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Digital cross-section mapping: automated 3D quantification of peri-sulcular structures adjacent to epi- and
1Department of Prosthodontics and Biomaterials, Centre for Implantology, RWTH Aachen University Hospital, Aachen, Germany.
Objectives:
To present a digital workflow that enables circumferential identification of anatomical landmarks and automated measurement of peri‑sulcular structures adjacent to epi‑ or subgingival finish lines.
Methods:
Five predominantly subgingivally prepared teeth were included. For each tooth, three STL files were obtained: an intraoral scan, a digitized conventional master-cast scan, and the same intraoral scan including the surrounding gingiva. Margin datasets were trimmed, then aligned with the gingival dataset using MarginSlicer, a custom Python program, with semi-automated best-fit registration. The merged dataset was sectioned into 360 transverse slices. Predefined landmarks and distances, including the sulcus floor, papilla vertex, sulcus width, sulcus depth, and preparation depth, were automatically identified and exported in 5° increments.
Results:
Of 365 exported cross sections, 349 (95.6%) yielded calculable margin deviations. Landmarks required for peri‑sulcular measurements were identified in 238 sections for sulcus width (65.2%), 235 for sulcus depth (64.4%), and 257 for preparation depth (70.4%). Per-tooth mean values (mm) ranged from 0.64 ± 0.16 to 0.75 ± 0.21 (sulcus width), 0.77 ± 0.30 to 1.37 ± 0.71 (sulcus depth), and 0.40 ± 0.24 to 0.93 ± 0.62 (preparation depth). Positive margin deviations predominated in all teeth, accounting for 75.8% to 92.5% of measurements.
Conclusions:
The proposed workflow enables automated two-dimensional measurements from three-dimensional datasets and permits circumferential geometric analysis of prepared teeth relative to surrounding gingival tissue.
Clinical Significance:
The workflow provides automated circumferential quantification of peri‑sulcular structures and finish-line capture in digital scans. These standardized measurements can be integrated into digital analysis pipelines to enhance CAD workflow accuracy, improve evaluation of intraoral scanner performance, and increase the reliability of digitally fabricated restorations.
