Accuracy of Guided Implant Placement Using Artificial Intelligence Segmentation Versus Conventional Technique

Official title

Accuracy of Guided Implant Placement Using Artificial Intelligence Segmentation Versus Conventional Technique: A Randomized Clinical Trial

Overview

To compare the accuracy of surgical guided implant using AI tooth segmentation of the CBCT alone without intraoral scan to fabricate the implant guide versus conventional technique by alignment the CBCT and intraoral scan to fabricate the implant guide in posterior implant.

Detailed description

Background:

Implant dentistry has developed rapidly all around the world in the past few decades. A revolutionary change in the field of implant dentistry has been brought forward by Artificial intelligence (AI) technology. Many advantages come from the use of guided implant surgery and virtual implant planning, including optimal surgical and prosthetic treatment plan and predictable and effective application.

Statement of the problem:

Even with over ten years of clinical and research data, as well as advancements in apparatus and technique, there are still discrepancies between planned and achieved implant placements when using conventional guided implant surgery.

Review of literature:

Conventional guided implant surgery is considered the gold standard for implant placement as it provides a higher degree of accuracy and a lower risk of complications after surgery. Conventional guided implant surgery consists of 3 key steps. The first step is obtaining a 3D model of the manually segmented CBCT and a 3D model of the intraoral scan (IOS). The second step is alignment of the 3D model of the CBCT and the IOS by Iterative Closest Point Alignment (ICP Alignment). The third step is the virtual implant planning and surgical guide design. The segmentation is needed to create a 3D surface model from a CBCT or CT. Because of this, any error or inaccuracy in the segmentation or alignment process will lower the associated quality of virtual surgical planning.

Segmentation based on AI shows promise and saves time. This revolutionary AI-driven tool allows for precise and quick segmentation of the CBCT images. Therefore, all the steps of the conventional technique to fabricate an implant surgical guide can be less sophisticated if we use the AI segmentation alone without IOS.

Using AI tooth segmentation of the CBCT to obtain a 3D model and immediately, in one step, start virtual implant planning and designing a tooth-supported surgical guide without intraoral scan can minimize the clinical workload while opening the door for possible uses regarding digital workflows.

The outcomes of this investigation may contribute to the enhancement of pre-operative planning processes, including implant positioning and bone grafting. Still, not much has been discovered about how different segmentation techniques may affect clinical practice.

According to our knowledge there is deficiency in the literature in the evaluation of the accuracy of the AI segmentation of the CBCT when used alone in surgical implant guide without the use of intraoral scan.

Interventions

• Device Implant surgery using surgical guide
  Dental implant surgical guides are customized device used to position dental implants to ensure that the implant is placed in the most ideal location, angulation and depth into the bone.

Primary outcome measures

• The accuracy of the placed implants [Time frame: one week after surgery]
• Horizontal apex deviation, (Measuring unit in millimeter) [Time frame: one week after surgery]
• Angular deviation, (Measuring unit in degree) [Time frame: one week after surgery]

Eligibility criteria

Inclusion criteria

• Patients with missing posterior teeth require implant surgery.
• Patients have remaining teeth that can support the surgical guide.
• The edentulous area should involve healed bone sites at least 3 months after extraction.
• The edentulous area should have bone height of at least 10 mm from the alveolar crest to the nearest vital structure and bone width of at least 6 mm.
• Age: patient above 18 years old.
• All patients are in good health with no systemic, immunologic or debilitating diseases that could affect normal bone healing.
• All selected patients are non-smokers and non-alcoholics. Patients are free from temporomandibular disorders and abnormal (TMD) oral habits such as bruxism.

Exclusion criteria

• Systemic disease that may affect bone quality.
• Patients with poor oral hygiene and active periodontal diseases.
• Patient with limited mouth opening.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Allocation

Randomized

Model

Parallel assignment

Masking

Open label

Primary purpose

Treatment

Study locations

Center list to be confirmed — check the primary protocol.

Publications

• On SW, Cho SW, Park SY, Yi SM, Park IY, Byun SH, Kim JC, Yang BE. Advancements in computer-assisted orthognathic surgery: A comprehensive review and clinical application in South Korea. J Dent. 2024 Jul;146:105061. doi: 10.1016/j.jdent.2024.105061. Epub 2024 May 9. PMID 38729289
• Altalhi AM, Alharbi FS, Alhodaithy MA, Almarshedy BS, Al-Saaib MY, Al Jfshar RM, Aljohani AS, Alshareef AH, Muhayya M, Al-Harbi NH. The Impact of Artificial Intelligence on Dental Implantology: A Narrative Review. Cureus. 2023 Oct 30;15(10):e47941. doi: 10.7759/cureus.47941. eCollection 2023 Oct. PMID 38034167
• Schubert O, Schweiger J, Stimmelmayr M, Nold E, Guth JF. Digital implant planning and guided implant surgery - workflow and reliability. Br Dent J. 2019 Jan 25;226(2):101-108. doi: 10.1038/sj.bdj.2019.44. PMID 30679852
• Mahardawi B, Jiaranuchart S, Arunjaroensuk S, Dhanesuan K, Mattheos N, Pimkhaokham A. The Accuracy of Dental Implant Placement With Different Methods of Computer-Assisted Implant Surgery: A Network Meta-Analysis of Clinical Studies. Clin Oral Implants Res. 2025 Jan;36(1):1-16. doi: 10.1111/clr.14357. Epub 2024 Sep 10. PMID 39254507
• Aghaloo T, Hadaya D, Schoenbaum TR, Pratt L, Favagehi M. Guided and Navigation Implant Surgery: A Systematic Review. Int J Oral Maxillofac Implants. 2023 May-Jun;38(suppl):7-15. doi: 10.11607/jomi.10465. PMID 37436945
• Ulbrich M, Van den Bosch V, Bonsch A, Gruber LJ, Ooms M, Melchior C, Motmaen I, Wilpert C, Rashad A, Kuhlen TW, Holzle F, Puladi B. Advantages of a Training Course for Surgical Planning in Virtual Reality for Oral and Maxillofacial Surgery: Crossover Study. JMIR Serious Games. 2023 Jan 19;11:e40541. doi: 10.2196/40541. PMID 36656632
• Marei HF, Abdel-Hady A, Al-Khalifa K, Al-Mahalawy H. Influence of surgeon experience on the accuracy of implant placement via a partially computer-guided surgical protocol. Int J Oral Maxillofac Implants. 2019 September/October;34(5):1177-1183. doi: 10.11607/jomi.7480. Epub 2019 Apr 1. PMID 30934035
• Khaohoen A, Powcharoen W, Sornsuwan T, Chaijareenont P, Rungsiyakull C, Rungsiyakull P. Accuracy of implant placement with computer-aided static, dynamic, and robot-assisted surgery: a systematic review and meta-analysis of clinical trials. BMC Oral Health. 2024 Mar 21;24(1):359. doi: 10.1186/s12903-024-04033-y. PMID 38509530

Identifiers

NCT: NCT06967090 · AI in Guided implant surgery

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗