Accurate 3D Prediction of Missing Teeth in Diverse Patterns for Precise Dental Implant Planning
Lei Ma, Peng Xue, Yuning Gu, Yue Zhao, Min Zhu, Zhongxiang Ding,, Dinggang Shen

TL;DR
This paper introduces a novel framework that accurately predicts missing teeth in diverse patterns using dental mesh models, enhancing digital implant planning with average errors around 1.04mm to 1.33mm.
Contribution
The study presents a new method combining point correspondence, tooth dictionaries, and sparse representation to improve missing teeth prediction accuracy.
Findings
Average prediction error of 1.04mm for single missing teeth
Average prediction error of 1.33mm for multiple missing teeth
Effective in various missing teeth patterns
Abstract
In recent years, the demand for dental implants has surged, driven by their high success rates and esthetic advantages. However, accurate prediction of missing teeth for precise digital implant planning remains a challenge due to the intricate nature of dental structures and the variability in tooth loss patterns. This study presents a novel framework for accurate prediction of missing teeth in different patterns, facilitating digital implant planning. The proposed framework begins by estimating point-to-point correspondence among a dataset of dental mesh models reconstructed from CBCT images of healthy subjects. Subsequently, tooth dictionaries are constructed for each tooth type, encoding their position and shape information based on the established point-to-point correspondence. To predict missing teeth in a given dental mesh model, sparse coefficients are learned by sparsely…
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Taxonomy
TopicsDental Radiography and Imaging · Dental Implant Techniques and Outcomes · Dental materials and restorations
