Abstract

BackgroundIn the regenerative treatment of intrabony periodontal defects, surgical strategies are primarily determined by defect morphologies. In certain cases, however, direct clinical measurements and intraoral radiographs do not provide sufficient information on defect morphologies. Therefore, the application of cone-beam computed tomography (CBCT) has been proposed in specific cases. 3D virtual models reconstructed with automatic thresholding algorithms have already been used for diagnostic purposes. The aim of this study was to utilize 3D virtual models, generated with a semi-automatic segmentation method, for the treatment planning of minimally invasive periodontal surgeries and to evaluate the accuracy of the virtual models, by comparing digital measurements to direct intrasurgical measurements.MethodsFour patients with a total of six intrabony periodontal defects were enrolled in the present study. Two months following initial periodontal treatment, a CBCT scan was taken. The novel semi-automatic segmentation method was performed in an open-source medical image processing software (3D Slicer) to acquire virtual 3D models of alveolar and dental structures. Intrasurgical and digital measurements were taken, and results were compared to validate the accuracy of the digital models. Defect characteristics were determined prior to surgery with conventional diagnostic methods and 3D virtual models. Diagnostic assessments were compared to the actual defect morphology during surgery.ResultsDifferences between intrasurgical and digital measurements in depth and width of intrabony components of periodontal defects averaged 0.31 ± 0.21 mm and 0.41 ± 0.44 mm, respectively. In five out of six cases, defect characteristics could not be assessed precisely with direct clinical measurements and intraoral radiographs. 3D models generated with the presented semi-automatic segmentation method depicted the defect characteristics correctly in all six cases.ConclusionIt can be concluded that 3D virtual models acquired with the described semi-automatic segmentation method provide accurate information on intrabony periodontal defect morphologies, thus influencing the treatment strategy. Within the limitations of this study, models were found to be accurate; however, further investigation with a standardized validation process on a large number of participants has to be conducted.

Highlights

  • In the regenerative treatment of intrabony periodontal defects, surgical strategies are primarily determined by defect morphologies

  • Direct clinical measurements [7] and intraoral radiographs: Intraoral radiographs (IR)) acquired with parallel long-cone technique [8, 9] are the main tools in periodontal diagnostics

  • Horizontal distances between the marginal bone crest and the tooth surface measured at 3.17 ± 0.98 mm intrasurgically and 3.50 ± 1.02 mm on digital models

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Summary

Introduction

In the regenerative treatment of intrabony periodontal defects, surgical strategies are primarily determined by defect morphologies. Direct clinical measurements and intraoral radiographs do not provide sufficient information on defect morphologies. The aim of this study was to utilize 3D virtual models, generated with a semi-automatic segmentation method, for the treatment planning of minimally invasive periodontal surgeries and to evaluate the accuracy of the virtual models, by comparing digital measurements to direct intrasurgical measurements. Among different site related factors, the morphology of the intrabony defect is the primary determining factor in the selection of the surgical technique and regenerative strategy [6]. Direct clinical measurements (probing pocket depth: PPD, gingival recession: GR, clinical attachment loss: CAL) [7] and intraoral radiographs: IRs) acquired with parallel long-cone technique [8, 9] are the main tools in periodontal diagnostics. IRs provide a two-dimensional (2D) image, in which overlapping anatomical structures make it difficult to accurately determine the true three-dimensional (3D) defect morphology [10, 11]

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