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Computer-Guided Surgery Utilizing a Computer-Milled Surgical Template

2001· article· en· W2330536903 on OpenAlexaboutno aff
Scott D. Ganz

Bibliographic record

VenueImplant Dentistry · 2001
Typearticle
Languageen
FieldDentistry
TopicDental Radiography and Imaging
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceSurgical proceduresMedicineSurgeryMedical physics

Abstract

fetched live from OpenAlex

Computer-Guided Surgery Utilizing a Computer-Milled Surgical Template M. Klein, M. Abrams, Pract Proced Aesthet Dent 2001;13:165–169 Computer-guided surgery is an evolving technology, pushing the envelope as the dental implant industry strives to replace missing teeth with even greater predictability and efficiency. Yet, it has been a long uphill battle. Computed tomography (CT) technology is still not widely used as a tool for the diagnosis and treatment planning of dental implants, bone grafting procedures, or identification of pathology. Although Klein and Abrams state, “In the area of diagnosis, radiographic templates, CT scans, and surgical simulation software are unparalleled,” there is still resistance to the adoption of this technology into mainstream implant practice. It has been well documented that CT scan imaging allows the clinician to plan the precise location of dental implants with an accurate assessment of site-specific bone density within the three-dimensional anatomy. The question therefore is, “Why has it been so difficult to get clinicians to utilize this valuable tool when the benefits are obvious?” One popular criticism of this diagnostic modality is how to relate the simulated treatment plan to actual clinical practice. Anthropologists have searched for hundreds of years to find the “missing link” between man and ape that they believe will define our origins. Perhaps this discovery will never be made in our lifetime. This simple analogy may have relevance because it has long been the cry of clinicians or academics that CT scan technology has limited use without a true link between the case planning and the actual surgical intervention. Drs. Klein and Abrams have worked for years in collaboration with Columbia Scientific, Inc. to help define a practical solution to this problem based on the solid foundation of SIM/Plant interactive software (Columbia Scientific, Inc. Columbia, MD). It was the refinement of the milling technology and development of the software interface that allowed this idea to become a practical reality. Klein and Abrams reveal two concepts of the missing link (Basic and Advanced), which coincident with the publication date was demonstrated live at the recent Academy of Osseointegration Meeting held in Toronto this past March. The timing could not have been better for reaching the wide circulation of the revamped Practical Procedures and Aesthetic Dentistry. The initial paragraphs define the importance of CT scanning for the proper evaluation of the existing bone, which is further enhanced when a CT scan appliance is used. As this is an introductory article in a non-implant specific journal, many aspects of the diagnostic modality are described in brief terms. A more thorough explanation of the basic imaging concepts can be found by reviewing the ample reference section. The goals of the techniques that the article defines are quietly mentioned in the second paragraph as solving the direct link between producing a predictable and repeatable planned result from a surgical template. This is the perhaps the most important aspect of this innovative approach, and it is delivered in such a subtle manner that many readers might just miss the point. The fact that we can translate simulated treatment plans to an accurate surgical template that can deliver predictable and repeatable results is a major breakthrough and is the ultimate answer to the critics of CT. A basic overview of the fabrication of a computer-milled surgical template from the “blueprint” data of the simulated placement of implants using SIM/Plant is supplied. The three-dimensional coordinates of the implant positions were transferred to a five-axis computer numerical-controlled (CNC) milling machine at a distant location. Special drill guides were installed on the template to direct the precise drilling of implant osteotomies. The “Basic Surgical Template” is defined as the incorporation of a 2-mm drill guide sleeve for accurate penetration of a corresponding implant drill. This allows the clinician to create a basic osteotomy within a specific area and angulation by using a pilot drill. The larger-diameter burs would then be used in the conventional manner. The “Advanced Surgical Template” was more involved, allowing for sequential drills to create larger-diameter osteotomies and the placement of the implant itself through the actual template. Looking through hundreds of journal articles each year it is rare to find new and innovative ideas that will affect the outcomes of our clinical treatment. Therefore, I was disappointed that this article did not fill the entire journal. The accompanying 17 figures were helpful in visualizing the techniques, but they were somewhat small because of space limitations. To be fully appreciated, the CT scan images could have been enlarged because they included all three views; axial, cross-sectional, and panoramic. These illustrations are critical to gaining a complete understanding of the planning process, use of the CT scan appliance, and subsequent milled templates. Unfortunately there are no pictures of the milling machine or the actual process of creating the templates, which was wonderfully demonstrated at the March meeting of the Academy of Osseointegration in Toronto. The authors conclude that conventional methods are unreliable and inefficient when compared with the techniques described. Computer-guided surgery with the use of computer milled surgical templates will reduce surgical error, enhance operator ability, facilitate less invasive surgical procedures, more accurate/predictable implant placement, and enhanced surgical and restorative coordination. Lofty statements which I believe are well founded. I look forward to seeing this exciting aspect of treatment planning coupled with accurate surgical template fabrication elucidated through additional publications and presentations. The concepts are truly important and significant for the future of the implant industry. There are certain areas that need further explanation: the differences between fully and partially edentulous ridges, the role that limited vertical dimension has on accessibility for the surgeon, exactly why the template facilitates less invasive surgical protocols, how to control the soft tissue around the templates, what problems are related to the scanning procedure itself, and the differences in accuracy between the “Basic” and “Advanced Surgical Template” designs. Of great significance is the role that this technology can play in the improvement of communication between all members of the implant team, including the surgeon placing the implants, the restorative dentist, the laboratory responsible for creating the templates, and of course the patient. This truly useful tool can be appreciated by all members of the team. An excellent bonus was the accompanying “Continuing Education Exercise No. 7” where pertinent questions were asked of the reader to gain continuing education credit. In this addendum, the authors posed 10 questions on materials presented in the main article. Readers, who are always encouraged to answer these correctly, will gain an even greater educational experience and better overall understanding of the goals and objectives of this new technology while reflecting on the material in a different light than if the questions had not been included. It just may be that Klein, Abrams, and the talented people at Columbia Scientific, Inc, have finally defined the missing link between simulation and execution. If this proves to be a cost-effective and practical tool, implant dentistry may never be the same.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.364
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.043
GPT teacher head0.304
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designCase report
Domainnot available
GenreEmpirical

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".

Quick stats

Citations33
Published2001
Admission routes1
Has abstractyes

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