Text-Conditioned Multi-Expert Regression Framework for Fully Automated Multi-Abutment Design
Authors: Mianjie Zheng, Xinquan Yang, Xuefen Liu, Xuguang Li, Kun Tang, He Meng, Linlin Shen
Organizations: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China · AI Research Center for Medical Image Analysis and Diagnosis, Shenzhen University, Shenzhen, China · National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, China · Department of Stomatology, Shenzhen University General Hospital, Shenzhen, China · School of Artificial Intelligence, Shenzhen University, Shenzhen, China
Dental implant abutments serve as the geometric and biomechanical interface between the implant fixture and the prosthetic crown, yet their design relies heavily on manual effort and is time-consuming. Although deep neural networks have been proposed to assist dentists in designing abutments, most existing approaches remain largely manual or semi-automated, requiring substantial clinician intervention and lacking scalability in multi-abutment scenarios. To address these limitations, we propose TEMAD, a fully automated, text-conditioned multi-expert architecture for multi-abutment design. This framework integrates implant site localization and implant system, compatible abutment parameter regression into a unified pipeline. Specifically, we introduce an Implant Site Identification Network (ISIN) to automatically localize implant sites and provide this information to the subsequent multi-abutment regression network. We further design a Tooth-Conditioned Feature-wise Linear Modulation (TC-FiLM) module, which adaptively calibrates mesh representations using tooth embeddings to enable position-specific feature modulation. Additionally, a System-Prompted Mixture-of-Experts (SPMoE) mechanism leverages implant system prompts to guide expert selection, ensuring system-aware regression. Extensive experiments on a large-scale abutment design dataset show that TEMAD achieves state-of-the-art performance compared to existing methods, particularly in multi-abutment settings, validating its effectiveness for fully automated dental implant planning.
Figures & tables
Figure 1: Illustration of the traditional CAD–CAM workflow for multi-abutment design, where each implant site must be positioned and measured individually before designing the corresponding abutment based on the measured parameters.
Figure 2: Illustration of the self-supervised masked autoencoder pretraining framework.
Figure 3: The overall framework of the proposed method.
Figure 4: Visualization of the remeshing process. The original intraoral scan mesh is first converted into a base mesh through manifold repair and mesh simplification. The base mesh is then refined through three successive subdivision steps, where each face is split into four ordered faces at each step. Finally, the refined mesh is partitioned into Fn patches, where each patch contains 64 faces generated after three subdivisions, serving as the input to the network.
Figure 5: Comparison of parameter-specific performance for multi-abutment prediction. Each subplot corresponds to one key parameter. The horizontal axis denotes IoU performance, while the vertical axis lists the values of the corresponding parameter.
Figure 6: Visualization of pre-trained reconstruction performance.
Network
Params(M)
FLOPs(G)
AISL (%)
Acc@1(%)
Acc@2(%)
Acc@3(%)
AISC (%)
PointFormer
12.91
43.01
19.86
32.98
33.64
33.64
26.57
PointNet
1.61
8.5
33.20
51.27
52.60
52.67
48.89
PointNet++
1.74
8.5
82.46
92.76
92.90
92.93
86.43
Table 1: The performance of IAPM. AISL : Implant Site Localization Accuracy, AISC : Implant Site Count Accuracy, Acc@k denotes the proportion of ground-truth implant sites whose predicted locations fall within k adjacent tooth positions (intra-arch distance), Params: Parameters, FLOPs: Floating Point Operations Per Second.
ISIN
SPMoE
Transgingival
Diameter
Height
✗
✔
29.42
72.00
40.01
✔
✗
26.24
55.63
19.91
✔
✔
29.88
70.78
40.45
Table 2: Ablation experiments on the ISIN and SPMoE module.
Dataset
Modality
Method
Transgingival
Diameter
Height
SA
PointNet
24.62
62.17
22.83
PointNet++
26.30
57.38
15.39
Point Cloud
PointMAE
25.10
55.83
43.16
PointMamba
30.14
62.17
46.49
PointFEMAE
26.60
57.70
19.16
Mesh
MeshMAE
28.61
62.53
18.44
Table 3: Comparison of TEMAD with other mainstream. SA denotes the dataset containing only single-abutment cases, while SMA includes both single- and multi-abutment samples.
Figure 7: Ablation study on encoder pretraining. The horizontal axis denotes IoU performance, while the vertical axis corresponds to the three key abutment parameters, including transgingival, diameter, and height. w/o PT and w/ PT indicate model variants trained without and with encoder pretraining, respectively.
School of Artifical Intelligence, Shenzhen University, Shenzhen, China · National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, China · Huangpu People’s Hospital, Zhongshan City, China +1
Department of Electrical and Electronic Engineering, University of Peradeniya, Peradeniya, Sri Lanka · Department of Oral Medicine and Periodontology, Faculty of Dental Sciences, University of Peradeniya, Peradeniya, Sri Lanka