A Unified and Interpretable Geometric Optimization Framework for Intelligent Automated Implant Planning in CBCT from Single to Consecutive Multiple Missing Teeth
- 1Department of Oral and Maxillofacial Surgery, Second Affiliated Hospital of Harbin Medical University, Harbin, China
Abstract
Automated dental implant planning using artificial intelligence (AI) faces challenges in interpretability, safety assurance, and applicability beyond single-tooth cases. This paper presents a unified explainable framework for cone-beam computed tomography (CBCT) that handles both single-tooth and consecutive multiple missing teeth. The method combines nnU-Net-based anatomical segmentation with a transparent geometric optimization pipeline. Implant placement is formulated as a lexicographic multi-objective constrained optimization problem that enforces clinical safety margins while balancing bone engagement, mesiodistal spacing, and buccolingual position. For multi-tooth cases, a global coordination strategy ensures inter-implant parallelism, minimum spacing, and a common insertion path. The framework was validated on 20 single-tooth and 9 consecutive multi-tooth cases. The AI plans achieved a centroid distance of 1.60{+/-}1.05 mm for single-tooth cases and 1.22{+/-}0.32 mm for multi-tooth cases, with approximately 7{degrees} axial angle difference. All plans achieved 100% compliance with predefined safety thresholds. In a Turing-style test, experts were unable to distinguish AI from human intelligence (HI) plans under blinded conditions. By decoupling perception from reasoning and embedding clinical knowledge as explicit constraints, the framework offers an interpretable and clinically trustworthy solution for computer-aided implant dentistry.
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BibTeX
@misc{zeng2026unified,
title = {{A Unified and Interpretable Geometric Optimization Framework for Intelligent Automated Implant Planning in CBCT from Single to Consecutive Multiple Missing Teeth}},
author = {Z. Zeng and Y. Jiang and J. Li and H. Cao and Y. Lu and H. Zhu and J. Xiong and Z. Tang and N. Hu and H. Ma},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.03.26364640},
url = {https://www.medrxiv.org/content/10.64898/2026.10.03.26364640v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - A Unified and Interpretable Geometric Optimization Framework for Intelligent Automated Implant Planning in CBCT from Single to Consecutive Multiple Missing Teeth
AU - Z. Zeng
AU - Y. Jiang
AU - J. Li
AU - H. Cao
AU - Y. Lu
AU - H. Zhu
AU - J. Xiong
AU - Z. Tang
AU - N. Hu
AU - H. Ma
PY - 2026
DA - 2026/10/03
PB - medRxiv
DO - 10.64898/2026.10.03.26364640
UR - https://www.medrxiv.org/content/10.64898/2026.10.03.26364640v1
AB - Automated dental implant planning using artificial intelligence (AI) faces challenges in interpretability, safety assurance, and applicability beyond single-tooth cases. This paper presents a unified explainable framework for cone-beam computed tomography (CBCT) that handles both single-tooth and consecutive multiple missing teeth. The method combines nnU-Net-based anatomical segmentation with a transparent geometric optimization pipeline. Implant placement is formulated as a lexicographic multi-objective constrained optimization problem that enforces clinical safety margins while balancing bone engagement, mesiodistal spacing, and buccolingual position. For multi-tooth cases, a global coordination strategy ensures inter-implant parallelism, minimum spacing, and a common insertion path. The framework was validated on 20 single-tooth and 9 consecutive multi-tooth cases. The AI plans achieved a centroid distance of 1.60{+/-}1.05 mm for single-tooth cases and 1.22{+/-}0.32 mm for multi-tooth cases, with approximately 7{degrees} axial angle difference. All plans achieved 100% compliance with predefined safety thresholds. In a Turing-style test, experts were unable to distinguish AI from human intelligence (HI) plans under blinded conditions. By decoupling perception from reasoning and embedding clinical knowledge as explicit constraints, the framework offers an interpretable and clinically trustworthy solution for computer-aided implant dentistry.
ER -