Remote aligner supervision and STL generation with Dental Monitoring®: Clinical integration and case report
Bibliographic record
Abstract
Artificial intelligence is currently being utilized to enhance the efficiency of orthodontic treatment and reduce the need for in-office visits. Dental Monitoring® (DM) facilitates remote supervision of clear aligner fit, oral hygiene, and appliance integrity through patient-performed scans. This case presents a 23-year-old male with an anterior open bite, treated with clear aligners and lower extra-alveolar mini-screws. Personalized clinical protocols developed within the DM platform for guiding aligner changes and monitoring hygiene are described. Each scan generated automated feedback for the patient and detailed reports for the orthodontist. A total of 59 scans were completed, with 88% submitted on time. A significant misfit was detected in 16% of scans, which was managed either by extending the aligner wear or through manual override based on clinical judgment. Hygiene alerts were triggered in 36% of scans, prompting automated reinforcement messages. At the end of treatment, a remote STL file was generated for refinement planning, demonstrating high agreement with intraoral scanning. This case highlights the usefulness of DM in supporting personalized aligner therapy through protocol-based monitoring, improved patient compliance, and reduced clinical viwsits.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".