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
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 representing adjacent teeth
of the missing teeth using the corresponding tooth dictionaries. These
coefficients are then applied to the dictionaries of the missing teeth to
generate accurate predictions of their positions and shapes. The evaluation
results on real subjects shows that our proposed framework achieves an average
prediction error of 1.04mm for predictions of single missing tooth and an
average prediction error of 1.33mm for the prediction of 14 missing teeth,
which demonstrates its capability of accurately predicting missing teeth in
various patterns. By accurately predicting missing teeth, dental professionals
can improve the planning and placement of dental implants, leading to better
esthetic and functional outcomes for patients undergoing dental implant
procedures.