Generative AI for reference-free dentoalveolar aesthetic reconstruction: A multi-center study

Linjun Zhang; Xuejing Gan; Yitao Zhong; Minghong Zhong; Jiaxu Duan; Beichen Wen; Peisheng Zeng; Yiwei Zhong; Mengru Shi; Lingxiao Wang; Sho Ozaki; Yudy Ardilla Utomoi; Lisa R. Amir; Shaohua Ge; Jiaxiang Qin; Zetao Chen

doi:10.1016/j.dtrs.2026.100042

Dental Research ›› 2026, Vol. 1 ›› Issue (3) :100042 DOI: 10.1016/j.dtrs.2026.100042

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Generative AI for reference-free dentoalveolar aesthetic reconstruction: A multi-center study

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^aHospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, and Guangdong Provincial Key Laboratory of Stomatology, Guangzhou 510055, China

^b Guangdong Research Center for Dental and Cranial Rehabilitation and Material Engineering , Guangzhou 510055, China

^c Laboratory of Language Engineering and Computing, Center for Linguistics and Applied Linguistics, Guangdong University of Foreign Studies , Guangzhou 510420, China

^d School of Computer Science and Engineering , Sun Yat-sen University, Guangzhou 510006, China

^e State Key Laboratory of Environmental Adaptability for Industrial Products, China National Electric Apparatus Research Institute Co., Ltd , Guangzhou 510663, China

^f Institute for Physics of Intelligence, The University of Tokyo , Hongo, Tokyo 113-0033, Japan

^g RIKEN Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS) , Wako, Saitama 351-0198, Japan

^h Graduate School of Science and Technology, Hirosaki University , 3 Bunkyo, Hirosaki, Aomori 036-8561, Japan

ⁱ Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Universitas Indonesia , Jakarta 10430, Indonesia

^j Department of Oral Biology, Faculty of Dentistry, Universitas Indonesia , Jakarta 10430, Indonesia

^k Department of Periodontology and Tissue Engineering and Regeneration, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University , Jinan, Shandong 250012, China

^* E-mail addresses: shaohuage@sdu.edu.cn (S. Ge),

qinjx7@mail.sysu.edu.cn (J. Qin),

^** Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, and Guangdong Provincial Key Laboratory of Stomatology, Guangzhou 510055, China. E-mail addresses: chenzet3@mail.sysu.edu.cn (Z. Chen).

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Abstract

Introduction: To develop and validate DAR-AI, a clinically applicable artificial intelligence system for reference-free dentoalveolar aesthetic reconstruction of alveolar bone defects. Materials and methods: DAR-AI was constructed using a Point Cloud Completion Network integrated with a score-based denoising module to automate and standardize reconstruction according to an anatomical morphology-oriented strategy. The system was validated using data from 170 patients across five centers, with an additional 8 cases used for clinical human-machine comparison. Performance was assessed by geometric accuracy, robustness between internal and external testing sets, blinded human-machine comparison, operation time, and feasibility of integration into clinical workflows. Results and discussion: On the external testing set, DAR-AI achieved a Chamfer Distance of 1.191 ± 0.273 mm and a mesh reconstruction RMS of 0.465 ± 0.090 mm, with no statistically significant difference compared with the internal set. In the blinded human-machine comparison, the AI workflow maintained high expert acceptance (most scores > 4.0/5.0) and significantly reduced operation time. The generated 3D models were successfully incorporated into pre-operative communication, 3D-printed bone graft fabrication, and titanium mesh fabrication. Conclusion: DAR-AI provides a rapid, accurate, and generalizable solution for dentoalveolar reconstruction by overcoming the constraints of missing anatomical references. This generative AI model demonstrates clinically acceptable planning performance, with high expert acceptance in most assessment dimensions, and holds significant potential for broader applications in complex dentoalveolar aesthetic reconstruction tasks.

Keywords

Dentoalveolar aesthetic reconstruction / Aesthetic / Artificial intelligence / Point cloud / Deep learning model

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Linjun Zhang, Xuejing Gan, Yitao Zhong, Minghong Zhong, Jiaxu Duan, Beichen Wen, Peisheng Zeng, Yiwei Zhong, Mengru Shi, Lingxiao Wang, Sho Ozaki, Yudy Ardilla Utomoi, Lisa R. Amir, Shaohua Ge, Jiaxiang Qin, Zetao Chen. Generative AI for reference-free dentoalveolar aesthetic reconstruction: A multi-center study. Dental Research, 2026, 1 (3) : 100042 DOI:10.1016/j.dtrs.2026.100042

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