Clinical oral investigations · 2026
Effect of 3D superimposition software on intraoral scanner accuracy and monitoring outcome.
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Mehl Albert, Buhl Jenny, Ender Andreas
Аннотация
OBJECTIVES: To determine whether 3D superimposition software ("Apps") systematically affects 3D deviation outcomes derived from intraoral scanner (IOS) data, using two quantile‑range accuracy metrics (Q90_Q10_half and Q95_Q05_half).
MATERIALS AND METHODS: Two jaw‑sized reference models were digitized with a high‑resolution optical scanner (ATOS III) to generate reference STL meshes (REF_STL). Five IOS devices acquired ten scans per device and per jaw model, exported as STL meshes (IMP_STL): Emerald Green, iTero 5D, Medit i900, Primescan, and Trios 5. Each IMP_STL was trimmed (1 mm above gingival line) and superimposed to REF_STL in four Apps: commercial industrial software: Geomagic Control X and GOM Inspect, and open dentistry-focused software Medit Design (Compare) and Dentexion; resulting in total to N = 400 superimpositions. For each superimposition, Q90_Q10_half and Q95_Q05_half values were computed from point-wise signed surface distances. A linear mixed‑effects model tested factor App as fixed effect with random intercepts for factors Device and Jaw, followed by Bonferroni‑adjusted pairwise comparisons (α = 0.05).
RESULTS: App significantly influenced Q90_Q10_half (p < 0.01) and Q95_Q05_half (p < 0.001). Control X yielded higher adjusted means than Dentexion and Compare for Q90_Q10_half (both p < 0.01), while Dentexion, GOM, and Compare did not differ. For Q95_Q05_half, Control X exceeded all other Apps (all p < 0.01). Notably, IOS rankings were stable across Apps, indicating that software altered magnitude but not relative ordering.
CONCLUSIONS: Evaluation software is a significant source of variability in 3D deviation metrics; Control X produced consistently higher (more conservative) quantile‑range values, particularly for Q95_Q05_half.
CLINICAL RELEVANCE: Inter‑study comparisons in digital accuracy research should report the evaluation software and settings. Dentistry‑focused Apps (e.g., with automated tooth segmentation) may reduce operator‑dependent effects and facilitate clinically applicable monitoring.
MATERIALS AND METHODS: Two jaw‑sized reference models were digitized with a high‑resolution optical scanner (ATOS III) to generate reference STL meshes (REF_STL). Five IOS devices acquired ten scans per device and per jaw model, exported as STL meshes (IMP_STL): Emerald Green, iTero 5D, Medit i900, Primescan, and Trios 5. Each IMP_STL was trimmed (1 mm above gingival line) and superimposed to REF_STL in four Apps: commercial industrial software: Geomagic Control X and GOM Inspect, and open dentistry-focused software Medit Design (Compare) and Dentexion; resulting in total to N = 400 superimpositions. For each superimposition, Q90_Q10_half and Q95_Q05_half values were computed from point-wise signed surface distances. A linear mixed‑effects model tested factor App as fixed effect with random intercepts for factors Device and Jaw, followed by Bonferroni‑adjusted pairwise comparisons (α = 0.05).
RESULTS: App significantly influenced Q90_Q10_half (p < 0.01) and Q95_Q05_half (p < 0.001). Control X yielded higher adjusted means than Dentexion and Compare for Q90_Q10_half (both p < 0.01), while Dentexion, GOM, and Compare did not differ. For Q95_Q05_half, Control X exceeded all other Apps (all p < 0.01). Notably, IOS rankings were stable across Apps, indicating that software altered magnitude but not relative ordering.
CONCLUSIONS: Evaluation software is a significant source of variability in 3D deviation metrics; Control X produced consistently higher (more conservative) quantile‑range values, particularly for Q95_Q05_half.
CLINICAL RELEVANCE: Inter‑study comparisons in digital accuracy research should report the evaluation software and settings. Dentistry‑focused Apps (e.g., with automated tooth segmentation) may reduce operator‑dependent effects and facilitate clinically applicable monitoring.