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Artificial Intelligence Segmentation Errors in Implant Planning Software Programs: An Overview
Ghida Lawand1, Luiz Gonzaga1, Julien Issa2
1Center for Implant Dentistry, Department of Oral and Maxillofacial Surgery, College of Dentistry, University of Florida, Gainesville, Florida, USA.
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.
