ArticleBMC oral health2026
Automated micro-CT quantification of clear aligner fit: a pilot comparison of manufacturing processes.
Article in BMC oral health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundClear aligner fit accuracy significantly influences treatment efficacy. Micro-computed tomography (micro-CT) enables precise three-dimensional quantification of aligner-tooth adaptation, but manual measurement is time-intensive and operator-dependent.
objectiveTo develop and validate an automated flood-fill algorithm for micro-CT gap quantification and compare fit accuracy across three aligner manufacturing processes: in-house 3D-printing, Invisalign (thermoformed), and Angelalign (thermoformed).
methodsThis in vitro pilot study used a standardized maxillary dental model to fabricate nine aligners across three manufacturing groups (three aligners per group): direct DLP 3D-printing (LuxCreo Inc., Chicago, US), Invisalign (SmartTrack™), and Angelalign (Angel Pro™). Each aligner was scanned using a Xradia 610 Versa micro-CT (ZEISS, Oberkochen, Germany) at 58.82 μm voxel resolution. An automated flood-fill algorithm was developed in Python to segment and quantify aligner-tooth gap widths from reconstructed volumes, and validated against manual segmentation using Dice similarity coefficient, sensitivity, and specificity. Group differences in gap width were assessed using the Kruskal-Wallis test with Mann-Whitney post-hoc comparisons and Bonferroni correction.
resultsThe automated algorithm achieved a mean Dice similarity coefficient of 0.87 ± 0.04 (range: 0.81-0.94), sensitivity of 0.89 ± 0.05, and specificity of 0.96 ± 0.03. A total of 120 gap width measurements were obtained (3D-printing: n = 42, Invisalign: n = 42, Angelalign: n = 36). Mean gap widths were 193.27 ± 93.03 μm, 186.96 ± 81.86 μm, and 254.89 ± 133.21 μm, respectively. The Kruskal-Wallis test showed no significant overall difference among groups (H = 4.396, P = 0.111). Post-hoc analysis revealed no significant difference between 3D-printing and Invisalign (U = 886.0, P = 0.974, Cliff's Delta = 0.005, negligible effect). Neither comparison with Angelalign reached statistical significance after Bonferroni correction (P = 0.070 and P = 0.065), though both showed small effect sizes (Cliff's Delta ≈ - 0.23). Linear mixed-effects models confirmed these findings (random-effects ICC = 0.179 for aligner-level clustering).
conclusionsThe automated flood-fill algorithm may enable efficient and reproducible micro-CT gap quantification, reducing processing time from ~ 45-60 min to approximately 3.2 min per scan. No statistically significant difference in fit accuracy was observed between in-house 3D-printed and Invisalign thermoformed aligners (mean difference: 6.31 μm), providing preliminary evidence for their potential as a cost-effective manufacturing alternative; however, this should not be interpreted as evidence of equivalence given the small aligner sample size. The Angelalign group showed a trend toward larger gaps with small effect sizes, warranting further investigation. Substantial within-group variability across all manufacturing methods underscores the need for systematic quality control in aligner production.
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