Three-dimensional Intraoral Imaging using a Portable 3D Freehand Ultrasound System: A Phantom Study
Bibliographic record
Abstract
Two-dimensional (2D) intraoral ultrasonography has shown promising potential to image the periodontal structures without the ionizing radiation. To advance the clinical application of intraoral ultrasonography, a novel ultrasound imaging system is necessary to image the structure of periodontal anatomy in 3D space. In this study, we developed a portable 3D intraoral imaging system by consisting of a handheld high-frequency linear intraoral ultrasound transducer (up to 23 MHz) and an optical tracking system. The proposed system can consistently provide the 2D images with 3D poses information for the real-time online reconstruction. After the online reconstruction, a total variation regularization (TVR) technique was adopted to filter the noise in raw tracking poses during the freehand scanning. The validation was performed on two incisors of a mandibular phantom, and the reconstructed volume from the proposed system was compared with the volume from Micro Computed Tomography (μCT). The global average absolute and relative errors measured on the width and height of the two incisors were 0.16±0.06 mm and 2.63±1.50%, respectively. The TVR-corrected reconstructed volumes revealed a smoother reconstructed surface. This in-vitro study demonstrated a comparable result of the proposed system with μCT to image the dental phantom.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".