Clinical Evaluation of AI-Generated Dental Crowns

A Clinical Research in Using Artificial Intelligence (AI) to Design Dental Crown

This clinical research validates a fully automatic AI algorithm for dental crown design using GANs trained on University of Hong Kong 3D prosthesis data and AI-powered FEA for stress correction, overcoming CAD/CAM limitations like manual technician time and occlusal errors. In-vitro fatigue tests confirmed performance comparable to conventional crowns. Clinically, AI-designed crowns are compared to technician CAD/CAM controls using 10 FDI criteria (aesthetic/functional/biological), assessed via oral exams, and IOS (wear), to prove feasibility and optimize the algorithm.

Study Overview

• Status: Active, not recruiting
• Conditions: Tooth Defect
• Intervention / Treatment: Device: AI-Designed Dental Crowns; Device: Conventional CAD/CAM Dental Crowns

Detailed Description

Artificial Intelligence (AI) is the science and engineering of machines that act intelligently (1). The Oxford Dictionary defines AI as the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision making, and translation between languages (2). There are many ways AI can be achieved, the most important among them are 1) Machine learning: It is a method where the target is defined and the steps to reach that target is learned by the machine itself by training (gaining experience); 2) Natural language processing, for example, Siri and Google assistant. 3) Computer vision, for example, tesla Autopilot. Many fields have already benefited from AI. In medical field, AI has already been implemented in various medical fields in diagnosis such as diabetic retinopathy, skin cancer and breast lesions (3). In dentistry, most of the application goes to the automatic diagnosis based on CT and radiology images (4).

Digital workflow has become an overwhelming trend in dentistry motivated by the prevalence of intraoral scanners (IOS) and computer aided design and computer aided manufacturing (CAD/CAM). Compared with traditional laboratory methods, which are regarded as time-consuming and technically sensitive, the digital workflow can be greatly convenient and efficient (5). Thus, CAD/CAM facilitates the opportunity for improving the productivity of dental prosthesis.

Current digital workflow consists of four basic elements: 1) tooth preparation and data acquisition (via intraoral scanner, x-ray, CBCT, etc.), 2) data processing and prosthesis design (via CAD), 3) prosthesis fabrication (either laboratory or chairside milling via CAM), and 4) try-in and cementation in the clinic (by the dentist). Despite all the advancements such as the elimination of physical models and labour-saving, many problems still exist in the current workflow. Each dental prosthesis must be customized to meet individual patients' condition and requirement. Designing dental restoration must be conducted and approved by the technician; this is a time-consuming and labour-intensive process even with the assistance of CAD software. In particular, the wrong design in CAD process makes the crowns that can induce major oral problems of: 1) Superocclusion, 2) Infraocclusion, and 3) Overcontour. This said, CAD/CAM does not save a lot of the dentists' and patients' time and cost as advocated. Therefore, there is a need to change the current practice of dental CAD/CAM.

In view of this, with the support of GRF, we have developed a fully automatic algorithm for the design of dental prosthesis by utilizing AI technology. The algorithm was based on two aspects: 1) utilization of the current dental knowledge by learning the materials-human interactions and materials-biomaterials properties to automate the prosthetic design; 2) based on the previous clinically relevant studies, to validate the design from finite element analysis (FEA) results. With the 3D digital dental prosthesis dataset obtained from Prince Philip Dental Hospital, Faculty of Dentistry, The University of Hong Kong, Generative Adversarial Network (GAN) was adopted to train the machine learning model on the design of dental prosthesis. It composed of two deep networks, the generator, and the discriminator. The discriminator could identify the tiny difference between the real and the generated designs, and the generator could create the designs that discriminator cannot tell the difference. Finally, the GAN model converges and produces natural look designs of prosthesis. Afterwards, an AI-enabled FEA algorithm was established in order to achieve the accurate and fast FEA of dental prosthesis. Stress concentration on the prepared tooth and prosthesis, a common cause of the failure, may result from flawed prosthesis design. Based on our published FEA data (6, 7), a validation model was built mainly to detect and correct the errors of design which may cause stress concentration. This FEA machine learning model also served as one of the criteria on evaluating the quality of automatic generated prosthesis.

After the training via GAN and machine learning model, the automatic prosthesis design algorithm needs to be validated by means of mechanical tests in the laboratory and application in clinical practice. Cyclic fatigue is prone to cause failure from stress concentration areas or loading contact points; however, it is hard to be detected by technicians directly (8, 9). In in-vitro validation, specimens were subject to cyclic loading using the Instron universal testing machine (Electro Puls E3000, Instron, Norwood, USA), then failure mode analysis and scanning electron microscopy (SEM) were conducted. Comparable fatigue properties of the automatically designed prosthesis to that of CAD/CAM prosthesis have been confirmed (7, 10).

Therefore, several clinically relevant parameters, such as anatomical form, marginal adaptation, wear behaviour of the restoration and the antagonist, and integrity of the restoration and the abutment tooth, are aimed to be evaluated clinically using World Dental Federation (FDI) criteria (11). Compared with the modified USPHS criteria, FDI criteria may give more sensitive results in relatively short-term clinical trials, as it has more scoping options (12). The criteria can be categorized into aesthetic parameters (4 items), functional parameters (6 items) and biological parameters (6 items). In this study, 10 items are selected as they are relevant to the design procedure of the prosthesis. Data collection will be accomplished using oral examination and grading and IOS.

In evaluation of the amount of wear, digital impressions captured by IOS can be superimposed and analysed directly in the software. The replica is no longer needed, the data capture procedure is simplified, so the error can be reduced. IOS has been utilized in several clinical assessments and its accuracy has been confirmed (15).

This proposal aims at validating prosthesis design by the fully automatic algorithm both clinical side and also to further optimize the algorithm. Restorations designed by the algorithm will be comprehensively evaluated according to the FDI criteria while the CAD/CAM prostheses designed by technicians using ordinary computer-aided design software serve as the control group.

Reference:

1. Norvig P. Artificial intelligence: Early ambitions. New Scientist. 2012;216(2889):ii-iii.
2. OxfordUniversityPress. English Oxford Dictionaries. Oxford University Press; 2019.
3. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nature medicine. 2019;25(1):30.
4. Hwang J-J, Jung Y-H, Cho B-H, Heo M-S. An overview of deep learning in the field of dentistry. 2019.
5. Li RW, Chow TW, Matinlinna JP. Ceramic dental biomaterials and CAD/CAM technology: state of the art. J Prosthodont Res. 2014;58(4):208-16.
6. Maghami E, Homaei E, Farhangdoost K, Pow EHN, Matinlinna JP, Tsoi JK-H. Effect of preparation design for all-ceramic restoration on maxillary premolar: a 3D finite element study. Journal of prosthodontic research. 2018;62(4):436-42.
7. Homaei E, Jin X-Z, Pow EHN, Matinlinna JP, Tsoi JK-H, Farhangdoost K. Numerical fatigue analysis of premolars restored by CAD/CAM ceramic crowns. Dental Materials. 2018;34(7):e149-e57.
8. Keulemans F, Palav P, Aboushelib MM, van Dalen A, Kleverlaan CJ, Feilzer AJ. Fracture strength and fatigue resistance of dental resin-based composites. Dent Mater. 2009;25(11):1433-41.
9. Zhang Z, Guazzato M, Sornsuwan T, Scherrer SS, Rungsiyakull C, Li W, et al. Thermally induced fracture for core-veneered dental ceramic structures. Acta Biomater. 2013;9(9):8394-402.
10. Homaei E, Pow EHN, Matinlinna JP, Akbari M, Tsoi JK-H. Fatigue resistance of monolithic CAD/CAM ceramic crowns on human premolars. Ceramics International. 2016;42(14):15709-17.
11. Hickel R, Peschke A, Tyas M, Mjor I, Bayne S, Peters M, et al. FDI World Dental Federation: clinical criteria for the evaluation of direct and indirect restorations-update and clinical examples. Clin Oral Investig. 2010;14(4):349-66.
12. Cakir NN, Demirbuga S. The effect of five different universal adhesives on the clinical success of class I restorations: 24-month clinical follow-up. Clin Oral Investig. 2019;23(6):2767-76.
13. Sinescu C, Negrutiu M, Topala F, Ionita C, Negru R, Fabriky M, et al. Ceramic and Polymeric Dental Onlays Evaluated by Photo elasticity, Optical Coherence Tomography and Micro Computed Tomography. Proc Spie. 2011;8172.
14. Fujita R, Komada W, Nozaki K, Miura H. Measurement of the remaining dentin thickness using optical coherence tomography for crown preparation. Dent Mater J. 2014;33(3):355-62.
15. Aladag A, Oguz D, Comlekoglu ME, Akan E. In vivo wear determination of novel CAD/CAM ceramic crowns by using 3D alignment. J Adv Prosthodont. 2019;11(2):120-7.

• Study Type: Interventional
• Enrollment (Actual): 40
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Hong Kong
  • Hong Kong, Hong Kong, 000000
    • Faculty of Dentisry, the University of Hong Kong

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• Both male and female patients (aged 18-60 years old) attending the HKU Faculty of Dentistry teaching clinic in Prince Philip Dental Hospital (PPDH), who are in need of single crown restorative treatment in the posterior region.

Exclusion Criteria:

• female patients with pregnancy; patients with any systemic diseases (e.g., uncontrolled diabetes, uncontrolled hypertension, uncontrolled osteoporosis, etc); patients with history of local irradiation therapy; patients with untreated periodontal diseases or poor oral hygiene; patients with severe bruxism or clenching habits; patients with periapical lesions in the treated teeth.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Treatment
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Triple

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: AI-designed crowns; Participants receive single-unit dental crowns automatically designed by a fully AI-based algorithm using Generative Adversarial Networks (GAN) trained on 3D clinical prosthesis data from the University of Hong Kong.	Device: AI-Designed Dental Crowns; Single-unit dental crowns automatically designed by a fully AI-based algorithm using Generative Adversarial Networks (GAN) trained on 3D dental prosthesis datasets from the University of Hong Kong.
Active Comparator: Conventional CAD/CAM Dental Crowns; Participants receive single-unit dental crowns designed manually by experienced dental technicians using standard computer-aided design and computer-aided manufacturing (CAD/CAM) software and workflow (current standard of care).	Device: Conventional CAD/CAM Dental Crowns; Single-unit dental crowns designed manually by experienced dental technicians using standard CAD software and fabricated using computer-aided manufacturing (CAM) methods (milling or 3D printing). This represents the current standard-of-care digital workflow for dental restorations.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Fracture of material or tooth and loss of retention	Primary outcome variables are the fracture of material or tooth and loss of retention. These major failures (graded as "Clinically poor - replacement necessary" on FDI criteria) are assessed by oral examination and IOS for detection of fractures, cracks, or debonding.	12 months (baseline + 6, 12months post-cementation)

Collaborators and Investigators

• Sponsor: The University of Hong Kong

Publications and helpful links

General Publications

• Hickel R, Peschke A, Tyas M, Mjor I, Bayne S, Peters M, Hiller KA, Randall R, Vanherle G, Heintze SD. FDI World Dental Federation: clinical criteria for the evaluation of direct and indirect restorations-update and clinical examples. Clin Oral Investig. 2010 Aug;14(4):349-66. doi: 10.1007/s00784-010-0432-8. Epub 2010 Jul 14.
• Ding H, Cui Z, Maghami E, Chen Y, Matinlinna JP, Pow EHN, Fok ASL, Burrow MF, Wang W, Tsoi JKH. Morphology and mechanical performance of dental crown designed by 3D-DCGAN. Dent Mater. 2023 Mar;39(3):320-332. doi: 10.1016/j.dental.2023.02.001. Epub 2023 Feb 21.

Study record dates

Study Major Dates

• Study Start (Actual): September 5, 2022
• Primary Completion (Actual): December 7, 2025
• Study Completion (Estimated): January 31, 2027

Study Registration Dates

• First Submitted: April 24, 2026
• First Submitted That Met QC Criteria: April 24, 2026
• First Posted (Actual): May 1, 2026

Study Record Updates

• Last Update Posted (Actual): May 1, 2026
• Last Update Submitted That Met QC Criteria: April 24, 2026
• Last Verified: March 1, 2026

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Clinical Evaluation; dental crown; Computer-aided design; Single-unit dental restoration
• Other Study ID Numbers: UW21-367

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No