Intra Oral Photogrammetry: Trueness Evaluation of Novel Technology for Implant Complete‐Arch Digital Impression In Vitro
Bibliographic record
Abstract
OBJECTIVES: To investigate the trueness of intraoral photogrammetry (IPG) technology for complete-arch implant digital impression and evaluate the effect of implant number. MATERIAL AND METHODS: All data were fully anonymized in compliance with ethical standards, and a total of 30 complete-arch patient models with 4 (n = 13), 5 (n = 9), or 6 (n = 8) implants were selected from the archive. Digital impressions were taken with IPG and a desktop scanner. Test and reference standard tessellation language (STL) files were superimposed using a best-fit algorithm. For each implant position, mean linear (ΔX, ΔY, ΔZ axes) and angular deviations (ΔANGLE) and three-dimensional (3D) Euclidean distances (ΔEUC) were measured as primary outcomes with a dedicated software program (Hyper Cad S, Cam HyperMill, Open Mind Technologies) and reported as descriptive statistics. Secondary aim was to determine using linear mixed models whether implant number affected trueness. All statistical analyses were conducted using Stata 18 (Stata Corp, College Station) and significance was set at 0.05. RESULTS: A total of 30 definitive casts with 4 (n = 13), 5 (n = 8), and 6 (n = 9) multi-unit abutment (MUA) analogs were analyzed (n = 146 implant positions). The mean deviations along the X-axis were -3.97 ± 32.8 μm, while along the Y-axis, they were -1.97 ± 25.03 μm. For the Z-axis, a greater deviation of -33 ± 34.77 μm was observed. The 3D Euclidean distance deviation measured 57.22 ± 27.41 μm, and the angular deviation was 0.26° ± 0.19°. Statistically significant deviations were experienced for ΔZ, ΔEUC, and ΔANGLE (p < 0.01). Additionally, the number of implants had a statistically significant effect only on the Z-axis deviation (p = 0.03). CONCLUSIONS: Within study limitations, IPG technology was feasible for complete-arch digital implant impression with mean linear, angular, and 3D deviations far below the acceptable range for a passive fit. Reported IPG trueness might avoid a rigid prototype try-in. The implant number had no influence on trueness except for Z-axis deviations. Integrating photogrammetry with intraoral optical scanning (IOS) improved practicality, optimizing the digital workflow. Further clinical trials are needed to confirm these findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".