BACKGROUND: In 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. METHODS: Four 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. RESULTS: Differences 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. CONCLUSION: It 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.
