Design and Implementation of Therapeutic Ultrasound Generating Circuit for Dental Tissue Formation and Tooth-Root Healing
Bibliographic record
Abstract
Biological tissue healing has recently attracted a great deal of research interest in various medical fields. Trauma to teeth, deep and root caries, and orthodontic treatment can all lead to various degrees of root resorption. In our previous study, we showed that low-intensity pulsed ultrasound (LIPUS) enhances the growth of lower incisor apices and accelerates their rate of eruption in rabbits by inducing dental tissue growth. We also performed clinical studies and demonstrated that LIPUS facilitates the healing of orthodontically induced teeth-root resorption in humans. However, the available LIPUS devices are too large to be used comfortably inside the mouth. In this paper, the design and implementation of a low-power LIPUS generator is presented. The generator is the core of the final intraoral device for preventing tooth root loss and enhancing tooth root tissue healing. The generator consists of a power-supply subsystem, an ultrasonic transducer, an impedance-matching circuit, and an integrated circuit composed of a digital controller circuitry and the associated driver circuit. Most of our efforts focus on the design of the impedance-matching circuit and the integrated system-on-chip circuit. The chip was designed and fabricated using 0.8- ¿m high-voltage technology from Dalsa Semiconductor, Inc. The power supply subsystem and its impedance-matching network are implemented using discrete components. The LIPUS generator was tested and verified to function as designed and is capable of producing ultrasound power up to 100 mW in the vicinity of the transducer's resonance frequency at 1.5 MHz. The power efficiency of the circuitry, excluding the power supply subsystem, is estimated at 70%. The final products will be tailored to the exact size of teeth or biological tissue, which is needed to be used for stimulating dental tissue (dentine and cementum) healing.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".