Digital Workflow and Guided Surgery in Implant Therapy—Literature Review and Practical Tips to Optimize Precision
Bibliographic record
Abstract
The application of digital technology in implant dentistry refines prosthetically-driven treatment planning by integrating virtual facial and intraoral models with cone-beam computed tomography (CBCT) images. This integration enables the development of more personalized treatment plans, ensures precise implant positioning, and strengthens communication between clinicians and patients, thereby reducing potential errors and risks. Computer-aided implant surgery consists of two primary approaches: static-guided surgery, which uses a physical surgical stent to guide the osteotomy based on the preoperatively planned virtual implant position, and dynamic-guided surgery, which employs an optical tracking system with a real-time monitor display for the visualization of implant osteotomy inside the alveolar bone. Each approach offers distinct advantages and poses unique clinical challenges. This paper provides an overview of the current applications and literature on digital treatment planning and computer-aided implant surgery, discussing the advantages and limitations of each approach. Clinical cases are presented to illustrate the digital workflow and highlight key considerations for implementing these methods. Currently, the use of digital workflow in implant dentistry is rising, and it is essential to strike a balance between precision and practicality. The future is promising, with generalized adoption anticipated.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".