An Ultra Low Noise Optoelectronic Module Enables an in Situ Range-Finder Probe to Locate a Neurovascular Bundle in Dental Implant Surgery
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».