Advanced dental surgeries using fused filament fabrication and stereolithography printing: Case reports
Notice bibliographique
Résumé
BACKGROUND: The widespread use of digital imaging can now be combined with additive three-dimensional (3D) printing, changing traditional clinical dentistry, especially in challenging cases. Visualizing the bone and soft tissue anatomy using computed tomography (CT) and intraoral scanning generated digital files that can be further processed for 3D printing. Among the popular 3D printing approaches, fused filament fabrication (FFF) and stereolithography (SLA) are broadly used due to their rapid production, precision, and ease of use. METHODS: The current case series outlines three challenging clinical scenarios where a combination of CT and intraoral scans were utilized for digital planning. FFF multicolor anatomical models and SLA surgical guides were produced using 3D printing technology. The first case outlines the utility of this approach to place the optimal surgical window at the lateral sinus lift with anticipated difficult access. In the second case, distinct sites for autogenous bone harvesting were identified while preserving critical adjacent structures with surgical simulation. Finally, the third case outlines this strategy for optimal surgical access to expose an impacted second premolar. RESULTS: Both clinicians and patients benefited from the educational use of FFF‒SLA 3D-printed models, and all cases were successfully treated without complications. CONCLUSIONS: These cases demonstrate the significant utility of these digital technologies and rapid prototyping for improved pre-surgical planning, patient motivation, and didactic training that contribute to improved quality of clinical care. KEY POINTS: To the authors' knowledge, this is the first case reports employing both fused filament fabrication (FFF) and stereolithography (SLA) printing techniques in dental surgery. This innovative approach addresses a range of clinically challenging scenarios presented in this report. Computed tomography (CT) and intraoral scanning are essential for three-dimensional (3D) reconstruction. Specialized software is required to design the guide with precise specifications, and FFF and SLA printers are necessary for fabricating the 3D model. Three-dimensional reconstruction can be time-intensive, particularly when manual segmentation is necessary. Acquiring proficiency in the software may require additional time, and multicolor 3D printing also demands extended printing durations. PLAIN LANGUAGE SUMMARY: This study explores how digital imaging and three-dimensional (3D) printing can improve complex dental surgeries. Using tools such as computed tomography scans and intraoral scans, dentists can create detailed 3D models of a patient's bone and soft tissues. Two popular 3D printing methods-fused-filament fabrication (FFF) and stereolithography (SLA)-were used to make these models, which help with surgical planning. The study includes three cases where 3D-printed models were used to prepare for difficult dental procedures. In the first case, the 3D model helped plan the best way to access a difficult area for sinus surgery. The second case used the model to identify the best sites for bone harvesting. The third case used the model to plan how to safely expose an impacted tooth. These helped both the dentist and the patient understand the procedure better. All surgeries were successful, demonstrating how FFF and SLA 3D printing enhance planning, making advanced dental surgeries safer and more efficient.
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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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,003 |
| Méta-épidémiologie (sens large) | 0,001 | 0,004 |
| Bibliométrie | 0,006 | 0,005 |
| Études des sciences et des technologies | 0,004 | 0,005 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,011 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,003 |
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 ».