Int J Stomatol

Int J Stomatol ›› 2026, Vol. 53 ›› Issue (5): 639-646.doi: 10.7518/gjkq.2026631

• Digital Oral Medicine Column • Previous Articles    

Applications of artificial intelligence in indirect tooth restorations

Lili Chang1(),Mian Wan1,Xin Xu2()   

  1. 1.State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Dept. of Cariology and Endodontics, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
    2.State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Dept. of Preventive Dentistry, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China Supported by: Natural Science Foundation of Sichuan Province (2025ZNSFSC0762 )
  • Received:2025-09-22 Revised:2026-06-29 Online:2026-09-01 Published:2026-08-28
  • Contact: Xin Xu E-mail:changlili0301@163.com;xin.xu@scu.edu.cn

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Abstract:

Indirect tooth restorations are crucial for repairing dental defects and restoring the functional and aesthetic integrity of teeth. The success of indirect restorations depends on several key factors, including accurate diagnosis, standardized tooth preparation, and precise design and fabrication. Artificial intelligence (AI), with its capabilities in efficient three-dimensional data processing, pattern recognition, and intelligent decision-making, has been extensively utilized in indirect restorations. This review summarizes current advances in AI applications in dental disease diagnosis, tooth preparation, intraoral scanning, restoration design, restoration fabrication, and treatment outcome prediction. Moreover, the existing limitations and future development directions are discussed to provide insights for the advancement of intelligent dental care.

Key words: artificial intelligence, machine learning, indirect dental restoration

CLC Number: 

  • R781.05

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Tab 1

Performance of different AI models in the diagnosis of dental caries"

研究者模型数据类型样本量性能指标/%
准确性灵敏度特异性
Ahmed等[16]U-Net（CNN）咬翼片554807583
Bayraktar等[17]YOLO（CNN）咬翼片80094.5972.2672.26
Zhu等[18]CariesNet（CNN）全景片3 12793.6186.01-
Esmaeilyfard等[11]CNN锥形束CT81998.694.591.8
Qi等[19]CariesAI-3D（CNN）锥形束CT2 14888.687.3-
Yoon等[10]Cascade R（CNN）口内照片24 57876.9--
Ahmed等[20]PointNetSeg口内扫描数据89389.59188
[1] 赵铱民. 口腔修复学[M]. 7版. 北京: 人民卫生出版社, 2012.
Zhao YM. Prosthodontics[M]. 7th ed. Beijing: People's Medical Publishing House, 2012.
[2] Mannocci F, Bitter K, Sauro S, et al. Present status and future directions: the restoration of root filled teeth[J]. Int Endod J, 2022, 55(): 1059-1084.
[3] Wang H, Fu T, Du Y, et al. Scientific discovery in the age of artificial intelligence[J]. Nature, 2023, 620(7972): 47-60.
[4] Nordblom NF, Büttner M, Schwendicke F. Artificial intelligence in orthodontics: critical review[J]. J Dent Res, 2024, 103(6): 577-584.
[5] Revilla-León M, Gómez-Polo M, Vyas S, et al. Artificial intelligence applications in restorative dentis-try: a systematic review[J]. J Prosthet Dent, 2022, 128(5): 867-875.
[6] Ourang SA, Sohrabniya F, Mohammad-Rahimi H, et al. Artificial intelligence in endodontics: fundamental principles, workflow, and tasks[J]. Int Endod J, 2024, 57(11): 1546-1565.
[7] Cheng L, Zhang L, Yue L, et al. Expert consensus on dental caries management[J]. Int J Oral Sci, 2022, 14(1): 17.
[8] Ricucci D, Siqueira JF Jr, Rôças IN, et al. Pulp and dentine responses to selective caries excavation: a histological and histobacteriological human study[J]. J Dent, 2020, 100: 103430.
[9] Szabó V, Szabó BT, Orhan K, et al. Validation of artificial intelligence application for dental caries diagnosis on intraoral bitewing and periapical radiographs[J]. J Dent, 2024, 147: 105105.
[10] Yoon K, Jeong HM, Kim JW, et al. AI-based dental caries and tooth number detection in intraoral photos: model development and performance evaluation[J]. J Dent, 2024, 141:104821.
[11] Esmaeilyfard R, Bonyadifard H, Paknahad M. Dental caries detection and classification in CBCT ima-ges using deep learning[J]. Int Dent J, 2024, 74(2): 328-334.
[12] Zheng LW, Wang HL, Mei L, et al. Artificial intelligence in digital cariology: a new tool for the diagnosis of deep caries and pulpitis using convolutional neural networks[J]. Ann Transl Med, 2021, 9(9): 763.
[13] Farook TH, Ahmed S, Rashid F, et al. Application of 3D neural networks and explainable AI to classify ICDAS detection system on mandibular molars[J]. J Prosthet Dent, 2025, 133(5): 1333-1341.
[14] Zanini LGK, Rubira-Bullen IRF, Nunes FLDS. Enhancing dental caries classification in CBCT images by using image processing and self-supervised lear-ning[J]. Comput Biol Med, 2024, 183: 109221.
[15] Mun SB, Kim J, Kim YJ, et al. Deep learning-based prediction of indication for cracked tooth extraction using panoramic radiography[J]. BMC Oral Health, 2024, 24(1): 952.
[16] Ahmed WM, Azhari AA, Fawaz KA, et al. Artificial intelligence in the detection and classification of dental caries[J]. J Prosthet Dent, 2025, 133(5): 1326-1332.
[17] Bayraktar Y, Ayan E. Diagnosis of interproximal ca-ries lesions with deep convolutional neural network in digital bitewing radiographs[J]. Clin Oral Invest, 2022, 26(1): 623-632.
[18] Zhu HH, Cao Z, Lian LY, et al. CariesNet: a deep learning approach for segmentation of multi-stage caries lesion from oral panoramic X-ray image[J]. Neural Comput Appl, 2023, 35(22): 16051-16059.
[19] Qi S, Shan HX, Fu YJ, et al. A clinically oriented and interpretable AI framework for classifying dentin caries severity on CBCT images[J]. J Prosthet Dent, 2026, 135(3): 594.e1-594.e8.
[20] Ahmed WM, Azhari AA, Alnakhli R, et al. Development and evaluation of an artificial intelligence (AI) model for detecting dental caries from 3D intraoral scans[J]. J Prosthet Dent, 2025, 134(4): 1115.e1-1115.e8.
[21] Tabatabaian F, Beyabanaki E, Alirezaei P, et al. Visual and digital tooth shade selection methods, rela-ted effective factors and conditions, and their accuracy and precision: a literature review[J]. J Esthet Restor Dent, 2021, 33(8): 1084-1104.
[22] Akl MA, Sim CPC, Nunn ME, et al. Validation of two clinical color measuring instruments for use in dental research[J]. J Dent, 2022, 125: 104223.
[23] Chen SL, Zhou HS, Chen TY, et al. Dental shade matching method based on hue, saturation, value color model with machine learning and fuzzy decision[J]. Sensor Mater, 2020, 32(10): 3185.
[24] Wei JQ, Peng MD, Li Q, et al. Evaluation of a novel computer color matching system based on the improved back-propagation neural network model[J]. J Prosthodont, 2018, 27(8): 775-783.
[25] Ueki K, Wakamatsu H, Hagiwara Y. Evaluation of dental prosthesis colors using a neural network[C]//2020 IEEE 5th International Conference on Signal and Image Processing (ICSIP). Nanjing: IEEE, 2020: 210-214.
[26] Li QJ, Chen D, Wang H, et al. A machine learning based approach to standardizing tooth color and shade recommendations[J]. J Prosthet Dent, 2025, 134(6): 2543-2551.
[27] Karcioglu AA, Efitli E, Simsek E, et al. ML-based tooth shade assessment to prevent metamerism in different clinic lights[J]. Lasers Med Sci, 2025, 40(1): 39.
[28] Katano K, Nakajima K, Saito M, et al. Effects of line of vision on posture, muscle activity and sitting balance during tooth preparation[J]. Int Dent J, 2021, 71(5): 399-406.
[29] 于海洋. 数字化牙体预备引导方案的临床效能与挑战[J]. 中华口腔医学杂志, 2025, 60(6): 589-595.
Yu HY. Clinical effectiveness and challenges of digital tooth preparation guidance protocols[J]. Chin J Stomatol, 2025, 60(6): 589-595.
[30] Sun JP, Jiang JG, Ma B, et al. Optimization of grin-ding parameters in robotic-assisted preparation of cracked teeth based on fracture mechanics: FEA and experiment[J]. Comput Methods Programs Biomed, 2025, 258: 108485.
[31] Yuan FS, Lyu PJ. A preliminary study on a tooth preparation robot[J]. Adv Appl Ceram, 2020, 119(5/6): 332-337.
[32] Knudsen JE, Ghaffar U, Ma RZ, et al. Clinical applications of artificial intelligence in robotic surgery[J]. J Robotic Surg, 2024, 18(1): 102.
[33] Zhao Y, Zhang LM, Liu Y, et al. Two-stream graph convolutional network for intra-oral scanner image segmentation[J]. IEEE Trans Med Imaging, 2022, 41(4): 826-835.
[34] Liu JX, Hao J, Lin HZ, et al. Deep learning-enabled 3D multimodal fusion of cone-beam CT and intraoral mesh scans for clinically applicable tooth-bone reconstruction[J]. Patterns, 2023, 4(9): 100825.
[35] Xu XJ, Liu C, Zheng YY. 3D tooth segmentation and labeling using deep convolutional neural networks[J]. IEEE Trans Visual Comput Graphics, 2019, 25(7): 2336-2348.
[36] Zhang LM, Zhao Y, Meng DY, et al. TSGCNet: discriminative geometric feature learning with two-stream graph convolutional network for 3D dental model segmentation[C]//2021 IEEE/CVF Confe-rence on Computer Vision and Pattern Recognition (CVPR). Nashville: IEEE, 2021: 6695-6704.
[37] Digmayer Romero VH, Signori C, Uehara JLS, et al. Diagnostic strategies for restorations management: a 70-month RCT[J]. J Dent Res, 2024, 103(7): 697-704.
[38] Zhang B, Dai N, Tian SK, et al. The extraction method of tooth preparation margin line based on S-Octree CNN[J]. Int J Numer Method Biomed Eng, 2019, 35(10): e3241.
[39] Alsheghri A, Zhang Y, Keren JL, et al. Automatic margin line extraction using 3D deep learning on digital surface models of prepared teeth for crown generation[J]. Comput Biol Med, 2025, 196: 110960.
[40] Choi J, Ahn J, Park JM. Deep learning-based automated detection of the dental crown finish line: an accuracy study[J]. J Prosthet Dent, 2024, 132(6): 1286.e1-1286.e9.
[41] Sawangsri K, Bekkali M, Lutz N, et al. Acceptability and deviation of finish line detection and restoration contour design in single-unit crown: comparative evaluation between 2 AI-based CAD software programs and dental laboratory technicians[J]. J Prosthet Dent, 2025, 134(2): 409-417.
[42] Zhu HY, Jia XY, Zhang CD, et al. ToothCR: a two-stage completion and reconstruction approach on 3D dental model[M]//Lecture Notes in Computer Science. Cham: Springer International Publishing, 2022: 161-172.
[43] Feng Y, Tao B, Fan J, et al. 3D reconstruction for maxillary anterior tooth crown based on shape and pose estimation networks[J]. Int J Comput Assist Radiol Surg, 2023,18(8): 1405-1416.
[44] Chau RCW, Hsung RTC, McGrath C, et al. Accuracy of artificial intelligence-designed single-molar dental prostheses: a feasibility study[J]. J Prosthet Dent, 2024, 131(6): 1111-1117.
[45] Tian SK, Wang MH, Dai N, et al. DCPR-GAN: dental crown prosthesis restoration using two-stage ge-nerative adversarial networks[J]. IEEE J Biomed Health Inform, 2022, 26(1): 151-160.
[46] Tian SK, Wang MH, Yuan FL, et al. Efficient computer-aided design of dental inlay restoration: a deep adversarial framework[J]. IEEE Trans Med Imaging, 2021, 40(9): 2415-2427.
[47] Cesar PF, Miranda RBP, Santos KF, et al. Recent advances in dental zirconia: 15 years of material and processing evolution[J]. Dent Mater, 2024, 40(5): 824-836.
[48] Kim H, Chi S, Oh H. Predicting and optimizing shrinkage rates in zirconia block production through machine learning[C]//2024 15th International Conference on Information and Communication Technology Convergence (ICTC). Jeju Island: IEEE, 2024: 221-222.
[49] Yang JW, Hao ZZ, Xu JN, et al. Fusion machine learning model predicts CAD-CAM ceramic colors and the corresponding minimal thicknesses over va-rious clinical backgrounds[J]. Dent Mater, 2024, 40(2): 285-296.
[50] Matin I, Hadzistevic M, Vukelic D, et al. Development of an expert system for the simulation model for casting metal substructure of a metal-ceramic crown design[J]. Comput Methods Programs Biomed, 2017, 146: 27-35.
[51] Basu B, Gowtham NH, Xiao Y, et al. Biomaterialomics: data science-driven pathways to develop fourth-generation biomaterials[J]. Acta Biomater, 2022, 143: 1-25.
[52] Yamaguchi S, Lee C, Karaer O, et al. Predicting the debonding of CAD/CAM composite resin crowns with AI[J]. J Dent Res, 2019, 98(11): 1234-1238.
[53] Chisini LA, Cenci MS, Skupien JA, et al. Restoration's longevity in endodontically treated teeth: a machine learning survival analysis from randomised clinical trials[J]. Int Endod J, 2026, 59(2): 251-261.
[54] Wang R, Hass V, Wang Y. Machine learning analysis of microtensile bond strength of dental adhesives[J]. J Dent Res, 2023, 102(9): 1022-1030.
[55] Tuntiwong K, Wangman R, Sritart H, et al. Crack localization detection in monolithic zirconia dental crowns via 1D-convolutional neural networks algorithm-based acoustic emission analysis[C]//2024 10th International Conference on Engineering, Applied Sciences, and Technology (ICEAST). Luang Prabang: IEEE, 2024: 53-56.
[56] Grymak A, Yang AHX, Li KC, et al. LSTM-based prediction of wear in 3D-printed restorative mate-rials under various methods[J]. Dent Mater, 2026, 42(1): 91-99.
[57] Martin KD, Zimmermann J. Artificial intelligence and its implications for data privacy[J]. Curr Opin Psychol, 2024, 58: 101829.
[58] Warraich HJ, Tazbaz T, Califf RM. FDA perspective on the regulation of artificial intelligence in health care and biomedicine[J]. JAMA, 2025, 333(3): 241.
[59] Yu KH, Healey E, Leong TY, et al. Medical artificial intelligence and human values[J]. N Engl J Med, 2024, 390(20): 1895-1904.
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