Academic Journal
Artificial Intelligence Segmentation Errors in Implant Planning Software Programs: An Overview.
| Title: | Artificial Intelligence Segmentation Errors in Implant Planning Software Programs: An Overview. |
|---|---|
| Authors: | Lawand G; Center for Implant Dentistry, Department of Oral and Maxillofacial Surgery, College of Dentistry, University of Florida, Gainesville, Florida, USA., Gonzaga L; Center for Implant Dentistry, Department of Oral and Maxillofacial Surgery, College of Dentistry, University of Florida, Gainesville, Florida, USA., Issa J; Department of Oral Radiology & Digital Dentistry, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam & Vrije Universiteit Amsterdam, Amsterdam, the Netherlands., Revilla-Leon M; Department of Restorative Dentistry, School of Dentistry, University of Washington, Seattle, Washington, USA.; Kois Center, Seattle, Washington, USA.; Department of Prosthodontics, School of Dental Medicine, Tufts University, Boston, Massachusetts, USA., Tohme H; Department of Digital Dentistry, AI, and Evolving Technologies, College of Dental Medicine, Saint Joseph University of Beirut, Beirut, Lebanon., Saleh A; Department of Periodontology, College of Dental Medicine, Saint Joseph University of Beirut, Beirut, Lebanon., Martin W; Center for Implant Dentistry, Department of Oral and Maxillofacial Surgery, College of Dentistry, University of Florida, Gainesville, Florida, USA. |
| Source: | Clinical implant dentistry and related research [Clin Implant Dent Relat Res] 2025 Oct; Vol. 27 (5), pp. e70095. |
| Publication Type: | Journal Article; Review |
| Language: | English |
| Journal Info: | Publisher: John Wiley & Sons, Inc Country of Publication: United States NLM ID: 100888977 Publication Model: Print Cited Medium: Internet ISSN: 1708-8208 (Electronic) Linking ISSN: 15230899 NLM ISO Abbreviation: Clin Implant Dent Relat Res Subsets: MEDLINE |
| Imprint Name(s): | Publication: [2013-] : Malden, MA : John Wiley & Sons, Inc Original Publication: Hamilton, Ont. : B.C. Decker, c1999- |
| MeSH Terms: | Surgery, Computer-Assisted*/methods , Dental Implantation, Endosseous*/methods , Artificial Intelligence* , Software* , Patient Care Planning*, Humans ; Cone-Beam Computed Tomography ; Imaging, Three-Dimensional ; Algorithms ; Dental Implants |
| Abstract: | Background: Static computer-assisted implant surgery (s-CAIS) utilizes 3D imaging data to guide implant placement with high precision. Accurate segmentation of CBCT and intraoral scan data is crucial to creating reliable anatomical models. While AI-driven segmentation has emerged as a promising solution to reduce manual workload, its performance is hindered by technical and algorithmic limitations. Objective: To evaluate the accuracy and limitations of AI-based segmentation in dental implant planning software and to identify common sources of segmentation errors, their clinical implications, and strategies for mitigation. Methods: This work is framed as a narrative literature review and educational practice overview. Observations on software functionality were based on direct use and exploration of varying implant planning software programs. This was conducted to qualitatively describe common segmentation error patterns (boundary errors, over-/under-segmentation, misidentification, and partial volume effects), and demonstrate editing functionalities across four implant planning systems (coDiagnostiX, BlueSkyPlan, Atomica, and Relu). These demonstrations are intended for illustrative purposes and do not constitute a formal, reproducible performance comparison. Results: AI-based segmentation frequently encounters errors due to imaging artifacts, motion blur, anatomical variability, and algorithmic biases. These errors can lead to inaccurate implant positioning, compromised surgical guide designs, and clinical complications. While advanced methods such as U-Net, GANs, and SISTR improve segmentation quality, manual intervention remains essential. The effectiveness of AI tools varies significantly across platforms, and limited editing capabilities often hinder error correction. Conclusion: Despite advances in AI, segmentation errors remain a critical barrier in s-CAIS workflows. Enhanced imaging protocols, algorithmic refinement, clinician oversight, and regulatory transparency are essential to improve segmentation accuracy and ensure safe, effective digital implant planning. (© 2025 Wiley Periodicals LLC.) |
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| Substance Nomenclature: | 0 (Dental Implants) |
| Entry Date(s): | Date Created: 20251007 Date Completed: 20251007 Latest Revision: 20251007 |
| Update Code: | 20260130 |
| DOI: | 10.1111/cid.70095 |
| PMID: | 41055139 |
| Database: | MEDLINE |
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