Artificial Intelligence Segmentation Errors in Implant Planning Software Programs: An Overview
Bibliographic record
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.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".