Moor, J. Dartmouth College Artificial Intelligence Conference: The Next 50 Years. AI mag. 2787 (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. 49687–688 (2018).
Google Scholar
Hamet, P. & Tremblay, J. Artificial intelligence in medicine. metabolism 69P36–P40 (2017).
Ivanov, SH, Webster, C. & Berezina, K. Adoption of robots and service automation by tourism and hospitality companies. tourism and development magazine 271501–1517 (2017).
Google Scholar
Grischke, J., Johannsmeier, L., Eich, L. & Haddadin, S. Dentronics: A review, first concepts, and pilot studies of new application domains for collaborative robots in dental assistance.of 2019 International Conference on Robotics and Automation (ICRA) 6525–6532 (IEEE, 2019).
Farook, TH, Jamayet, NB, Abdullah, JY & Alam, MK Machine learning and intelligent diagnostics in dental and orofacial pain management: a systematic review. pain resolution. manage. 20216659133 (2021).
Zhang, B., Dai, N., Tian, S., Yuan, F. & Yu, Q. Extraction method of tooth preparation margin line based on S-Octree CNN. Inside and outside J. Numer. Method Biomed.English 35e3241 (2019).
Dudley, J. Comparison of coronal tooth reduction with different crown preparations. Inside and outside J. Prosthetics. 31142–144 (2018).
Tran, J., Dudley, J. & Richards, L. Preparation of all-ceramic crowns: alternative techniques. Austria. dent. J. 6265–70 (2017).
Farook, TH, Barman, A., Abdullah, JY & Jamayet, NB Optimization of prosthetic computer-aided design models: virtual assessment of mesh quality degradation using open source software. J. Prosthetics. 30420–429 (2021).
Farouk, TH and others. Development and virtual validation of a novel digital workflow for rehabilitation of palate defects using smartphone-integrated stereophotogrammetry (SPINS). Science.manager 111–10 (2021).
Boner, L. and others. Accuracy of digital techniques for facial, skeletal, and intraoral tissue scanning: A systematic review. J. Prosthet. dent. 121246–251 (2019).
Patzelt, SBM, Emmanouilidi, A., Stampf, S., Strub, JR & Att, W. Accuracy of full-arch scans using an intraoral scanner. Clin. Oral survey. 181687–1694 (2014).
Patil, PG & Lim, HF Using Intraoral Scanning and 3D Print Casting to Facilitate the Fabrication and Modification of New Metal-Ceramic Crowns to Support Existing Removable Partial Dentures. J. Prosthet. dent. (2021).
Kuo, R.-F., Fang, K.-M. & Su, F.-C. Open source technologies and workflows in digital dentistry.of Interface Oral Health Science 2016 (ed.) Kenichiro Sasaki and others.) 165–171 (Springer, 2017).
Jokstad, A. Computer-assisted techniques used in oral rehabilitation and clinical documentation of their claimed benefits – a systematic review. J. Oral Rehabilitation. 44261–290 (2017).
Hanagaru, SB and others. Development, application, and performance of artificial intelligence in dentistry—a systematic review. J. Dent. Science. 16508–522 (2021).
Milner, Minnesota and others. Patient perceptions of new robotic technology in clinical restorative dentistry. J. Med.system 441–10 (2020).
Gilani, S. and others. Robotics and Medicine: The Rainbow of Science in Hospitals. J. Pharm. Bio allied science. 7S381 (2015).
Lee, J.-H., Kim, D.-H., Jeong, S.-N. & Choi, S.-H. Caries detection and diagnosis using deep learning-based convolutional neural network algorithms. J. Dent. 77106–111 (2018).
Tian, S. and others. DCPR-GAN: Crown prosthetic restoration using a two-step adversarial generative network. IEEEJ. Biomed. health. Inform. 26151–160 (2021).
Guy, P. and others. A review of medical image data augmentation techniques for deep learning applications. J. Med. imaging radiat. on call. 65545–563 (2021).
Nozawa, M. and others. Automatic segmentation of the temporomandibular joint disc in magnetic resonance imaging using deep learning techniques. Dent maxilofak. Radiol. 5120210185 (2022).
Pan, SJ & Yang, Q. Research on transfer learning. IEEE transformer. Knowle.data engineering twenty two1345–1359 (2009).
Tran, D., Bourdev, L., Fergus, R., Torresani, L. & Paluri, M. Learning spatiotemporal features with 3D convolutional networks.of Proceedings of the IEEE International Conference on Computer Vision 4489–4497 (2015).
Zheng, G. Effectively incorporate spatial information into mutual information-based 3D-2D registration of CT volumes to radiographic images. Calculate. medicine. Imaging graph. 34553–562 (2010).
milk, AF and others. A novel artificial intelligence-driven tool for tooth detection and segmentation in panoramic radiographs. Clin. Oral survey. twenty five2257–2267 (2021).
Kareem, SA, Pozos-Parra, P. and Wilson, N. Application of belief fusion for the diagnosis of oral cancer. Application software Compute. 611105–1112 (2017).
Bank, D., Koenigstein, N. & Giryes, R. Autoencoders. arXiv preprint arXiv:2003.05991 (2020).
Ritter, AV Sturdevant’s Art & Science of Operative Dentistry-e-Book (Elsevier Health Sciences, 2017).
Google Scholar
Rashid, F. and others. Color change during digital imaging of facial prostheses exposed to unfiltered ambient light and in-office image calibration techniques: an in vitro analysis. pro swan 17e0273029 (2022).
Rashid, F. and others. Factors Affecting Color Stability of Maxillofacial Prosthetic Silicone Elastomers: A Systematic Review and Meta-Analysis. J. Elastom. Plast. 53698–754 (2021).
Farook, TH, Abdullah, JY, Jamayet, NB & Alam, MK Suitable mesh reduction ratios for designing digital obturator prostheses on a personal computer. J. Prosthet. dent. 128, 219–224. https://doi.org/10.1016/j.prosdent.2020.07.039 (2020).
Jamayet, NB, Farrook, TH, Ayman, A.-O., Johari, Y. & Patil, PG Digital workflow and virtual validation of 3D-printed definitive hollow emboli for large palate defects. J. Prosthet. dent. (2021).
Bay, YH and others. Evaluation of differences between conventional and digitally developed models used for prosthetic rehabilitation in cases of untreated cleft palate. Cleft palate cranium. J. 58386–390 (2020).
Farouk, TH and others. Designing 3D prosthetic templates for maxillofacial defect rehabilitation: a comparative analysis of different virtual workflows. Calculate. creature. medicine. 118103646 (2020).
Paulus, D., Wolf, M., Meller, S. & Niemann, H. Three-dimensional computer vision for tooth restoration. med image anal 31–19 (1999).
Rystedt, H., Reit, C., Johansson, E. & Lindwall, O. Seeing through the Dentist’s Eyes: A Video-Based Clinical Demonstration in Preclinical Dentistry Training. J. Dent. educate. 771629–1638 (2013).
Zunair, H., Rahman, A., Mohammed, N. & Cohen, JP Homogenization Techniques Using 3D CNNs to Process CT Scans for Tuberculosis Prediction.of International Workshop on Predictive Intelligence in Medicine 156–168 (Springer, 2020).
Sid, YD and others. Overview of ImageCLEFtuberculosis 2019 – Automated CT-based report generation and tuberculosis severity assessment.of 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 method 39175–191 (2007).
