Digital implantology—a review of virtual planning software for guided implant surgery. Part II: Prosthetic set-up and virtual implant planning
Bibliographic record
Abstract
BACKGROUND: Patient- and technology-related parameters influence the successful implementation of virtual implant planning and guided implant surgery. Besides data processing and computer aided design of drill guides as described in Part I, the possibilities and limitations for prosthetic set-up and virtual implant planning are essential (Part II). METHODS: The following software systems were examined using two different clinical situations for implant therapy: coDiagnostiX™, DentalWings, Canada (CDX); Simplant Pro™, Dentsply, Sweden (SIM); Smop™, Swissmeda, Switzerland (SMP); NobelClinician™, Nobel Biocare, Switzerland (NC); Implant Studio, 3Shape, Denmark (IST). Assessment criteria geared towards interfaces and integrated tools for prosthetic set-up and virtual implant planning. RESULTS: A software interface for an individual virtual prosthetic set-up was provided by two systems (CDX, IST), whereas the set-up of standardized teeth was provided by four systems (CDX, SIM, SMP, IST). Alternatively, a conventional set-up could be scanned and imported. One system could solely work with the digitization of a conventional set-up for virtual implant planning (NC). Stock abutments could be displayed for implant planning, but none of the tested software systems provided tools for the design of an individual abutment. All systems displayed three-dimensional reconstructions or two-dimensional cross-sections with varying orientation for virtual implant placement. The inferior alveolar nerve could be marked to respect a minimum distance between the nerve and the planned implant. Three implant planning systems provided a library to display more than 50 implant systems (CDX, SIM, IST), one system provided 33 implant systems (SMP) and one implant system provided 4 implant systems (NC). CONCLUSION: Depending on the used software system, there are limited options for a virtual set-up, virtual articulators and the display of a virtual prosthetic set-up. The implant systems used by the clinician is important for the decision which software system to choose, as there is a discrepancy between available implant systems and the number of supported systems in each software.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".