Revealing the Three‐Dimensional Complexity of Facial Anatomy Through Micro‐Computed Tomography
Bibliographic record
Abstract
BACKGROUND: Micro-computed tomography (micro-CT) offers exceptional three-dimensional resolution for studying facial anatomy, capturing intricate structural relationships with isotropic resolutions as fine as 4-10 μm. However, its use is limited to ex vivo specimens, precluding real-time or functional evaluation. OBJECTIVE: To highlight the complementary value of high-frequency ultrasound in facial anatomical assessment and procedural guidance, particularly in the clinical context. METHODS: We discuss the limitations of micro-CT for dynamic applications and explore the clinical advantages of ultrasound, including Doppler capabilities and real-time observation of soft tissue structures. RESULTS: High-frequency ultrasound (30-70 μm resolution) enables in vivo, dynamic imaging of vascular flow, muscle activity, and filler placement. It is non-invasive, repeatable, and applicable at the point of care. Finger-mounted devices, such as those used in Safe Injection By Ultrasound (SIBUS) protocols, provide sufficient resolution for guiding aesthetic procedures involving superficial facial anatomy. Doppler integration further enhances procedural safety. CONCLUSION: While micro-CT remains ideal for high-resolution anatomical research and educational modeling, ultrasound uniquely enables functional, real-time assessment essential for safe clinical practice. Together, these modalities serve complementary roles in advancing both anatomical understanding and patient care in aesthetic medicine.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".