Application of Artificial Intelligence Graphics and Intraoral Scanning in Medical Scenes
Bibliographic record
Abstract
Intraoral scanning technology has become an essential tool in current digital oral medicine, with rapid technological development and increasingly widespread clinical applications. This article reviews the development process of intraoral scanning technology, classifies and introduces the principles of commonly used intraoral scanning technology, and briefly explains the application of this technology in digital diagnosis and treatment in different fields of dentistry. The author analyzes and compares the scanning accuracy of five different types of oral scanners for scanning single jaw complete dentition plaster models. And evaluate the scanning quality to provide reference for clinical application and provide a basis for further improving the performance of domestic oral scanners in the future. The author used a high-precision desktop scanner (Yunjia UP560) to obtain a digital model and used it as truth group data. After using the analysis software Geomagic Studio14 for "best fit comparison", the author conducted deviation analysis on the true value group and experimental group data, evaluated the quality indicators of the scanned data, and compared the scanning accuracy. In terms of scanning accuracy, international manufacturers represented by iTeroElement1 and 3ShapeTrios3 are both at a high level. The Fusion Scanner, Aoralscan2, and Mediti500 instruments have different advantages in accuracy and precision across different measurement ranges. The accuracy of scanning single tooth crowns with several instruments is better than that of scanning single jaw full dentition, indicating that reducing the scanning range can improve the accuracy of the scanner.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".