An Ultra Low Noise Optoelectronic Module Enables an in Situ Range-Finder Probe to Locate a Neurovascular Bundle in Dental Implant Surgery
Bibliographic record
Abstract
Dental implantology procedures necessitate surgical drilling of a hole in the jawbone to insert an artificial root over which a dental prosthesis is placed. The success factor of a dental implant is attaining successful osseointegration. This implies a deeper placed implant closest to the Inferior Alveolar Nerve (IAN) but not too close that it carries the risk of damaging this nerve-artery bundle located along the mandible line. Implications for perforating the IAN in patients can vary from temporary numbness to permanent loss of sensation in the lower facial area. Surgeons routinely use computer-assisted navigation techniques relying on static preoperative cone-beam X-ray computed tomography or other imaging methods such as Magnetic Resonance Imaging (MRI) to assess optimal anatomical distances in the patient’s jaw. However, variability of the measured IAN bundle position with these techniques is at the moment accurate to 30% at best. These techniques are known to introduce localization errors and do not benefit from in situ guidance. Therefore there is an important need for an online, in vivo probe to assess drill distances to a critical nerve-artery bundle in situ to not only avoid jaw paralysis but to improve overall outcome of the implantation. To this aim, we investigate a combined multimodal approach using Optical Coherence Tomography (OCT) for high resolution structural imaging, and functional NIR absorption detection in an optical fiber probe eventually small enough to fit into a surgical drill bit, for real-time evaluation of the distance from the probe to the IAN. We designed a reflectance fiber-optic probe to sense the pulsation of the artery (Heartbeat ~1-2Hz) in the IAN bundle, and a detection circuit board with a high gain (2 . 106 V/A), and very low noise (10mVRMS at output) and bandwidth (10Hz) shown on Figure 1. The success of this NIR channel for accurate real-time proximity sensing by capturing low frequency pulsing signals relies on an ultra low noise photon detection module. Measurements with this fiber probe were conducted at probe-target distances varying from 0mm (probe in contact) to 3mm. The results obtained are promising: the oscillating RMS amplitude varies as a function of distance in a similar fashion to previously published work in pulse oximetry literature [1]. The DC level also has a specific trend as a function of distance. Both AC and DC signal components can be used to extract useful distance information. Original designs using a lock-in amplifier to measure the very low RMS amplitude of the of the back-reflected light intensity, modulated by the spatially oscillating absorption changes due to flowing blood surrogate in an artery simulating tube were promising, but limited. Lock-in signal detection with a reference signal modulated at only 1-2 Hz needs filtering with long time constants. Moreover, clinical implementation with the heartbeat as the reference signal would make real-time measurement impractical. As such, we have developed a flexible optoelectronic detection platform with tunable bandwidth, gain and bias enabling the system to not be limited by electronics noise. Using custom phantoms of the jawbone and including a surrogate arterial dynamic pumping circuit, we demonstrated proof of concept for potential detection range of 0.5-4 mm at l-850nm with a source-detector separation of 1.8mm using the pulsating signal from an artery simulating tube. In compliment, a swept-source OCT at 1.3mm provided finer resolution sensitivity to the proximity of the IAN bundle in the 0-0.9mm range and offered the possibility of imaging the inhomogeneous IAN interface. Methods for calibrating and processing the data to provide robust long range NIR finding capabilities in combination with short-range high precision OCT imaging towards a complete clinical solution will be discussed.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".