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Title |
Comparison of AI-generated and conventional CAD/CAM crown designs: An in vitro evaluation
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Authors |
Shipra Shukla1, Meetkumar Dedania2, Kriti Dubey3, Noor Addeen Abo Arsheed4, Karthikeyan Vasudevan5,* & Vipin Arora6
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Affiliation |
1Department of Prosthodontics and Crown & Bridge, People's College of Dental Sciences and Research Centre, Bhanpur, Bhopal Madhya Pradesh, India; 2Department of Conservative Dentistry and Endodontics, K. M. Shah Dental College and Hospital, Sumandeep Vidyapeeth Deemed to be University, Piparia, Vadodara, Gujarat, India; 3Department of Dentistry, ESIC Hospital Sonagiri, Bhopal, Madhya Pradesh, India; 4Department of Restorative Dentistry, Faculty of Dentistry, MAHSA University, Malaysia; 5Department of Prosthodontics, SRM Dental College, Ramapuram, Chennai, Tamil Nadu, India; 6Department of Restorative Dental Sciences, Faculty of Dentistry, Taif University, Taif, Saudi Arabia; *Corresponding author
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Shipra Shukla - E-mail: drshipras19@gmail.com
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Article Type |
Research Article
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Date |
Received September 1, 2026; Revised September 30, 2026; Accepted
September 30, 2026, Published September 30, 2026 |
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Abstract |
Artificial intelligence (AI) can automate crown design, but its accuracy and function require comparison with conventional CAD/CAM. Therefore, it is of interest to compare the both workflows on 30 standardized mandibular first-molar preparations. AI shortened design time (1.73 ± 0.43 vs 5.63 ± 1.14 min; p<0.001) and reduced whole-crown RMS deviation. AI reduced marginal gap (60.3 ± 14.2 versus 68.8 ± 13.0 µm; p<0.001), while conventional CAD was better proximally. Thus, functional contacts were comparable, supporting AI as an efficient design aid with final technician or clinician verification. |
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Keywords |
Artificial intelligence (AI), Computer-aided design and computer-aided manufacturing (CAD/CAM), crown design, digital dentistry, deep learning (DL), marginal fit, occlusion, prosthodontics
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Citation |
Shukla et al. Bioinformation 22(9): 5558-5563 (2026)
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Edited by |
Ashwini Dhopte
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ISSN |
0973-2063
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Publisher |
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License |
This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.
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