with the goal of exploring the potential of ultrasound color flow to correlate with the clinical diagnosis of dental implants. The study protocol was in accordance with the Declaration of Helsinki of 1965, as revisited in Tokyo in 2004, and was approved by the Institutional Review Board of the University of Michigan Medical School (HUM00170906). The current report has been prepared following the items presented by the STROBE guidelines (Appendix).

Additional details on recruitment, eligibility criteria, and data collection are available in the Appendix.

Clinical Visit and Ultrasonography Image Acquisition

At the time of the visit, a study team member (S.B.) collected implant and demographic data, including relevant medical and dental history, general implant characteristics, range of probing depths, bleeding on probing and suppuration, as well as periapical radiographs. The clinical diagnoses of dental implants were confirmed according to the 2017 World Workshop (Berglundh et al. 2018; Renvert et al. 2018) by the same examiner (Appendix).

Next, a commercially available ultrasound imaging device (ZS3; Mindray) was used to generate the ultrasound images. The device was coupled with a linear array transducer (L30-8; 128 elements, 8- to 30-MHz bandwidth) of miniature size (~30 × 18 × 12 mm) and set to 24 MHz (64-µm axial image resolution) (Fig. 1).

Figure 1.

Figure 1.

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The ultrasound setup in this study (top) and an example of an implant with peri-implantitis as viewed in different ultrasound scans (bottom). (A) The Mindray dental ultrasound machine. (B) Front view of the miniature probe prototype (L30-8) prior to preparation. (C) Front and (D) lateral views of the probe prototype after preparation for clinical intraoral use. (E) An example of the application of the ultrasound probe at the time of acquiring an image. Note that an anterior site is selected for demonstration purposes. The bottom series of images present (F) the clinical image of an implant with peri-implantitis and its (G) midbuccal, (H) mesial, (I) distal, and (J) transverse ultrasound B-mode scans. Abt, implant abutment; C, crown; CB, crestal bone; DP, distal papilla; I, implant; MP, mesial papilla; mpC, mesial portion of crown; PL, peri-implant lesion; ST, soft tissue.

The ultrasound procedures (equipment setup, scanning protocol, etc.) are outlined in previous reports (Barootchi, Chan, et al. 2020; Chan and Kripfgans 2020; Tavelli et al. 2021) and detailed in the Appendix, with complete descriptions on 3 imaging modalities (Fig. 2).

Figure 2.

Figure 2.

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Representations of the ultrasound imaging modes (B-mode, color velocity, color power) and the 4 implant regions of dental implants in each clinical diagnosis group. The displayed color velocity visualizes the velocity (speed) at which blood flows, while color power shows the amount of blood flowing within the ultrasound beam in field of view. Color velocity imaging was performed with a constant velocity scale (±2.3 cm/s), with red indicating blood flow toward the transducer and blue denoting blood flow in the opposite direction. Color power is displayed in a single hue of red.

B-mode

B-mode generates 2D cross-sectional anatomic grayscale images.

Color Flow (Color Doppler)

For color flow, the B-mode display is overlaid with additional color pixels that represent the detected blood flow velocities. The flowing red blood cells scatter the ultrasound wave and produce a scattered signal that changes in radio frequency phase as long as the direction of the motion is nonperpendicular to the ultrasound beam.

The displayed color velocity (CV) is the projection of the actual velocity onto the ultrasound beam—that is, the measured mean phase shift, which mathematically equals the multiplication of the true mean velocity within the point-spread function by the cosine of the angle to the ultrasound beam, thus visualizing the velocity at which blood flows within the lumens in the field of view.

Color Power

Color power (CP), also based on detecting phase change of the received ultrasound signal, is displayed as a single hue of red. Since the power is derived from the integrated phase shift, CP scales with the amount of blood flowing within the ultrasound beam point-spread function, and it is particularly useful for detecting flow in small vessels or low-velocity flow. For higher tissue perfusion, more blood vessels might be active, and the vasculature might show vasodilation; thus, more blood might be flowing (Chan and Kripfgans 2020; Tavelli et al. 2021). The images were acquired in “still images” (a single image of the field of view) and “cine loops” (videos generated as a series of consecutive still images at a frame rate of 20 Hz).

Peri-implant Regions of Assessment

B-mode, CV, and CP were assessed at 4 distinct regions (Fig. 3): midbuccal, mesial (line angle between the crown and the mesial papilla), distal (line angle between the crown and the distal papilla), and transverse (approximately 3 mm below the crown margin). Still images were acquired for all scans. Cine loops (videos) were recorded for CV and CP to quantify real-time tissue perfusion (Appendix Video 1).

Figure 3.

Figure 3.

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Ultrasound color velocity and color power of a patient who contributed 2 symmetrical dental implants with different clinical diagnoses. The ultrasound imaging modalities employed in this study, alongside the clinical and 2-dimensional radiographic representation of the dental implants, are shown for the 4 peri-implant regions. Note that for the midbuccal, mesial, and distal scans, the ultrasound probe was oriented parallel to the long axis of the implant and perpendicular to the occlusal plane, while for the transverse scan, the probe was oriented parallel to the occlusal plane. The video file of this figure is available as Appendix Video 1.

Ultrasonography Blood Flow Volume Calculation

The velocity (speed)–weighted color pixel density and power-weighted color pixel density were computed from CV and CP, respectively, as previously described (Tavelli et al. 2021) and detailed in the Appendix.

Statistical Analysis

A linear mixed model was used to assess the relationship among the implants’ clinical diagnoses (3 levels: health, peri-implant mucositis, and peri-implantitis), the implant region (3 levels: midbuccal, interproximal [average of mesial and distal], and transverse), and the outcomes of CV and CP. For each outcome, 9 submodels were then fit, each equating 1 pair of clinical diagnosis level within 1 of the 3 implant regions (3 health diagnosis, 3 implant regions). These submodels were used to assess for significant differences in CV and CP between 2 clinical levels within 1 site via the Akaike information criterion (AIC). Differences in AIC (ΔAIC) were recorded to quantify evidence for such differences (Burnham and Anderson 2002).

All models accounted for the fact that every patient contributed 2 implants with different diagnoses to assess the possible influence of patient- and implant-level confounders on the outcomes by including random effects for patients and implants (reported as fractional variance contributions).

The analyses were performed by a study team member with experience in statistical methodology (S.B.) using RStudio software (version 1.1.383; RStudio, Inc.) and the statistical packages lme4 and dplyr. Box plots were constructed with the ggplot2 package for demonstration of data based on the AIC comparisons of the mixed models.

Results

Characteristics of the Population and Dental Implants

Forty-two nonadjacent posterior dental implants in 21 patients (13 male, 8 female; mean ± SD age, 56.23 ± 11.61 y) were assessed with ultrasonography and included in this study. Nine patients (mean age, 52.6 y; 6 male, 3 female) had a healthy implant and an implant with peri-implantitis; 7 patients (mean age, 49 y; 4 male, 3 female), a healthy implant and an implant with peri-implant mucositis; and 5 patients (mean age, 47.6 y; 3 male, 2 female), an implant with peri-implantitis and an implant with peri-implant mucositis (Fig. 3). Overall, 16 implants were diagnosed as clinically healthy, 12 with peri-implant mucositis, and 14 with peri-implantitis. All were bone-level implants and in function for at least 3 y prior to the date of ultrasonography. In all patients, the 2 selected implants were in different quadrants of the oral cavity. The Table presents additional information regarding the analyzed implants.

Table.

Characteristics of the Implants per Clinical Diagnosis.

Clinical Diagnosis, No. of Implants (%)
Characteristic Health(n = 16) Peri-implant Mucositis (n = 12) Peri-implantitis (n = 14)
Arch
 Maxillary 7 (43.7) 6 (50) 5 (35.7)
 Mandibular 9 (6.2) 6 (50) 9 (64.2)
Site
 Premolar 7 (43.7) 1 (8.3) 6 (42.8)
 Molar 9 (56.2) 11 (91.6) 8 (57.1)
Prosthesis
 Screw-retained restoration 13 (81.2) 7 (58.3) 11 (78.5)
 Cement-retained restoration 3 (18.7) 5 (41.6) 3 (21.4)
Time in function, mo a 58.2 ± 24.8 75.1 ± 32.6 67.1 ± 17.1
Implant systems
 Straumann Bone Level 2 (12.5) 2 (16.6) —
 Zimmer Tapered Screw-Vent 11 (68.7) 5 (41.6) 6 (42.8)
 Nobel Biocare 3 (18.7) 5 (41.6) 8 (57.1)
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a

Mean ± SD.

Tissue Perfusion Analysis and Implant Clinical Diagnosis

Analysis of Color Velocity (CV)

The mixed models and AIC values strongly indicated that the clinical diagnosis and implant region interact in predicting CV. The submodel AIC comparisons indicated that each pair within the 3 clinical diagnosis level is strongly different within the midbuccal region (healthy vs. peri-implantitis, ΔAIC = 69.4; healthy vs. peri-implant mucositis, ΔAIC = 16.46; peri-implant mucositis vs. peri-implantitis, ΔAIC = 27.9) and the transverse region (healthy vs. peri-implantitis, ΔAIC = 26.1; healthy vs. peri-implant mucositis, ΔAIC = 45.2; peri-implant mucositis vs. peri-implantitis, ΔAIC = 6.8). Within the interproximal assessment, clinical diagnoses of peri-implantitis and healthy (ΔAIC = 8.5) and peri-implantitis and peri-implant mucositis (ΔAIC = 6.4) were significantly different, while peri-implant mucositis and healthy sites (ΔAIC = −5.5) did not reach a statistically significant difference.

The fractional variance contributions of patient and implant were 2.1% and 35.8%, respectively, and the unexplained fractional variance amounted to 62.1%.

Analysis of Color Power (CP)

For the analysis of CP, the submodel AIC comparisons indicated that within the midbuccal region, each pair within the 3 clinical diagnosis levels is strongly different (healthy vs. peri-implantitis, ΔAIC = 16.2; healthy vs. peri-implant mucositis, ΔAIC = 5.9; peri-implant mucositis vs. peri-implantitis, ΔAIC = 6.8). Within the transverse region, the 2 pairs of peri-implantitis and healthy (ΔAIC = 11.2) and peri-implantitis and peri-implant mucositis (ΔAIC = 6.2) were significantly different. For the interproximal region, only the peri-implantitis and healthy groups (ΔAIC = −3.4) showed a statistically significant difference.

The fractional variance contributions of patient and implant were 6.1% and 38.8%, respectively, and the unexplained fractional variance amounted to 55.1%.

Figure 4 displays the quantified CV and CP of the implants per region per clinical diagnosis.

Figure 4.

Figure 4.

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Box plots visualizing the quantified color velocity (left) and color power (right) measures per implant region and clinical diagnosis. Diamond, mean; asterisk, outlier; horizontal line, median; vertical lines, 95% CI. Statistically significant differences based on Akaike information criterion comparisons between pairs of clinical diagnoses are shown with braces and asterisks.

Discussion

The present study was designed to investigate the potential of quantified tissue perfusion of point-of-care color power ultrasonography in correlating with the clinical diagnosis of dental implants in health and disease according to the case definitions proposed by the 2017 World Workshop.

Ultrasonography is commonly known as a safe and reliable real-time cross-sectional imaging tool and has been used by physicians worldwide for at least half a century (Moore and Copel 2011). It works by acquiring scattered and reflected acoustic waves that are emitted through a transducer array as they encounter tissue interfaces (Chan and Kripfgans 2020). In static tissues, these waves possess constant phase relationships (Moore and Copel 2011). During tissue movement (as in blood flow), a phase shift is observed for the reflected waves, and this information is used to form B-mode superimposed 2D color flow images. The mean phase shift is computed to derive velocity information, whereas the integrated power of the phase-shifted signal is used to compute the CP to form B-mode superimposed 2D CP images (Chan and Kripfgans 2020). These facets of ultrasonography and the quantified measurements of CV and CP (used as a surrogate for tissue perfusion) have become a milestone across a diversity of medical disciplines and grown to be a full portion of diagnostics, in particular for distinguishing normal from abnormal blood flow (Pinter et al. 2015; Oglat et al. 2018; Pinter et al. 2018; Rubin et al. 2021).

More recent technological advancements have allowed for point-of-care clinical application of ultrasound, which entails its chairside use in real time for obtaining nearly immediate and dynamic images that allow for direct correlation with patients’ signs and symptoms and the object’s clinical representation (Moore and Copel 2011; Chan et al. 2017). In dentistry, studies have utilized ultrasonography for the assessment of anatomic structures, maxillofacial fractures, detection of dental decay, measurement of soft tissue thickness, and so on (Marotti et al. 2013; Barootchi, Chan, et al. 2020). Additionally, vascular perfusion in the periodontia has been investigated through other modalities, such as laser Doppler flowmetry and fluorescein angiography (Mormann and Ciancio 1977; Ambrosini et al. 2002). However, the application of color flow (velocity and power) ultrasonography to discriminate dynamic tissue perfusion at healthy versus diseased dental implants has never been explored.

In a recent preliminary assessment of 5 patients who underwent ultrasonography after a peri-implant soft tissue–grafting procedure, we analyzed the blood flow variation at the palatal donor site and the augmented soft tissues over 12 mo (Tavelli et al. 2021). Despite the limited sample size, we consistently observed an increase in CV and CP at the augmented sites and the palatal wound during the early time points of the healing period (1 wk and 1 mo). Given the rise and the ongoing challenges of implant biologic complications (Heitz-Mayfield and Salvi 2018; Schwarz et al. 2018), the application of color flow/power ultrasonography could prove particularly beneficial in the field of peri-implant disease.

Currently, the diagnostic tools for the assessment of peri-implant health currently stem from the foundation of periodontology and the notion that the clinical diagnoses of implant diseases share similarities with those of natural teeth. Nevertheless, studies have shown distinct differences between the periodontia and the peri-implant tissues, as well as variations in their response to different stimuli or interventions (Hammerle et al. 1996; Ivanovski and Lee 2018; Yuan et al. 2021). Epidemiologic reports have suggested differences in disease patterns and progression rates between the entities (Fransson et al. 2010; Derks et al. 2016b). The prevalence of peri-implant diseases varies significantly due to factors inherent to the nature and design of dental implants (placement, prosthetic design, implant threads, location, thresholds for diagnosis, etc.; Tomasi and Derks 2012; Derks and Tomasi 2015). Variations in the pressure applied on a periodontal probe, its angulation, the resistance of the peri-implant tissues, and the prosthesis design may hinder an accurate diagnosis or result in different measurements in the assessment of disease progression or outcomes of therapy (Serino et al. 2013; Ivanovski and Lee 2018). Employment of more advanced diagnostic means can alleviate these concerns, all of which are among the advantages inherently provided by ultrasonography.

Despite the preliminary nature of this study, we observed that ultrasound’s assessment of peri-implant soft tissue perfusion provides significant diagnostic resolution, especially at the midbuccal region. CV and CP were remarkably increased (200% to 300%) in implants with peri-implantitis as compared with those that were clinically healthy. Except for CP at the transverse region, the measurements increased significantly with the stage of the peri-implant disease. In fact, some of the differences yielded ΔAICs in the magnitude of the 60s, whereas differences in AIC scores of 3 to 4 are commonly known as vast and meaningful variations (Burnham and Anderson 2002).

Notably, there was some overlap between the interproximal assessment of healthy and peri-implant mucositis sites for CV and CP and for the transverse region of CP. The reason behind these findings is open to speculation. One might simply be due to our sample size. Another may be due to that fact that we averaged the mesial and distal measurements to obtain a single interproximal score. Considering that the diagnosis of dental implants is implant- and not site-specific, it may be possible that merging the interproximal sites could have led to some variations between the determination of ultrasound blood flow/perfusion and the clinical assessment. Indeed, studies have suggested differences in the extent and patterns of peri-implant lesions (Schwarz et al. 2007; Monje et al. 2019). Furthermore, given the false-positive rates of bleeding on probing for diagnosing clinical inflammation (Hashim et al. 2018), it could be plausible that some of the clinically determined peri-implant mucositis sites were in fact “healthy” or that some of the implants in the clinically assessed healthy group that exhibited higher ultrasound measurements were indicative of signs of subclinical inflammation.

As an attribute of our discordant-pairs design, all patients contributed 2 implants with different diagnoses to eliminate the risk of patient-level confounding as much as possible. Indeed, in both analyses of CV and CP, the random effects showed very low unexplained patient variance (2.1% and 6.1%, respectively). From a statistical standpoint, this may either hint towards the point that there was minimum inter-patient heterogeneity in the assessment of CV and CP for any single diagnosis group, or that the clinical diagnoses were so informative, that after removing their effect, there was no remaining variance explained by patient factors.

Last, one should bear in mind that ultrasound is a user-dependent technology and its knowledge and proper application require a learning curve and indeed calibration, as done for this research. In fact, all current study team members who took part in ultrasonography were calibrated during past studies and as part of an ongoing clinical trial. This report was intended to inspire future application of ultrasound in the assessment of peri-implant tissue structures and perfusion analysis. Studies with a larger sample size and different designs are underway and indeed required for generalizability of our preliminary findings and to explore additional parameters and fine-tune the diagnostic capacity of this approach. In addition, as related to the site specificity of dental implant conditions, we note that in this preliminary assessment, we did not include the assessment of palatal/lingual sides, which should be included in future studies for a comprehensive and detailed site-specific evaluation. Last, longitudinal assessment of implants and implant sites at different stages of health and disease would be ideal for investigating variations relative to interventions or stimuli.

Conclusion

To the best of our knowledge, this study showed for the first time the diagnostic capacity of ultrasound color flow/power in correlating with the clinical diagnoses of dental implants in health and disease. This technology may serve as a valuable tool for evaluating the degree of inflammation at implant sites and in the assessment of therapeutic outcomes.

Author Contributions

S. Barootchi, designed and carried out the clinical study, performed the statistical analysis, initiated the draft of the report; L. Tavelli, contributed to study design and ultrasound data analysis, critically revised the manuscript; J. Majzoub, contributed to analysis of ultrasound data, critically revised the manuscript; H.L. Chan, contributed to study design and conception, contributed to writing of sections regarding ultrasonography, critically revised the manuscript; H.L. Wang, contributed to study conception, critically revised the manuscript; O.D. Kripfgans, contributed to study design and conception, performed ultrasound quantification and tissue perfusion analysis, drafted the sections regarding ultrasonography. All authors gave final approval and agree to be accountable for all aspects of the work.

Supplemental Material

sj-docx-1-jdr-10.1177_00220345211035684 – Supplemental material for Ultrasonographic Tissue Perfusion in Peri-implant Health and Disease
Click here for additional data file. (47.8KB, docx)

Supplemental material, sj-docx-1-jdr-10.1177_00220345211035684 for Ultrasonographic Tissue Perfusion in Peri-implant Health and Disease by S. Barootchi, L. Tavelli, J. Majzoub, H.L. Chan, H.L. Wang and O.D. Kripfgans in Journal of Dental Research

Footnotes

A supplemental appendix to this article is available online.

Declaration of Conflicting Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The conception of this study was supported by the National Institute of Dental and Craniofacial Research (1R21DE027765-01A1 and 1R21DE029005-01A1) and the National Center for Advancing Translational Sciences (UL1TR000433) of the National Institutes of Health, the Delta Dental Foundation (AWD010089 and AWD004687), the American Academy of Periodontology (Sunstar Innovation Award; AWD007224), and the University of Michigan Department of Periodontics and Oral Medicine (clinical research grant).

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sj-docx-1-jdr-10.1177_00220345211035684 – Supplemental material for Ultrasonographic Tissue Perfusion in Peri-implant Health and Disease
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Supplemental material, sj-docx-1-jdr-10.1177_00220345211035684 for Ultrasonographic Tissue Perfusion in Peri-implant Health and Disease by S. Barootchi, L. Tavelli, J. Majzoub, H.L. Chan, H.L. Wang and O.D. Kripfgans in Journal of Dental Research

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