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. 2018 Jun 18;13(6):e0198252.
doi: 10.1371/journal.pone.0198252. eCollection 2018.

Complete denture tooth arrangement technology driven by a reconfigurable rule

Ning Dai  1 , Xiaoling Yu  1 , Qilei Fan  1 , Fulai Yuan  1 , Lele Liu  1 , Yuchun Sun  2
Affiliations

Complete denture tooth arrangement technology driven by a reconfigurable rule

Ning Dai et al. PLoS One. .

Abstract

The conventional technique for the fabrication of complete dentures is complex, with a long fabrication process and difficult-to-control restoration quality. In recent years, digital complete denture design has become a research focus. Digital complete denture tooth arrangement is a challenging issue that is difficult to efficiently implement under the constraints of complex tooth arrangement rules and the patient's individualized functional aesthetics. The present study proposes a complete denture automatic tooth arrangement method driven by a reconfigurable rule; it uses four typical operators, including a position operator, a scaling operator, a posture operator, and a contact operator, to establish the constraint mapping association between the teeth and the constraint set of the individual patient. By using the process reorganization of different constraint operators, this method can flexibly implement different clinical tooth arrangement rules. When combined with a virtual occlusion algorithm based on progressive iterative Laplacian deformation, the proposed method can achieve automatic and individual tooth arrangement. Finally, the experimental results verify that the proposed method is flexible and efficient.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig 1
Fig 1. Technology flowchart of automatic tooth arrangement for complete dentures.
Fig 2
Fig 2. Obtaining the individualized characteristics constraint set.
(a) Edentulous digital model; (b) premade occlusal rim digital model; (c) extracted individualized characteristics constraint set.
Fig 3
Fig 3. Defining artificial tooth characteristics.
(a) Maxillary left 1st incisor; (b) maxillary left 4th premolar; (c) maxillary left 6th molar; (d) mandibular left 1st incisor; (e) mandibular left 4th premolar; and (f) mandibular left 6th molar.
Fig 4
Fig 4. Posture adjustment of anterior teeth.
(a) Defining the maxillary central incisor characteristics; (b) posture rotation of the maxillary central incisor on the tooth arrangement curve; (c) the fullness mark of the maxillary central incisor rotates to the fullness constraint surface; and (d) adjusting the long axis posture of the mandibular central incisor.
Fig 5
Fig 5. Contact constraint model.
(a) Adjacent teeth positioned by Loose Octree; (b) occlusal contact of the first maxillary and mandibular molars; (c) occlusal contact of the posterior dentition.
Fig 6
Fig 6. Tooth arrangement frame based on the rules.
Fig 7
Fig 7. Virtual adjusting occlusion.
(a) Occlusal region analysis; (b) occlusal interference region detection: (c) results after 10 iterative processes; (d) results after 20 iterative processes. Interference distances greater than 0.05 mm are shown in red, and interference distances less than 0.05 mm are shown in light blue.
Fig 8
Fig 8. Extraction of tooth arrangement constraint information.
(a) Preparation of wax occlusal rim; (b) extracted tooth arrangement constraints.
Fig 9
Fig 9. Virtual tooth arrangement.
(a) Arrangement of the maxillary anterior teeth; (b) arrangement of the mandibular anterior teeth; (c) arrangement of the mandibular canines; (d) arrangement of the initial maxillary posterior teeth; (e) arrangement of the initial mandibular posterior teeth; (f) arrangement of the maxillary canines; (g) rearrangement of the maxillary posterior teeth; (h) rearrangement of the mandibular posterior teeth; and (i) front view of the full dentition.
Fig 10
Fig 10. Virtual adjusting occlusion.
(a) Interference region detection; (b) virtual adjusting occlusion with a maximum interference of 0.02 mm; and (c) virtual occlusion with a maximum interference of 0.01 mm.
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Grants and funding

This study was supported by the Natural Science Foundation of Jiangsu Province, China (No. BK20161487) to Ning Dai, Six talent peaks project in Jiangsu Province, China(No. GDZB-034) to Ning Dai, the National Natural Science Foundation of China (No.81271181) to Yuchun Sun, the Fundamental Research Funds for the Central Universities (No.NS2017029) to Ning Dai, the Jiangsu Province science and technology support plan project,China (No. BE2016010-4) to Ning Dai, and Foundation of Graduate Innovation Center in NUAA (NO.kfjj20170520) to Lele Liu. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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