Computer-aided design and 3-dimensional artificial/convolutional neural network for digital partial dental crown synthesis and validation

Advanced in Tech & Business

Computer-aided design and 3-dimensional artificial/convolutional neural network for digital partial dental crown synthesis and validation

  • Moor, J. The Dartmouth College artificial intelligence conference: The next fifty years. AI Mag. 27, 87 (2006).


    Google Scholar
     

  • Mupparapu, M., Wu, C.-W. & Chen, Y.-C. Artificial intelligence, machine learning, neural networks, and deep learning: Futuristic concepts for new dental diagnosis. Quintessence Int. 49, 687–688 (2018).


    Google Scholar
     

  • Hamet, P. & Tremblay, J. Artificial intelligence in medicine. Metabolism 69, S36–S40 (2017).

    Article 
    CAS 

    Google Scholar
     

  • Ivanov, S. H., Webster, C. & Berezina, K. Adoption of robots and service automation by tourism and hospitality companies. Revista Turismo & Desenvolvimento 27, 1501–1517 (2017).


    Google Scholar
     

  • Grischke, J., Johannsmeier, L., Eich, L. & Haddadin, S. Dentronics: Review, first concepts and pilot study of a new application domain for collaborative robots in dental assistance. In 2019 International Conference on Robotics and Automation (ICRA) 6525–6532 (IEEE, 2019).

  • Farook, T. H., Jamayet, N. B., Abdullah, J. Y. & Alam, M. K. Machine learning and intelligent diagnostics in dental and orofacial pain management: A systematic review. Pain Res. Manag. 2021, 6659133 (2021).

    Article 

    Google Scholar
     

  • Zhang, B., Dai, N., Tian, S., Yuan, F. & Yu, Q. The extraction method of tooth preparation margin line based on S-Octree CNN. Int. J. Numer. Methods Biomed. Eng. 35, e3241 (2019).

    Article 

    Google Scholar
     

  • Dudley, J. Comparison of coronal tooth reductions resulting from different crown preparations. Int. J. Prosthodont. 31, 142–144 (2018).

    Article 

    Google Scholar
     

  • Tran, J., Dudley, J. & Richards, L. All-ceramic crown preparations: An alternative technique. Aust. Dent. J. 62, 65–70 (2017).

    Article 
    CAS 

    Google Scholar
     

  • Farook, T. H., Barman, A., Abdullah, J. Y. & Jamayet, N. B. Optimization of prosthodontic computer-aided designed models: A virtual evaluation of mesh quality reduction using open source software. J. Prosthodont. 30, 420–429 (2021).

    Article 

    Google Scholar
     

  • Farook, T. H. et al. Development and virtual validation of a novel digital workflow to rehabilitate palatal defects by using smartphone-integrated stereophotogrammetry (SPINS). Sci. Rep. 11, 1–10 (2021).

    Article 

    Google Scholar
     

  • Bohner, L. et al. Accuracy of digital technologies for the scanning of facial, skeletal, and intraoral tissues: A systematic review. J. Prosthet. Dent. 121, 246–251 (2019).

    Article 

    Google Scholar
     

  • Patzelt, S. B. M., Emmanouilidi, A., Stampf, S., Strub, J. R. & Att, W. Accuracy of full-arch scans using intraoral scanners. Clin. Oral Investig. 18, 1687–1694 (2014).

    Article 

    Google Scholar
     

  • Patil, P. G. & Lim, H. F. The use of intraoral scanning and 3D printed casts to facilitate the fabrication and retrofitting of a new metal-ceramic crown supporting an existing removable partial denture. J. Prosthet. Dent. (2021).

  • Kuo, R.-F., Fang, K.-M. & Su, F.-C. Open-source technologies and workflows in digital dentistry. In Interface Oral Health Science 2016 (eds Sasaki, K. et al.) 165–171 (Springer, 2017).

    Chapter 

    Google Scholar
     

  • Jokstad, A. Computer-assisted technologies used in oral rehabilitation and the clinical documentation of alleged advantages—A systematic review. J. Oral Rehabil. 44, 261–290 (2017).

    Article 
    CAS 

    Google Scholar
     

  • Khanagar, S. B. et al. Developments, application, and performance of artificial intelligence in dentistry—A systematic review. J. Dent. Sci. 16, 508–522 (2021).

    Article 

    Google Scholar
     

  • Milner, M. N. et al. Patient perceptions of new robotic technologies in clinical restorative dentistry. J. Med. Syst. 44, 1–10 (2020).

    Article 

    Google Scholar
     

  • Jeelani, S. et al. Robotics and medicine: A scientific rainbow in hospital. J. Pharm. Bioallied Sci. 7, S381 (2015).

    Article 
    CAS 

    Google Scholar
     

  • Lee, J.-H., Kim, D.-H., Jeong, S.-N. & Choi, S.-H. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J. Dent. 77, 106–111 (2018).

    Article 

    Google Scholar
     

  • Tian, S. et al. DCPR-GAN: Dental crown prosthesis restoration using two-stage generative adversarial networks. IEEE J. Biomed. Health. Inform. 26, 151–160 (2021).

    Article 

    Google Scholar
     

  • Chlap, P. et al. A review of medical image data augmentation techniques for deep learning applications. J. Med. Imaging Radiat. Oncol. 65, 545–563 (2021).

    Article 

    Google Scholar
     

  • Nozawa, M. et al. Automatic segmentation of the temporomandibular joint disc on magnetic resonance images using a deep learning technique. Dentomaxillofac. Radiol. 51, 20210185 (2022).

    Article 

    Google Scholar
     

  • Pan, S. J. & Yang, Q. A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22, 1345–1359 (2009).

    Article 

    Google Scholar
     

  • Tran, D., Bourdev, L., Fergus, R., Torresani, L. & Paluri, M. Learning spatiotemporal features with 3d convolutional networks. In Proceedings of the IEEE International Conference on Computer Vision 4489–4497 (2015).

  • Zheng, G. Effective incorporating spatial information in a mutual information-based 3D–2D registration of a CT volume to X-ray images. Comput. Med. Imaging Graph. 34, 553–562 (2010).

    Article 

    Google Scholar
     

  • Leite, A. F. et al. Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs. Clin. Oral Investig. 25, 2257–2267 (2021).

    Article 

    Google Scholar
     

  • Kareem, S. A., Pozos-Parra, P. & Wilson, N. An application of belief merging for the diagnosis of oral cancer. Appl. Soft Comput. 61, 1105–1112 (2017).

    Article 

    Google Scholar
     

  • Bank, D., Koenigstein, N. & Giryes, R. Autoencoders. arXiv preprint arXiv:2003.05991 (2020).

  • Ritter, A. V. Sturdevant’s Art & Science of Operative Dentistry-e-Book (Elsevier Health Sciences, 2017).


    Google Scholar
     

  • Rashid, F. et al. Color variations during digital imaging of facial prostheses subjected to unfiltered ambient light and image calibration techniques within dental clinics: An in vitro analysis. PLoS ONE 17, e0273029 (2022).

    Article 
    CAS 

    Google Scholar
     

  • Rashid, F. et al. Factors affecting color stability of maxillofacial prosthetic silicone elastomer: A systematic review and meta-analysis. J. Elastom. Plast. 53, 698–754 (2021).

    Article 
    CAS 

    Google Scholar
     

  • Farook, T. H., Abdullah, J. Y., Jamayet, N. B. & Alam, M. K. Percentage of mesh reduction appropriate for designing digital obturator prostheses on personal computerse. J. Prosthet. Dent. 128, 219–224. https://doi.org/10.1016/j.prosdent.2020.07.039 (2020).

    Article 

    Google Scholar
     

  • Jamayet, N.B., Farook, T. H., Ayman, A.-O., Johari, Y. & Patil, P. G. Digital workflow and virtual validation of a 3D-printed definitive hollow obturator for a large palatal defect. J. Prosthet. Dent. (2021).

  • Beh, Y. H. et al. Evaluation of the differences between conventional and digitally developed models used for prosthetic rehabilitation in a case of untreated palatal cleft. Cleft Palate Craniofac. J. 58, 386–390 (2020).

    Article 

    Google Scholar
     

  • Farook, T. H. et al. Designing 3D prosthetic templates for maxillofacial defect rehabilitation: A comparative analysis of different virtual workflows. Comput. Biol. Med. 118, 103646 (2020).

    Article 
    CAS 

    Google Scholar
     

  • Paulus, D., Wolf, M., Meller, S. & Niemann, H. Three-dimensional computer vision for tooth restoration. Med Image Anal 3, 1–19 (1999).

    Article 
    CAS 

    Google Scholar
     

  • Rystedt, H., Reit, C., Johansson, E. & Lindwall, O. Seeing through the dentist’s eyes: Video-based clinical demonstrations in preclinical dental training. J. Dent. Educ. 77, 1629–1638 (2013).

    Article 

    Google Scholar
     

  • Zunair, H., Rahman, A., Mohammed, N. & Cohen, J. P. Uniformizing techniques to process CT scans with 3D CNNs for tuberculosis prediction. In International Workshop on PRedictive Intelligence in Medicine 156–168 (Springer, 2020).

  • Cid, Y. D. et al. Overview of ImageCLEFtuberculosis 2019-Automatic CT-based Report Generation and Tuberculosis Severity Assessment. In CLEF (Working Notes) (2019).

  • Faul, F., Erdfelder, E., Lang, A.-G. & Buchner, A. G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods 39, 175–191 (2007).

    Article 

    Google Scholar
     

  •