INTRAOPERATIVE SURGICAL NAVIGATION BASED ON LASER SCANNER FOR IMAGE-GUIDED ORAL AND MAXILLOFACIAL SURGERY
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Bibliographic record
Abstract
In oral and maxillofacial surgery, computer-assisted navigation technologies have been widely used to achieve intraoperative positioning. The traditional methods mainly rely on the experience of doctors and the difference between the locations of key points in the surgical area and the preoperative planning, which have certain limitations. In this paper, a new intraoperative surgical navigation framework based on mobile laser scanner is proposed, which ensures that surgery is performed accurately according to the preoperative planning. The framework mainly includes two parts. First, the real-time surface reconstruction of the anatomy should be realized during the operation. Second, the acquired image is matched to the planned image in real time. Although the most common method of surface reconstruction is to render the volume directly from raw data or render the surface from the segmented data using computed tomography/magnetic resonance (CT/MR) data, this method is too complicated for performing the real-time operation during surgery. Furthermore, a new surface registration technique is proposed for image-guided oral and maxillofacial surgery based on the point sets. To improve the registration accuracy and robustness, the point sets are modeled by Mixed Student’s t-Distribution model. In the experiments, the point sets of CT data are from 10 patients with craniomaxillofacial diseases and the surface point set is from the LRS. The TRE of 10 data was less than 1[Formula: see text]mm. Compared with the paired-point registration method and Iterative Closest Point algorithm, the results demonstrated better performance of the proposed method, the surgical situation can be displayed in real time during the surgical process, and any differences from the surgical plan can also be reflected.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it