Advancing the diagnosis of peri‐implant diseases: From histological foundations to next‐generation ultrasonographic diagnostic markers

Abstract

Background

This critical review provides a comprehensive analysis of the histological foundations, current clinical diagnostic standards, and emerging ultrasonographic methods for diagnosing peri‐implant diseases.

Methods

An electronic search in Medline (PubMed) database was conducted to identify original and review articles focused on the histological foundation and diagnostic approaches of peri‐implant diseases. This review provides an integrated perspective on the evolution of peri‐implant diseases diagnostics, focusing on the clinical and radiographic criteria established by the latest consensus guidelines. It outlines the histological features of peri‐implant diseases and discusses recent advancements in ultrasonographic imaging approaches for their characterization and diagnosis. This review further explores elements such as peri‐implant anatomical landmarks, echo intensity, tissue perfusion, and strain elastography.

Results

Histological studies have defined the peri‐implant mucosal architecture and inflammatory patterns characteristic of disease progression. While conventional diagnostics have relied on clinical examination and radiographic imaging, high‐frequency ultrasound (HFUS) has recently emerged as a promising noninvasive imaging tool enabling detailed assessment of peri‐implant tissue dimensions, echo intensity, vascular perfusion, and the extent of inflammatory involvement. Echo intensity mapping aids in the visualization and quantification of peri‐implant lesions and the identification of associated bone defects. These imaging advancements not only complement current diagnostic methods but also deepen our understanding of peri‐implant disease dynamics. Observed correlations between histological findings and ultrasonographic features can support the development of a new diagnostic framework incorporating HFUS‐derived markers for detecting peri‐implant lesions and evaluating the severity of inflammation.

Conclusions

Integrating HFUS and power Doppler imaging into peri‐implant diagnostics allows real‐time, noninvasive visualization of key structures such as the supracrestal adherent connective tissue, buccal bone levels, and tissue perfusion. These modalities can complement existing standards and enhance diagnostic precision by identifying the extent of peri‐implant lesions and quantifying the severity of inflammation.

Plain language summary

Peri‐implant diseases can affect the tissues around dental implants and can lead to inflammation and bone loss. Understanding how these diseases develop and how best to diagnose them is essential for improving patient care. The present narrative review looked at scientific studies that examined the tissues around implants and the methods used to diagnose peri‐implant diseases. It explains what researchers have learned from tissue (histological) studies and summarizes the current clinical and X‐ray based diagnostic standards. It also highlights new advances in high‐frequency ultrasound (HFUS), a technology that can provide detailed, real‐time images of the soft tissues and bone around implants without radiation. Recent studies show that HFUS can measure tissue thickness, detect inflammation, visualize blood flow, and help identify early bone defects. These ultrasound features match what is known from histology, suggesting that HFUS could become an important tool for detecting peri‐implant disease earlier and more accurately. Overall, adding HFUS and power Doppler imaging to current diagnostic methods can improve the evaluation of peri‐implant tissues. These noninvasive techniques can help clinicians see key structures more clearly, assess inflammation, and better understand the severity of the disease.

1. INTRODUCTION

The evolution of diagnostic criteria for peri‐implant diseases can be comprehended through a historical perspective based on clinical evidence and numerous consensus papers proceedings. ¹ , ² , ³ , ⁴ , ⁵ Over 30 years ago Mombelli et al. analyzed the microbiota associated with both successful and failing osseointegrated implants. They introduced the term “peri‐implantitis”, describing it as a site‐specific infection caused primarily by gram‐negative anaerobic rods. This condition shared several clinical features with chronic periodontitis. ⁶ Later between 1992 and 1993, the term “peri‐implantitis” gained importance in the periodontal scientific community, and in 1994, the proceedings of the First European Workshop on Periodontology by Lang and Karring, described peri‐implantitis as an inflammatory process that affected the tissues around an osseointegrated implant, resulting in loss of supporting bone ⁷ , ⁸ .

In 2008, the Sixth European Workshop on Periodontology, defined peri‐implant lesions as two distinct entities: (i) peri‐implant mucositis, characterized by inflammation confined to the soft tissues, and (ii) peri‐implantitis, which involves both soft tissue inflammation and loss of peri‐implant supporting bone. ² The consensus also established that peri‐implant mucositis could be clinically identified by swelling, redness of the soft tissue, and bleeding on probing (BOP), recognized as the most crucial diagnostic feature. On the other hand, peri‐implantitis was established to be diagnosed by the presence of suppuration (SUPP), deepened peri‐implant pockets and loss of supporting marginal bone. In this consensus, it was determined that probing was mandatory to monitor peri‐implant conditions and that baseline radiographs were needed at the time of the suprastructure placement to determine the initial marginal bone level. ² These standards were further formalized in the 2017 World Workshop, which established globally adopted diagnostic definitions grounded in both clinical and histological evidence ⁹ , ¹⁰ .

The histological foundation of peri‐implant diseased sites has been studied in the last 30 years. ¹¹ , ¹² , ¹³ , ¹⁴ , ¹⁵ , ¹⁶ , ¹⁷ Histological analyses of peri‐implant mucositis have revealed a defined inflammatory lesion lateral to the junctional epithelium mainly composed by an infiltrate of plasma cells, lymphocytes, and vascular structures. ¹⁸ , ¹⁹ Importantly, in mucositis lesions, the inflammatory infiltrate does not extend into the supracrestal connective tissue zone. In contrast, the primary histological feature of peri‐implantitis lesions is its extension beyond the junctional epithelium, characterized by higher density of plasma cells, macrophages, and neutrophils ⁹ , ²⁰ .

The current standardized methods for diagnosing peri‐implant health rely on clinical and radiographic examination. Clinical signs of inflammation, such as BOP, SUPP, and progressive peri‐implant bone loss have been recognized as key markers according to the latest consensus. ⁹ , ²¹ However, these tools can be limited by their binary nature and inability to quantify the severity/degree of disease. ²² Additionally, conventional two‐dimensional (2D) periapical radiographs only provide the marginal bone level at mesial and distal sites, often missing critical changes on the buccal and lingual aspects, which may compromise diagnostic accuracy. To overcome these limitations, advanced diagnostic approaches have been proposed incorporating biologic and imaging markers. ²³ , ²⁴ , ²⁵ , ²⁶ , ²⁷ High‐frequency ultrasound (HFUS) can provide invaluable information in a real‐time and noninvasive manner, regarding the peri‐implant anatomical dimensions, echo intensity, tissue perfusion, and strain elastography of the tissues at buccal, palatal, and interproximal peri‐implant sites. ²⁵ , ²⁶ , ²⁷ , ²⁸ Moreover, ultrasonography can provide a stronger understanding of the degree of the occurring inflammatory process at diseased sites, thereby offering valuable insights into the severity of the disease. ²⁶ , ²⁷ HFUS echo intensity has been shown to accurately diagnose peri‐implant diseases, visualize lesions as hypo‐echoic areas, quantify lesion size relative to peri‐implant tissue, and identify associated buccal bone dehiscence (BBD). ²⁶ As such, ultrasonography can complement existing histological knowledge and standard diagnostic tools for peri‐implant diseases.

In light of the possible limitations of conventional methods, this review critically explores the evolution of peri‐implant disease diagnostics. It highlights the histological basis of current definitions and presents the potential of next generation ultrasonographic imaging as a complementary tool for improving diagnostic accuracy.

2. METHODS

The present critical review was performed following the Scale for the Assessment of Narrative Review Articles (SANRA). ²⁹ An electronic search in Medline (PubMed) database was conducted to identify original and review articles focused on the histological foundations, clinical, radiographic and HFUS diagnostic approaches of peri‐implant diseases from January 1st to May 30th, 2025. Given the high global prevalence of both peri‐implant mucositis and peri‐implantitis in contemporary clinical practice, ¹⁰ the aim of this review is to present a comprehensive analysis of the histological foundations, current clinical diagnostic standards, and emerging ultrasonographic techniques for the diagnosis of peri‐implant diseases.

3. HISTOLOGICAL FOUNDATIONS

3.1. Histological differences between healthy, mucositis, and peri‐implantitis sites

The histological investigation of peri‐implant tissues can be traced back to the foundational studies by Brånemark and colleagues in the late 1960s, which introduced the concept of osseointegration and described implant‐tissue integration based on preclinical animal and cadaveric models. These early studies laid the groundwork for our current understanding of healthy peri‐implant anatomy ³⁰ .

The histopathological understanding of implant infections began in the early 1990s, primarily through clinical studies utilizing soft tissue biopsies. Sanz and coworkers ²⁰ reported that peri‐implant tissues from infected sites exhibited significantly more inflammatory cells in the epithelium and a higher proportion of plasma cells and mononuclear cells infiltrating the connective tissue, compared to healthy peri‐implant tissues.

The transition from peri‐implant health to mucositis has also been extensively studied in animal models with experimentally induced disease. The Lindhe group histologically confirmed the sequential pathophysiological changes caused by plaque accumulation around teeth and implants using a canine model. In this study, 3 weeks of plaque accumulation led to gingivitis around teeth and mucositis around implants, both exhibiting similar histological features, including (1) increased leukocyte migration through the junctional epithelium, and (2) formation of inflamed connective tissue (ICT) ³¹ .

Furthermore, the same group induced experimental peri‐implant bone breakdown using ligatures ³¹ revealing several key differences between periodontal and peri‐implant tissues: (1) clinically and radiographically, tissue destruction was more pronounced around implants compared to teeth; (2) the soft tissue lesion around implants was relatively larger; and (3) unlike periodontal lesions, peri‐implant lesions extended deep into the bone marrow. These distinctive histological features of peri‐implantitis suggest that spontaneous bone loss around implants may continue even after the removal of ligatures, potentially leading to a self‐sustaining destructive process. In the absence of appropriate therapy, including biofilm removal, spontaneous progression of experimentally induced peri‐implantitis has been observed in most of the implants over a 1‐year period ³² .

In a similar context, Schou et al. ³³ suggested that the absence of periodontal ligament in peri‐implant tissues may increase the susceptibility to disease, based on findings from a primate model. While healthy implants and teeth are similarly surrounded by para‐ or ortho‐keratinized stratified squamous epithelium, peri‐implantitis sites demonstrated (1) apical migration of the pocket epithelium, and (2) extensive ulceration within the epithelial layer (Figure 1).

FIGURE 1.

Histologic characteristics of healthy (A, D), mucositis (B, E), and peri‐implantitis (C, F) lesions in a ligature‐induced peri‐implantitis model in the dog maxilla. Panels (A–C) represent low‐magnification bucco‐lingual sections, and the corresponding regions shown at higher magnification in panels (D–F) are indicated by yellow dashed boxes. Panels (D–F) highlight detailed features of healthy connective tissue, inflamed and edematous soft tissue, and peri‐implant bone resorption. All sections were stained with Goldner's trichrome and examined using dedicated visualization software (CaseViewer; 3DHISTECH, Budapest, Hungary). Images originate from the same experimental model as previously published ⁸⁵ (Courtesy of Dr. Ui‐Won Jung).

3.2. Cellular components of ICT

As previously demonstrated in experimental peri‐implantitis models by Lindhe and colleagues, ²⁹ the cellular composition of the ICT in peri‐implantitis shows both quantitative and qualitative differences compared to periodontitis. These histopathologic differences have been documented in various pre‐clinical animal studies, which reported that ICT in peri‐implantitis often extends apically beyond the pocket epithelium, containing a dense accumulation of plasma cells ¹¹ .

The structural differences in ICT between tooth and implant sites can largely be attributed to the direction and composition of collagen fibers within the peri‐implant connective tissue. At tooth sites, the ICT is typically separated from the alveolar crest by a layer of supracrestal collagen fibers, creating a biologic barrier that restricts the downward spread of inflammation. In contrast, the ICT at implant sites is more likely to extend directly to the alveolar crest and even penetrate the bone marrow. ³¹ This anatomical difference may contribute to the greater extent and aggressiveness of peri‐implantitis compared to periodontitis.

Further supporting this distinction, Carcuac et al. reported that peri‐implantitis lesions are not only larger but also extend closer to the crestal bone and contain greater numbers of osteoclasts compared to periodontitis lesions. ³⁴ These findings underscore the more destructive nature of peri‐implant inflammation.

This observation has been corroborated by human biopsy studies. In a comparative analysis of 40 tissue biopsies from periodontitis‐affected teeth and 40 from peri‐implantitis‐affected implants, the ICT area around implants was found to be more than twice as large (3.48 mm² vs. 1.49 mm²) as that around teeth. ³⁵ Furthermore, peri‐implantitis lesions demonstrated significantly higher densities and proportions of CD138⁺ plasma cells, CD68⁺ macrophages, and myeloperoxidase (MPO) ‐positive neutrophils, compared to periodontitis lesions. Vascular density was also significantly increased in peri‐implant tissues, reflecting a more inflamed and hyper‐vascular environment.

3.3. Histologic probe penetration at healthy and diseased peri‐implant sites

The relationship between clinical probing depth and underlying histologic structures has been systematically investigated in animal models to assess its diagnostic accuracy for peri‐implant disease. In a canine study by Lang et al., probe penetration was measured in relation to key histologic landmarks, including the junctional epithelium and alveolar bone crest. ³⁶ In healthy peri‐implant tissues, the mean histologic probing depth (HPD) was 1.75 mm, with a mean distance from the probe tip to the alveolar bone (DBP) of 0.59 mm. Similarly, in mucositis lesions, HPD was to 1.62 mm and DBP to 0.80 mm. However, in peri‐implantitis sites, the probe tip reached much deeper, with a mean HPD of 3.80 mm and DBP of 0.25 mm, indicating that the probe tip often surpassed the connective tissue level in advanced inflammatory lesions.

These findings were corroborated by Schou et al., who assessed probe penetration in a primate model (Macaca fascicularis). ³⁷ In health, the probe tip typically remained 0.5–1.5 mm from the alveolar bone. However, in conditions of mild mucositis or gingivitis, the probe was significantly closer to the bone around implants than around teeth (p = 0.034). This difference became more pronounced in peri‐implantitis, where the distance from the probe tip to bone around implants was frequently less than 0.5 mm (p < 0.0001), compared to 0.5–1.5 mm around natural teeth with periodontitis.

Histologic sections from these studies also revealed that in peri‐implantitis, the epithelial lining underwent apical migration and ulceration, allowing the probe to penetrate directly into the connective tissue. ³⁷ This loss of structural integrity and resistance within the supracrestal connective tissue likely accounts for the deeper probe penetration observed at diseased sites.

4. CLINICAL AND RADIOGRAPHIC DIAGNOSTIC PARAMETERS AND CURRENT CHALLENGES

Peri‐implant conditions were histologically and clinically defined by the 2017 World Workshop. Clinically, peri‐implant health was characterized by the absence of inflammatory visual signs such as BOP, erythema, swelling, suppuration, and no increase in probing depth (PD) values compared to previous exams. ⁹ Radiographically, peri‐implant health was defined as the absence of bone loss beyond initial remodeling. Notably, peri‐implant health can be diagnosed even in implants with reduced bone support, provided there are no clinical signs of inflammation. Peri‐implant mucositis was defined as presence of bleeding and/or suppuration on gentle probing, with or without increase in PD scores, and absence of crestal bone loss beyond initial bone remodeling. Peri‐implantitis was defined as presence of bleeding and/or suppuration on gentle probing, with increase in PD scores and presence of marginal bone loss ≥ 2 mm, beyond initial bone remodeling. In cases with no baseline, PD and/or radiograph, the definition for peri‐implantitis was presence of bleeding and/or suppuration on gentle probing, PD scores ≥ to 6 mm and marginal bone loss of ≥ 3 mm. According to mentioned consensus, the best clinical method to detect the presence or absence of peri‐implant inflammation included visual inspection and peri‐implant probing. Moreover, 2D periapical radiographs were also determined to be the best imaging method to assess progressive marginal bone loss (MBL) around dental implants ⁹ .

The latest consensus definitions have brought numerous advantages, particularly in standardizing peri‐implant disease definitions and permitting more accurate comparisons of prevalence and incidence outcomes across studies worldwide. In the most recent American Academy of Periodontology and Academy of Osseointegration (AAP/AO) consensus on the prevalence, incidence, and risk indicators for peri‐implant diseases, Galarraga‐Vinueza et al. emphasized the importance of this standardization. ¹⁰ Despite these advancements, the current diagnostic criteria still face limitations in both specificity and sensitivity, ³⁸ particularly in detecting early or subclinical inflammation, and in assessing disease severity, and identifying progressive bone loss. ²² The criteria may also fail to identify subclinical signs of inflammation, which can be critical for early intervention.

The application of conventional periodontal probing methods to assess peri‐implant health status has been widely discussed in the last decades. ³⁹ In an aforementioned animal study, Lang and colleagues evaluated probe penetration at both healthy and peri‐implantitis sites and concluded that probing is a sensitive and reliable tool for monitoring peri‐implant conditions. ⁴⁵ This conclusion was based on the observation that the “soft tissue seal,” or the attached mucosa, effectively resisted probe penetration in healthy and mildly inflamed tissues but failed to provide resistance in peri‐implantitis lesions.

While deep PD values have been associated with peri‐implantitis prevalence in clinical studies, ³⁶ , ⁴⁰ , ⁴¹ , ⁴² shallow PD values have also been reported at peri‐implantitis cases. ⁴³ It is important to consider that clinical probing might not be accurate in all implant systems. Multiple factors such as the variability in probing force can be crucial in discriminating health versus disease, and operators should be trained and calibrated for this assessment. Some studies have also showed the limitations of peri‐implant probing, establishing that peri‐implant PD values can be influenced by several aspects, including but not limited to, operator experience and the diverse implant prosthetic components involving the crown angulation, abutment angulation, type of crown retention, and the sub‐crestal implant positioning. ⁴⁴ , ⁴⁵ , ⁴⁶ Furthermore, an important limitation in diagnosing peri‐implant diseases is the difficulty in standardizing probing sites during longitudinal clinical assessments, which challenges a consistent and fair comparison of results over time. ⁴⁶ This point is particularly significant given that the latest consensus emphasizes the importance of comparing PD values with those from previous examinations. ⁹ In this context, the latest 2017 World Workshop consensus established that it is not possible to determine exact PD values compatible with peri‐implant health, and that more important are the clinical signs of inflammation when diagnosing peri‐implant diseases.

Clinical probing also comes with the assessment of BOP, which is widely regarded as the most important clinical sign of inflammation in peri‐implant diseases diagnostics. ⁹ According to the Seventh European Workshop on Periodontology, BOP assessed with gentle probing force (< 0.25 N) is the fundamental parameter for diagnosing both mucositis and peri‐implantitis. ⁴⁷ Excessive force can cause trauma to peri‐implant soft tissue and induce BOP even at healthy implant sites, leading to false positive results. At diseased sites, it has been assumed that and inflammatory process can increase the risk of BOP by rupturing blood vessels within the peri‐implant mucosa during probing. ²² Nevertheless, as BOP can be determined by visual examination and is a dichotomous test, its sensitivity and specificity in diagnosing peri‐implant diseases has been evaluated in several studies. ²² , ³⁸ In a systematic review evaluating the predictive value of BOP, Hashim and collaborators established that high false positive rates of BOP were present when diagnosing peri‐implant clinical inflammation. ²¹ Several studies have reported a mismatch between BOP positivity and actual peri‐implant tissue breakdown, raising concerns about the overreliance on BOP as a standalone indicator of disease activity. ⁵¹ Moreover, Romandini et al. showed in a prospective cohort study that the common presence of BOP, raises doubts whether BOP at only one site can truly reflect an inflammatory disease process. This study assessed the accuracy of clinical parameters in predicting peri‐implant bone loss and concluded that BOP should be considered highly sensitive but extremely low in terms of specificity, as it leads to over‐diagnosis of mucositis ³⁸ .

SUPP is also considered a diagnostic sign of peri‐implant inflammation. ⁴⁸ Although its presence indicates inflammation and infection; its absence does not rule out disease. Moreover, SUPP cannot distinguish between peri‐implant mucositis and peri‐implantitis, as it may be present in both conditions. ⁹ Studies have determined that the inflammatory process at diseased peri‐implant sites can cause suppuration upon gentle probing. ¹⁵ , ³⁸ Fransson et al. described in a clinical study that SUPP was present in 19% of the cases diagnosed with peri‐implantitis, and only in 5% of the implants with stable marginal bone levels. ¹⁵ Similarly, Monje et al. found that the presence and severity of SUPP were correlated with marginal bone loss, PD, and defect morphology. ⁴⁸ Romandini et al. also noted that SUPP had high specificity but low sensitivity for predicting bone loss, making it useful in confirming but not in early detection ³⁸ .

Given these limitations, it may be reasonable to assume that the binary nature of BOP and SUPP prevents the accurate determination of the degree and severity of the destructive inflammatory process associated with peri‐implant diseases. Consequently, early pathological changes may remain undetected, narrowing the window for timely intervention ²⁴ , ²⁶ .

The clinical distinction between peri‐implant mucositis and peri‐implantitis relies on the presence or absence of bone loss beyond the initial crestal bone remodeling, combined with clinical signs of inflammation. ⁹ , ¹⁰ One of the key diagnostic challenges lies in discriminating bone loss due to infection from physiologic bone remodeling. It has been established that radiographic progressive MBL is a crucial feature in diagnosing peri‐implantitis, ²¹ highlighting the importance of baseline radiographic documentation. ⁴⁶ Currently, 2D periapical radiographs are the standard method for assessing MBL, primarily at interproximal sites. ⁴⁹ However, this approach has limitations: it does not visualize buccal or lingual/palatal bone levels, which may lead to underestimation of bone loss. ²⁶ Moreover, radiographic consistency over time can be compromised by variations in patient anatomy, image angulation, and operator technique. ⁴⁹ While 2D radiography is reliable for identifying significant bone changes over long‐term follow‐up, it lacks the sensitivity to detect early or subtle bone loss. ⁵⁰ As a result, it may fail to capture the critical transition from mucositis to peri‐implantitis ⁴² .

Taken together, these limitations underscore the need for adjunctive diagnostic technologies that can provide objective, quantitative, and site‐specific data. As the following section will discuss, HFUS has emerged as a promising noninvasive tool to overcome many of these diagnostic challenges and support early and accurate detection of peri‐implant diseases.

5. HIGH‐FREQUENCY ULTRASOUND

Diagnostic imaging technologies for peri‐implant diseases have gained increasing attention in recent years, reflecting their expanding role in enhancing diagnostic accuracy and supporting treatment planning in implant dentistry. ⁵¹ , ⁵² , ⁵³ , ⁵⁴ Emerging imaging techniques in dentistry such as ultrasonography may offer a promising diagnostic potential due to its real time, noninvasive nature. ²⁸ Ultrasonography has been used for more than five decades in diverse medical fields to visualize various anatomical structures and as a diagnostic tool for pathological conditions. ⁵⁵ This imaging technology employs high‐frequency acoustic waves (greater 20 000 Hz), which are transmitted through a transducer into human body tissues. ⁵⁵ The transducer is also able to detect the echoes of the waves encountering boundaries between tissues. These echoes are then converted into images and can be visualized chair‐side in real time, providing a noninvasive method of visualization and diagnosis ²⁴ .

In dentistry, preliminary experiments using high‐resolution, intensity‐modulated ultrasonography, demonstrated that at a frequency of 15 MHz, certain internal structures of vital teeth could be visualized, revealing a relationship between imaging time, tooth vitality, and ultrasonic visibility. Additionally, reports from the early 1970s documented the use of transducers to observe and measure periodontal tissue thickness. ⁵⁶ , ⁵⁷ , ⁵⁸ In the context implant dentistry, HFUS has emerged over the past ten years as a valuable imaging tool for visualizing peri‐implant hard and soft tissue morphology, monitoring healing processes, assessing tissue perfusion and elasticity, evaluating the outcomes of soft tissue augmentation procedures and as diagnostic tool for peri‐implant diseases. ²⁵ , ²⁶ , ²⁷ , ²⁸ , ⁵⁶ , ⁵⁹ , ⁶⁰ HFUS at peri‐implant sites allows the assessment of both peri‐implant soft and hard tissue dimensions, at the interproximal, buccal and lingual aspects, and therefore it is reasonable considering the use of HFUS as a valuable complement to the existing diagnostic tools. ²⁴ Table 1 summarizes and suggest the application of HFUS for evaluating peri‐implant anatomy and differentiating implant site conditions.

TABLE 1.

HFUS modalities to assess peri‐implant anatomical landmarks and discriminate among healthy, diseased, and PSTD implant sites

HFUS modality	Outcome measures	Description	Healthy implant sites	Diseased implant sites	PSTD (healthy site) *
B‐mode	MT	Mucosal thickness, measured as the horizontal distance from the buccal bone/implant surface to the most external epithelial layer (hyperechoic border) of the soft tissue.	—	Higher due to inflammation	Reduced 61 , 62
	STH	Supracrestal tissue height, assessed as the apico‐coronal distance from the soft tissue margin to the buccal bone	—	—	Reduced 61 , 62
	KMW	Keratinized mucosa width assessed along the surface of the soft tissue, from the mucosal margin to the mucogingival junction, observed as a hyperechoic line.	≥2 mm (ideally)	—	Reduced 61
	BB BBD	Buccal bone, observed as a hyperechoic mineralized tissue 54 , 61 . Buccal bone dehiscence, measured from the implant platform to the buccal bone crest	No progressive MBL, BB level above or at implant platform (ideally)	Mucositis: BB loss ≤ 3 mm without baseline or ≤ 2 mm with baseline Peri‐implantitis: BB loss ≥3 mm without baseline or ≥ 2 mm with baseline	Below implant platform, with presence of BBD
	SACT	Supracrestal adherent connective tissue height. Clinically interpreted as adherent portion of the connective tissue where the probe is not able to penetrate at healthy sites 36 .	Apical limit: above the bone crest	Mucositis: Apical limit could be above the bone crest, at implant platform or below the implant platform Peri‐implantitis: Apical limit penetrates below implant platform.	Not affected by the buccal bone level
	Echo intensity (EI)	Quantifies the average brightness of pixels within a ROI in an ultrasound image, corresponding to the amount of ultrasound waves that are reflected to the transducer. ‐Higher EI (brighter) indicates more fibrous, collagen rich, or calcified tissue. ‐Lower EI (darker) indicates softer, fluid‐filled or less organized tissue.	No hypo‐echoic supracrestal area (HSA) resembling the peri‐implant lesion 26	Presence of HSA	No HSA
Doppler	Color Doppler velocity (CDV)	‐Intensity and distribution of vascular flow within the vessels, by calculating the density of color pixels. ‐Weighted by the velocity of moving blood within the ROI. ‐Provides insights into both blood flow direction and relative velocity 65 .	Lower CDV values compared to diseased sites 27 .	Higher CDV values	N.A.
	Power Doppler imaging (PDI)	‐Overall strength of Doppler signal: calculates density of color pixels weighted by signal power, regardless of flow direction 65 . ‐Provides insights of vascular flow quantity, which correlates with the concentration of moving red blood cells that scatter the transmitted acoustic wave. ‐More sensitive for low‐velocity and lower‐volume blood flow.	Lower PDI values compared to diseased sites 27 .	Higher PDI values	N.A.
Elastography	Strain ratio (SR) 25	‐Strain ratio is measured as the tissue's resistance to deformation under an applied force. ‐Mainly depends on the intrinsic characteristics of the tissue being examined. ‐Strain elastography involves compressing the target tissue using an ultrasound transducer. ‐SR can be influenced by the peri‐implant KMW and MT 25 .	Coronal soft tissue may present higher stiffness	Coronal soft tissue may present more elasticity	Coronal soft tissue may have higher stiffness if adequate soft tissue phenotype is present 25

Abbreviation: ROI, region of interest.

^*PSTD with mid‐hard and soft tissue deficiency in the absence of BOP or SUPP.

5.1. HFUS for evaluating peri‐implant anatomical landmarks

HFUS has recently been validated as an imaging diagnostic tool in implant dentistry through both pre‐clinical and clinical studies. ²⁷ , ²⁸ Lately, multiple studies have demonstrated that HFUS application at teeth and implant sites can be a valuable tool to characterize both periodontal and peri‐implant anatomical landmarks. ⁶¹ , ⁶² Among the peri‐implant structures, HFUS allows an accurate visualization and measurement of the peri‐implant phenotype components such as the soft tissue or mucosal thickness (MT), keratinized mucosa (KM) width, ⁶³ the supracrestal tissue height (STH), and the buccal bone (BB) level and thickness ⁶¹ , ⁶⁴ (Figure 2). In a cross‐sectional study assessing the prevalence of peri‐implant soft tissue dehiscences (PSTDs), Tavelli et al. advocated that ultrasonography should be considered the technology of choice for determining the peri‐implant MT. Moreover, this study highlighted the advantage of HFUS when evaluating BB dehiscences and thickness without the exposure to radiation required by CBCT scans. ⁶¹ Prosthetic components, such as the abutment and suprastructures, can also be visualized and delimited in peri‐implant HFUS (brightness) B‐mode scans. ⁶¹ , ⁶⁵ , ⁶⁶ Furthermore, the ultrasound scan can be performed simultaneously with implant probing to determine the extent of probe penetration within the soft tissue compartment. ³⁶ , ³⁷ , ⁶⁷ This might allow for the identification of a ultrasonographic supracrestal adherent connective tissue (SACT) that could be clinically interpreted as an adherent portion of the connective tissue where the probe is not able to penetrate at healthy sites ³⁶ (Figure 3). Identifying the starting point and extension of the SACT, which can resemble the supracrestal connective tissue “barrier”, and its ability to limit the entry of pathological agents may be a crucial factor in determining whether an implant is more susceptible to developing peri‐implant diseases over time. Probe penetration and identification of the referenced anatomical landmark have previously been documented in histological studies using animal models, where the probe tip was identified to be very close to the apical extension of the junctional epithelium at healthy implant sites. ³⁶ , ⁶⁷ Ultrasonography might provide a distinct advantage in evaluating the SACT, that can correspond to the level of attachment at implant sites previously described by Lang et al. ³⁶ . This technology can enable real‐time, noninvasive visualization of this critical anatomical structure at human peri‐implant sites, offering a possible method for evaluating the SACT.

FIGURE 2.

(A) Periapical radiograph; (B, C) clinical probing with and without the implant‐supported restoration; and (D) high‐frequency ultrasound (HFUS) scan of a healthy anterior implant. The HFUS image highlights key peri‐implant anatomical landmarks on the buccal aspect, including the soft tissue margin, supracrestal tissue height (STH), mucosal thickness (MT), keratinized mucosa (KM) width, and buccal bone (BB). Prosthetic components such as the abutment (Ab) and crown (Cr) are also visible. (E) Frontal clinical view and (F) HFUS scan of a healthy implant site, with simultaneous clinical probing. The scan illustrates the periodontal probe penetrating the peri‐implant soft tissue compartment (in yellow), showing the probing depth (PD) (in red). Anatomical features include the supracrestal adherent connective tissue (SACT) dimension (in green), which limits probe penetration, and the bone crest (BC). (G) Histological section of an implant site from a canine model (adapted from Abrahamsson and Soldini), ⁶⁷ shown here exclusively to illustrate the analogy between histological structures andkey anatomical landmarks visualized with ultrasonography. The peri‐implant margin (PM), apical termination of the junctional epithelium (aJE), and the probe tip (TIP) are highlighted to demonstrate how these structures correspond to those identified in HFUS imaging. Although readers should keep in mind that this histologic reference is from a canine model and not from the patient shown, it helps contextualize the ultrasound findings and illustrate probe penetration, which cannot be ethically confirmed through human histology. (H) Occlusal clinical photograph of the same implant site and (I) corresponding HFUS occlusal scan, demonstrating visualization of the implant platform and mucosal thickness from a top‐down perspective.

FIGURE 3.

Diagram illustrating clinical photographs of implants in the anterior region across three different diagnostic scenarios: healthy (top panel), peri‐implant mucositis (middle panel), and peri‐implantitis (bottom panel). Corresponding high‐frequency ultrasound (HFUS) scans display the buccal aspect in both B‐mode and color Doppler velocity. Differences between healthy and diseased sites are evident in the HFUS images, primarily through the presence or absence of a hypo‐echoic supracrestal area (HSA) in the marginal region. Notably, the peri‐implantitis case shows a larger HSA extending apically towards the bone. Color Doppler imaging further reveals increased tissue perfusion in diseased sites compared to the healthy implant, reflecting the heightened inflammatory activity. BB, buccal bone.

It should be also mentioned that HFUS has also been employed to assess the outcomes of peri‐implant soft and hard tissue augmentation with different autogenous grafts and biomaterials. ⁶³ , ⁶⁸ , ⁶⁹ In fact, there is robust evidence supporting that HFUS scans performed before and after peri‐implant soft tissue augmentation can accurately quantify the gain in MT and changes in STH and KM, occurring as a result of the surgical intervention. ⁶² , ⁶³ , ⁶⁵ This noninvasive approach also allows for real‐time observation and delineation of the graft during early healing. ⁶⁹ In a randomized clinical trial, Tavelli and collaborators analyzed the efficacy of coronally advanced flap (CAF) with connective tissue graft (CTG) versus tunneling technique with CTG in covering isolated PSTDs. One of the secondary outcomes of this study was to assess the horizontal MT changes with ultrasonography. ⁶² The MT changes following augmentation could be clearly observed in the HFUS scans and were measured at three points within a region of interest (ROI). The study demonstrated that CAF combined with CTG resulted in superior PSTD coverage, along with greater gains in KM width and MT, compared to the tunneling technique with CTG. This further validates HFUS as a precise tool to monitor surgical outcomes.

5.2. HFUS gray‐scale texture analysis for peri‐implant diagnosis

HFUS can provide an accurate, noninvasive method to visualize the echo intensity (EI) and differentiate between the hypo and hyper‐echoic regions at the peri‐implant mucosa. EI quantifies the average pixels brightness within a ROI in an ultrasound image, reflecting the amount of ultrasound waves that are reflected to the transducer. Higher EI (brighter) usually indicates more fibrous, collagen rich, or calcified tissue, while lower EI (darker) often indicates softer, fluid‐filled or less organized tissue. ²⁶ In other medical fields, EI on ultrasonography scans has been used a surrogate marker for skeletal muscle quality. For instance, a computer‐aided gray scale analysis by Watanabe et al., described that increased EI was correlated with adipose tissue and collagen concentration. ⁷⁰ On the other hand, other ultrasonographic studies evaluating infectious processes have demonstrated that infected regions with abscesses and fluid‐filled areas can be observed as hypoattenuating and hypoechoic lesions on HFUS scans ⁷¹ , ⁷² .

In the context of peri‐implant diagnosis, HFUS‐derived EI has been reported as a reliable method to detect diseased sites. It may enable the visualization of peri‐implant lesions as hypoechoic zones adjacent to the implant surface, quantifies the proportion of the lesion within the surrounding tissue, and identifies BBD associated with the lesion. In a prospective case‐control study by Galárraga‐Vinueza et al., HFUS EI was applied to characterize peri‐implant tissues at healthy and diseased sites and identify consistent ultrasonographic markers of disease. ²⁶ EI characterization of peri‐implant tissues revealed a significant difference between healthy and diseased sites. HFUS EI and the presence of a hypo‐echoic supracrestal area (HSA) were valid and statistically significant diagnostic markers to discriminate peri‐implant health status. The HSA was a constant diagnostic feature at peri‐implantitis sites, which can be suggested to resemble the peri‐implant lesion, as depicted in Figure 3.

The hypo‐echoic nature of the mentioned area may be attributed to the infiltrated connective tissue and presence of fluids such as pus, which clinically may me present as suppuration following peri‐implant palpation or probing. The lower density of the peri‐implant lesion or granulation tissue, compared to the surrounding “healthy” tissue, leads to reduced reflection of ultrasound waves, resulting in a “darker” hypoechoic area on the scan. ²⁶ Considering that HFUS may detect changes in the peri‐implant mucosa dimensions and inner structure, together with marginal bone stability following grafting procedures, it is reasonable to assume that this imaging tool may also have the potential to monitor disease resolution following peri‐implant disease therapy as portrayed in Figure 4. The presence or absence of a HSA, validated as a consistent diagnostic marker, further supports HFUS could be a practical tool for the early detection and monitoring of peri‐implantitis. Moreover, another study highlighting the potential of HFUS as a diagnostic and interventional tool in fixed prosthodontics has also reported that HFUS gray‐scale texture analysis can be valuable for evaluating the correlation between a specific prosthetic emergence profile and peri‐implantitis prevalence ⁶⁶ .

FIGURE 4.

Schematic diagram of a peri‐implant mucositis case involving an implant in function for over 10 years. (A) Periapical radiograph and (B) frontal clinical photograph illustrating the initial presentation. (C) High‐frequency ultrasound (HFUS) scan of the buccal aspect shows the soft tissue (ST) and a hypo‐echoic supracrestal area (HSA), outlined in yellow, indicative of a peri‐implant lesion. Non‐surgical therapy was initially performed, followed by a connective tissue graft (CTG) to enhance the peri‐implant soft tissue phenotype. (D) Occlusal and frontal clinical views following non‐surgical therapy, and (E) corresponding views after soft tissue grafting and delivery of a new prosthesis after 6 months. (F) HFUS scans before and 6 months after therapy highlight key improvements: the HSA is no longer detectable, mucosal thickness has increased significantly due to grafting, and color Doppler imaging reveals reduced tissue perfusion in the marginal region, indicating resolution of inflammation. BB, buccal bone; ST, soft tissue; Ab, abutment; Cr, crown.

5.3. HFUS in the assessment of peri‐implant bone defects

HFUS has also emerged as a promising imaging modality in visualizing peri‐implant bone defects. ²⁶ , ⁵² , ⁵⁴ Bykhovsky et al. evaluated the accuracy of HFUS in an in vitro study using native human cadavers presenting peri‐implant fenestrations, dehiscences, and three‐wall bone defects. This study demonstrated that HFUS measurements had a strong correlation with the ones obtained with CBCT. Among the evaluated peri‐implant defects BBD, bone fenestrations, and three‐wall defects, HFUS was found to have the highest accuracy. ⁵² Additionally, Siqueira et al. conducted a prospective clinical study comparing CBCT with HFUS concluding that both imaging methods were very accurate to evaluate crestal bone thickness and the peri‐implant facial bone level. ⁶² As follows, HFUS can serve to complement CBCT when assessing buccal and palatal sites, particularly when artifacts can compromise diagnostic accuracy, and at routine follow‐up visits when exposing patients to radiations can be avoided. Interestingly, a recent clinical study employing HFUS to characterize peri‐implant anatomy reporter that the mean BBD was 4.30 ± 1.35 mm and 0.25 ± 0.49 mm at diseased and healthy sites, respectively. ²⁶ All in all, HFUS can be a potential complement to assess the marginal bone levels at peri‐implant sites, especially at mid‐buccal and mid‐lingual/palatal aspects which are not considered by the current standardized diagnostic criteria. Moreover, HFUS can also assist in identifying the configuration and extension of peri‐implant bone defects at peri‐implantitis sites, which is an essential factor in both diagnosis and planning the surgical strategy (regenerative, resective, or combined therapy), given that the defect morphology significantly influences the prognosis of peri‐implantitis surgical therapy ⁷³ (Figure 5).

FIGURE 5.

Illustration demonstrating the accuracy of high‐frequency ultrasound (HFUS) in detecting peri‐implant bone defects from both buccal and occlusal perspectives. (A) Intraoperative photograph of a peri‐implantitis case showing a combined bone defect. (B) Preoperative HFUS scan of the same site accurately identifies the bone crest level (BC), an infrabony defect compartment (highlighted in red), a bone fragment (in yellow), and the crown margin level. (C) Periapical radiograph of another advanced peri‐implantitis case showing significant interproximal bone loss, and (D) the corresponding frontal clinical view. (E) HFUS scan of the same case reveals a large supracrestal hypoechoic area (HSA) extending along the entire buccal aspect of the implant, representing the peri‐implant lesion. (F) Occlusal view and (G) corresponding HFUS scan with Power Doppler imaging illustrate elevated tissue perfusion, indicating an active inflammatory process extending from the buccal to palatal aspects. (H) Intraoperative occlusal photograph confirms the lesion's shape and extent, closely mirroring the features seen on the HFUS scan. BB, buccal bone; IF, infrabony defect; BF, bone fragment; Imp, implant; Ab, abutment; Cr, crown.

5.4. Doppler ultrasonography

Tissue perfusion is a physiological process that maintains tissue health by delivering oxygen and nutrients while facilitating the removal of metabolic waste. ⁶⁵ , ⁷⁴ , ⁷⁵ Doppler HFUS is a noninvasive imaging method to assess the blood flow and/or tissue perfusion through the blood vessels. It mainly works by bouncing high‐frequency sound waves emitted by red blood cells that are circulating in the blood stream of a specific tissue. Color Doppler velocity (CDV) and power Doppler imaging (PDI) are ultrasound imaging modalities that superimpose color‐coded information onto B‐mode images to visualize blood flow within tissues. CDV assesses the intensity and distribution of vascular flow within the vessels, by calculating the density of color pixels, weighted by the velocity of moving blood within the region of interest. CDV provides insights into both blood flow direction and relative velocity. As the moving red blood cells scatter the ultrasound waves, they provoke frequency changes in the returning signal, where blood flowing towards the transducer is typically displayed in red, while flow moving outwards is usually indicated in blue. PDI can reflect the overall strength of the Doppler signal by calculating the density of color pixels weighted by signal power, regardless of flow direction. PDI can provide insights about vascular flow quantity, which correlates with the concentration of moving red blood cells that scatter the transmitted acoustic wave. The intensity is typically displayed in red, reflecting the volume of blood flowing within the vessels. PDI is more sensitive for low‐velocity and lower‐volume blood flow ⁶⁵ , ⁷⁴ .

Tissue perfusion is crucial for effective wound healing and graft vascularization. Cross‐sectional studies and randomized clinical trials have employed Doppler HFUS at implant sites for assessing tissue perfusion, soft tissue graft (re)vascularization, and detecting inflammation or infection. ²⁷ , ⁶⁵ In particular, Doppler HFUS provided valuable insights into the revascularization process around palatal donor sites and augmented teeth and dental implants following soft tissue grafting. ²⁸ , ⁶⁵ A clinical trial utilized Doppler HFUS to characterize tissue perfusion at different time points following CTG, in combination with either CAF or tunneling technique, at implant sites. This technology allowed to monitor the changes occurring at grafted sites over 1 year, revealing that CAF treated sites presented a higher blood flow at interproximal regions at early stages when compared to tunneled treated cases. ⁶⁵ Notably, early tissue perfusion patterns observed via Doppler HFUS were predictive of final PSTD coverage and volumetric soft tissue gain at the 1‐year follow‐up. These findings underscore the potential of this imaging modality in predicting the course of healing and success of surgical augmentative interventions.

5.4.1. Capability of Doppler ultrasonography to diagnose peri‐implant diseases

Abnormal tissue perfusion has been associated with pathological and malignant conditions. ⁷⁴ , ⁷⁶ Particularly, increased and atypical vascularization patterns have been observed at infected and neoplastic tissues. Doppler ultrasonography has shown its potential for clinical diagnosis in several medical fields. ⁷⁴ , ⁷⁷ , ⁷⁸ Clinical evidence has demonstrated that power Doppler ultrasonography can detect an increased vascularization and hyperemia in the wall of superficial soft tissues abscesses. ⁷⁴ Other studies have also described the accuracy of color and power HFUS to identify cellulitis, fasciitis, osteomyelitis, and abscesses. ⁷⁷ Moreover, Doppler HFUS has been considered as a valuable tool to diagnose inflammatory versus non‐inflammatory diseases, where the inflamed areas presented significantly altered and higher blood flow dynamics, higher vascular density, and disorganized microcirculation that serve as markers of pathology. ⁷⁵ Within periodontal research, Woo et al. used an animal model to evaluate the vascular response to mechanical compression during induced gingival inflammation and found that Doppler HFUS was a useful tool for assessing inflammation a tooth sites. ⁷⁹ In this context, peri‐implant diseases defined as pathological inflammatory conditions, ²¹ may be more accurately diagnosed through Doppler ultrasonography, which complements existing diagnostic criteria by enabling real‐time assessment of tissue perfusion, infection, and inflammatory burden in the peri‐implant soft tissue compartment ²⁷ , ⁷⁹ (Figure 3).

Barootchi et al. conducted a pioneer discordant‐matched case‐control cross‐sectional study to investigate whether tissue perfusion as assessed by color flow/power Doppler ultrasonography was associated with the clinical diagnosis of peri‐implant diseases. ²⁷ This study included a total of 42 implants in 21 patients and assessed through linear mixed effect models the relationship between peri‐implant clinical diagnoses and CDV/PDI. This study demonstrated a significant increase in ultrasonographic blood flow with the severity of peri‐implant disease, presenting the lower CDV and PDI values in healthy implants, higher values in mucositis sites, and the highest in peri‐implantitis cases. The authors further demonstrated that CDV and PDI values significantly correlated with clinical diagnoses, underscoring the need for more advanced imaging tools capable of detecting subclinical inflammation and guiding early therapeutic decisions. ²⁷ As follows, this was the first clinical study that confirmed the diagnostic capacity of Doppler HFUS in diagnosing peri‐implant health status and as a valuable tool to assess the degree of inflammation at implant sites.

5.5. Elastography

Elastography is an imaging modality that evaluates soft tissue elasticity, defined as the tissue's resistance to deformation under applied force. This property mainly depends on the intrinsic characteristics of the tissue being examined. ²⁵ Strain elastography involves compressing the target tissue using an ultrasound transducer. The resulting tissue deformation can be analyzed by a specialized software, which generates a color‐coded map, known as an elastogram, that can be overlaid on the B‐mode image to visualize varying levels of tissue elasticity.

Studies in the medical field have shown that elastography might have the potential to complement the diagnosis of evolving soft tissue infections and provide valuable information in the progression of skin infections from induration to fluctuant abscesses. ⁷⁸ Moreover, ultrasound elastography may help to differentiate between malignant and benign tumors, where malignant lesions generally exhibit a higher stiffness. ⁸⁰ , ⁸¹ Riegler et al. demonstrated in a clinical study that ultrasound elastography can be used to track the stiffness changes in a range of tumor models, and that stiffness was highly correlated with collagen, fibroblast and blood vessel density. ⁸¹ These findings are particularly relevant to implant dentistry, as the primary components of the peri‐implant soft tissue (i.e., collagen fibers, fibroblasts, extracellular matrix, and blood vessels) are critical to maintaining structural integrity and implant stability. ⁸² Given that strain elastography can indirectly assess these components by measuring tissue elasticity, it offers promising potential as a noninvasive tool for evaluating peri‐implant tissue quality. Its application may facilitate early detection of pathological soft tissue changes and support more precise, longitudinal monitoring of peri‐implant health and stability.

In a recent clinical study, Tavelli and Barootchi were the first to clinically evaluate strain elastography at both healthy tooth and implant sites, as well as to assess stiffness changes following peri‐implant soft tissue augmentation. ²⁵ Their findings demonstrated that ultrasound strain elastography can be used to estimate and quantify tissue elasticity of periodontal and peri‐implant tissues and track changes within their structure following soft tissue grafting. The main factors that were found to influence tissue elasticity were KM width and MT, leading the authors to speculate that HFUS elastography should be interpreted as a dynamic and synergistic assessment of periodontal and peri‐implant soft tissue phenotype. ²⁵ It is conceivable to infer that this HFUS modality can also be applied to assess tissue elasticity at peri‐implant diseased sites, considering it has previously contributed as a valuable tool to determine evolution of soft tissue infections. ⁸¹ Preliminary evidence from our group suggests that strain elastography may offer valuable insights into peri‐implant disease progression, lesion characterization, and the phenotypic factors that may predispose sites to disease onset. Nevertheless, further validation is needed through larger clinical trials to establish the reproducibility, clinical thresholds, and integration of this modality into diagnostic protocols.

5.6. Current limitations in diagnostic ultrasonography

Despite the growing promise of HFUS as a noninvasive imaging modality, it still presents challenges and limitations that currently restrict its widespread use in peri‐implant diagnostics. One of the main limitations is device availability, as high‐frequency ultrasonographic systems capable of producing detailed peri‐implant images are not yet routinely found in most dental practices. This limited accessibility, combined with the high cost of advanced probes and software, can be a significant barrier to adoption. Another significant constraint is the technique‐sensitive nature of ultrasonographic imaging. Accurate interpretation depends on operator skill, probe angulation, and consistent scanning protocols. Even slight variations in technique can lead to discrepancies in image quality and diagnostic reliability, accentuating the need for standardized training and calibration.

While ultrasonography excels in visualizing soft tissues, its focus on soft‐tissue structures results in limited capability to assess hard‐tissue changes around implants. Furthermore, image resolution and clarity can be influenced by implant material, surface properties, and surrounding anatomical structures, which could reduce the diagnostic accuracy.

5.7. Future perspectives and advancements in diagnostic ultrasonography

Future studies directly comparing histological and ultrasonographic findings are essential to validate the diagnostic accuracy of HFUS in peri‐implant diseases. While histopathological analysis remains the gold standard for identifying the inflammatory components and anatomical features of peri‐implant tissues, ¹¹ , ¹² , ³³ , ³⁶ its invasive nature, particularly in human studies, limits its routine clinical applicability. Interestingly, consistent analogies between histological and ultrasonographic features have been observed, suggesting that HFUS may offer a reliable, noninvasive alternative. It allows for the assessment of the SACT dimension, the vertical and horizontal extension of peri‐implant lesions, quantify the proportion of the lesion in relation to the total soft tissue, and tissue perfusion. ²⁵ These parameters may become pivotal in future diagnostic protocols, especially considering that current clinical and radiographic diagnostic criteria are themselves grounded in histological evidence.

Future advancements in HFUS should aim to improve image resolution for more precise detection of key anatomical structures, and incorporate complementary techniques such as color Doppler, elastography and contrast‐enhanced imaging, to enable more accurate characterization of peri‐implant structures.

As emphasized throughout this review, the diagnosis of peri‐implant diseases cannot rely on isolated clinical parameters. A comprehensive approach that integrates clinical, radiographic, and adjunctive diagnostic tools is required. ³⁸ , ⁴¹ Within this framework, the incorporation of ultrasonographic data offers critical benefits: improved detection of subclinical inflammation, enhanced assessment of peri‐implant tissue structure, and quantification of buccal/palatal bone loss—features not captured by traditional radiographs. We propose and suggest an expanded diagnostic framework that integrates ultrasonographic parameters into current clinical and radiographic criteria (Figure 6). This multimodal approach may enable the detection of subclinical stages of peri‐implant diseases, facilitate the identification of transitions from mucositis to peri‐implantitis (an aspect that remains an enigma), ⁹ and support a more nuanced classification system beyond the binary health/disease model. Moreover, it might allow for longitudinal monitoring of therapeutic outcomes and healing dynamics. This diagnostic framework is an evidence‐informed conceptual model, and not yet a validated diagnostic system. Further prospective studies are needed before formal adoption.

FIGURE 6.

Proposed evidence‐informed diagnostic framework that integrates clinical, radiographic and ultrasonographic imaging diagnostic markers to support a more comprehensive and precise diagnosis of peri‐implant diseases. The mentioned diagnostic considerations may facilitate the detection of subclinical stages of peri‐implant diseases*, help identify the transition from mucositis to peri‐implantitis* and facilitate stratification of the disease into multiple levels of severity (color coded arrow showing the different stages in which peri‐implant diseases can be detected). This framework incorporates the assessment of the buccal aspect in diagnosing peri‐implant diseases, highlighting key indicators such as marginal bone loss, aligned with standardized diagnostic criteria, the presence of a hypo‐echoic supracrestal area (HSA), and increased tissue perfusion on Doppler imaging. These ultrasonographic markers, which are absent in healthy sites, reflect pathological changes. As the size and intensity of these markers increase relative to baseline, they may be used to stratify disease severity, offering a more detailed classification system. B‐mode, brilliant mode; BL, baseline; CDV, color Doppler velocity; DTP, Doppler tissue perfusion; iMBL, interproximal marginal bone loss.

Looking ahead, future diagnostic strategies should also consider the integration of artificial intelligence (AI). For instance, recent work has demonstrated that computer vision can estimate periodontal stability and detect peri‐implant marginal bone remodeling directly from radiographs, without relying on traditional clinical parameters such as probing—further supporting the role of AI in streamlining diagnostics. ⁸³ , ⁸⁴ Advanced AI models may ultimately unify clinical, radiographic, microbiological, immunological, and HFUS‐derived data. Such integration has the potential to significantly improve diagnostic accuracy reproducibility, and scalability.

6. CONCLUSION

Advancing diagnostic capabilities in peri‐implant diseases requires a shift beyond traditional boundaries and a willingness to incorporate validated innovations from other medical fields. HFUS and power Doppler imaging can offer unique, noninvasive insights into peri‐implant tissue architecture and inflammatory dynamics. Parameters like the SACT, BBD, tissue perfusion, and lesion extent can provide quantifiable data that complement clinical and radiographic diagnostics, enabling a more precise and individualized assessment. As evidence continues to build, integrating these modalities into clinical practice may redefine how peri‐implant diseases are detected, classified, and managed, paving the way for earlier intervention and personalized treatment strategies.

AUTHOR CONTRIBUTIONS

Maria Elisa Galarraga‐Vinueza, Jae‐Kook Cha, and Magda Feres designed the study. Maria Elisa Galarraga‐Vinueza and Jae‐Kook Cha performed the literature search. Maria Elisa Galarraga‐Vinueza and Jae‐Kook Cha performed data extraction. Maria Elisa Galarraga‐Vinueza, Jae‐Kook Cha, and Lorenzo Tavelli performed the qualitative analysis. Maria Elisa Galarraga‐Vinueza, Lorenzo Tavelli, Jae‐Kook Cha, and Magda Feres contributed to data interpretation. Maria Elisa Galarraga‐Vinueza and Jae‐Kook Cha led the writing. All authors critically revised the manuscript, gave final approval, and agreed to be accountable for all aspects of the scientific work.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

Galarraga‐Vinueza ME, Cha J‐K, Tavelli L, Feres M. Advancing the diagnosis of peri‐implant diseases: From histological foundations to next‐generation ultrasonographic diagnostic markers. J Periodontol. 2026;97:1683–1702. 10.1002/jper.70113

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available upon request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.