A Prognosis System for Periimplant Diseases

Decker, Ann M.; Sheridan, Rachel A.; Lin, Guo‐Hao; Sutthiboonyapan, Pimchanok; Carroll, William R.; Wang, Hom Lay

doi:10.1097/id.0000000000000276

Cited by 13 publications

(12 citation statements)

References 34 publications

(21 reference statements)

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“…A dental clinician needs to locate appropriate landmarks to analyze radiographs and diagnose peri-implantitis. Moreover, as suggested by previous studies, the severity of peri-implantitis can be categorized based on the percentage of the radiographic bone loss [24][25][26]. For these tasks, the automated system proposed herein can be used for assisting dental researchers or practitioners.…”

Section: Discussionmentioning

confidence: 99%

“…Some studies exist on classification systems for peri-implantitis that use radiographic bone loss together with clinical indicators, such as bleeding/suppuration on probing or probing depth [24][25][26]. These studies used the ratio of the radiographic bone loss over the total implant length to classify the peri-implantitis.…”

Section: Bone Loss Ratio and Classificationmentioning

confidence: 99%

See 1 more Smart Citation

Peri-Implant Bone Loss Measurement Using a Region-Based Convolutional Neural Network on Dental Periapical Radiographs

Cha

Yoon

Yeo

et al. 2021

JCM

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Determining the peri-implant marginal bone level on radiographs is challenging because the boundaries of the bones around implants are often unclear or the heights of the buccal and lingual bone levels are different. Therefore, a deep convolutional neural network (CNN) was evaluated for detecting the marginal bone level, top, and apex of implants on dental periapical radiographs. An automated assistant system was proposed for calculating the bone loss percentage and classifying the bone resorption severity. A modified region-based CNN (R-CNN) was trained using transfer learning based on Microsoft Common Objects in Context dataset. Overall, 708 periapical radiographic images were divided into training (n = 508), validation (n = 100), and test (n = 100) datasets. The training dataset was randomly enriched by data augmentation. For evaluation, average precision, average recall, and mean object keypoint similarity (OKS) were calculated, and the mean OKS values of the model and a dental clinician were compared. Using detected keypoints, radiographic bone loss was measured and classified. No statistically significant difference was found between the modified R-CNN model and dental clinician for detecting landmarks around dental implants. The modified R-CNN model can be utilized to measure the radiographic peri-implant bone loss ratio to assess the severity of peri-implantitis.

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Section: Discussionmentioning

confidence: 99%

Section: Bone Loss Ratio and Classificationmentioning

confidence: 99%

Peri-Implant Bone Loss Measurement Using a Region-Based Convolutional Neural Network on Dental Periapical Radiographs

Cha

Yoon

Yeo

et al. 2021

JCM

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“…When there is a substantial loss of bone in peri-implantitis (half the length of the implant), the success of surgery is unlikely. 88 Implants that are placed in an improper position can limit treatment outcomes. Additionally, implant mobility indicates advanced bone loss (> 60%) or a lack of osseointegration of the implant.…”

Section: Regenerative Surgerymentioning

confidence: 99%

Peri-implantitis Update: Risk Indicators, Diagnosis, and Treatment

et al. 2020

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Despite the success rates of dental implants, peri-implantitis presents as the most common complication in implant dentistry. This review discusses various factors associated with peri-implantitis and various available treatments, highlighting their advantages and disadvantages. Relevant articles on peri-implantitis published in English were reviewed from August 2010 to April 2020 in MEDLINE/PubMed, Scopus, and ScienceDirect. The identified risk indicators of peri-implant diseases are plaque, smoking, history of periodontitis, surface roughness, residual cement, emergence angle >30 degrees, radiation therapy, keratinized tissue width, and function time of the implant, sex, and diabetes. Peri-implantitis treatments can be divided into nonsurgical (mechanical, antiseptic, and antibiotics), surface decontamination (chemical and laser), and surgical (air powder abrasive, resective, and regenerative). However, mechanical debridement alone may fail to eliminate the causative bacteria, and this treatment should be combined with other treatments (antiseptics and surgical treatment). Surface decontamination using chemical agents may be used as an adjuvant treatment; however, the definitive clinical benefit is yet not proven. Laser treatment may result in a short-term decrease in periodontal pocket depth, while air powder abrasive is effective in cleaning a previously contaminated implant surface. Surgical elimination of a pocket, bone recontouring and plaque control are also effective for treating peri-implantitis. The current evidence indicates that regenerative approaches to treat peri-implant defects are unpredictable.

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“…Different authors have concluded that when bone loss reaches or exceeds 50% of the total length of the implant, the treatment of choice would be implant removal, in view of the hopeless prognosis 7,8 . Accordingly, other therapeutic options must be proposed for these patients in order to restore chewing function and aesthetics 7,9,10 …”

Section: Introductionmentioning

confidence: 99%

Hard tissue dimensional changes following implant removal due to peri‐implantitis: A retrospective study

Pons

Carreño

Amerio

et al. 2021

Clin Implant Dent Rel Res

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Background The current evidence regarding the alterations experienced by the alveolar ridge (hard tissue changes) after implant removal due to peri‐implantitis is limited. Purpose To assess the hard tissue dimensional changes following implant removal due to peri‐implantitis. Material and methods Clinical records were examined to identify patients with implants that had to be removed due to a hopeless prognosis secondary to peri‐implantitis due to expendability of peri‐implantitis implants for functional reasons. Patients with preoperative and postoperative cone‐beam computed tomography (CBCT) scans were included. Patient‐related, implant‐related, and surgery‐related factors were assessed based on the clinical records. Linear measurements were made to evaluate the influence of bone plate thickness (BPT), ridge width (RW), and ridge height (RH) at various levels upon the outcome of implant removal. A descriptive statistical analysis of the quantitative and qualitative variables was performed. Correlations of the variables with the primary outcome (dimensional changes) were tested using univariate and multivariate analyses (multinomial random intercept mixed model linear regressions). Results A total of 26 patients (nimplants = 79) met the eligibility criteria. The mean decrease in RW at 1 and 3 mm below the crest was 11.3% and 4.4%, respectively (P < 0.001). Buccal and lingual RH was significantly reduced by 2.2% and 6.3%, respectively (P < 0.001). Few patient‐related, implant‐related, and surgery‐related factors appeared to have an impact upon the hard tissue dimensional changes. Bone regeneration simultaneous to implant removal minimized the dimensional changes of the ridge both vertically (5% lesser buccal RH reduction) and horizontally (12% lesser RW reduction) when compared with spontaneous healing. The use of a reverse‐torque removal kit seemed to be critical in limiting the dimensional changes of the ridge. Conclusions Minimal hard tissue changes can be expected following implant removal due to peri‐implantitis. Simultaneous bone regeneration procedures and the use of a removal kit may considerably reduce the impact upon the dimensional changes (NCT04534361).

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A Prognosis System for Periimplant Diseases

Abstract: The proposed prognostic system can be used as a tool for clinicians as they develop a treatment plan for all stages of periimplant disease.

Cited by 13 publications

References 34 publications

Peri-Implant Bone Loss Measurement Using a Region-Based Convolutional Neural Network on Dental Periapical Radiographs

Peri-Implant Bone Loss Measurement Using a Region-Based Convolutional Neural Network on Dental Periapical Radiographs

Peri-implantitis Update: Risk Indicators, Diagnosis, and Treatment

Hard tissue dimensional changes following implant removal due to peri‐implantitis: A retrospective study

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