College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China.
Laboratory for Marine Biology and Biotechnology, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao, China.
CAS and Shandong Province Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China.
Laboratory for Marine Biology and Biotechnology, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao, China.
CAS and Shandong Province Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China.
Laboratory for Marine Biology and Biotechnology, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao, China.
CAS and Shandong Province Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China.
Deep Sea Research Center, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China.
Laboratory for Marine Biology and Biotechnology, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao, China.
CAS and Shandong Province Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China.
Laboratory for Marine Biology and Biotechnology, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao, China.
CAS and Shandong Province Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China.
Laboratory for Marine Biology and Biotechnology, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao, China.
CAS and Shandong Province Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China.
Department of Biochemistry, Cancer Institute of the Second Affiliated Hospital (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education), School of Medicine, Zhejiang University, Hangzhou, China.
Department of Biochemistry, Cancer Institute of the Second Affiliated Hospital (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education), School of Medicine, Zhejiang University, Hangzhou, China.
Department of Biochemistry, Cancer Institute of the Second Affiliated Hospital (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education), School of Medicine, Zhejiang University, Hangzhou, China.
Department of Biochemistry, Cancer Institute of the Second Affiliated Hospital (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education), School of Medicine, Zhejiang University, Hangzhou, China.
Department of Biochemistry, Cancer Institute of the Second Affiliated Hospital (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education), School of Medicine, Zhejiang University, Hangzhou, China.
This study aimed to understand the effect of physiological and dental implant-related parameter variations on the osseointegration for an implant-supported fixed prosthesis. Eight design factors were ...
In recent years, artificial intelligence (AI) has made remarkable advancements and achieved significant accomplishments across the entire field of dentistry. Notably, efforts to apply AI in prosthodon...
We conducted a literature search of PubMed, Scopus, Web of Science, Google Scholar, and IEEE Xplore databases from January 2010 to January 2024. The included articles addressed the application of AI i...
The initial electronic literature search yielded 393 records, which were reduced to 315 after eliminating duplicate references. The application of inclusion criteria led to analysis of 12 eligible pub...
AI has the potential to increase efficiency in processes such as fabricating and evaluating dental crowns, with a high level of accuracy reported in most of the analyzed studies. However, a significan...
A 3-D printing method to produce dental prostheses of complex shapes from a commercial, photocurable resin-ceramic slurry is developed and optimized. The microstructure, mechanical properties and wear...
This clinical report describes the process for fabricating a double-crown-retained removable dental prosthesis combining a fiber-reinforced composite and zirconia using digital technology. An 83-year-...
Digital technology offers many advantages such as efficient fabrication of double crowns, reduced material costs, improved biocompatibility, and good aesthetics of metal-free materials....
This clinical report describes the application of digital technology for the fabrication of a double-crown-retained removable dental prosthesis combining a fiber-reinforced composite and zirconia, res...
To investigate how different types of dental prosthesis perform in patients with head and neck tumors....
In this retrospective clinical cohort study, the impact of different patient-related factors was analyzed as influencing factors on the survival probability of dental prosthesis using Kaplan-Meier est...
Two hundred seventy-nine restorations were observed (mean observation: 2.7 ± 3.0 years, max.14.8 years) out of which 49 (17.6%) had to be replaced during the observation. After 5 years, 100% of group ...
Groups 1, 2, and 3 restorations showed good survival times after 5 years in function, whereas group 4 presented the worst survival times. Group 2 restorations showed the highest amount of necessary af...
The current investigation shows that groups 1, 2, and 3 restorations should be preferred in the prosthetic treatment planning of patients with head and neck tumors. A treatment with group 4 restoratio...
This study utilised an Artificial Intelligence (AI) method, namely 3D-Deep Convolutional Generative Adversarial Network (3D-DCGAN), which is one of the true 3D machine learning methods, as an automati...
Six hundred sets of digital casts containing mandibular second premolars and their adjacent and antagonist teeth obtained from healthy personnel were machine-learned using 3D-DCGAN. Additional 12 sets...
The 3D-DCGAN design and natural teeth had the lowest discrepancy in morphology compared with the other groups (root mean square value = 0.3611). The Biogeneric design showed a significantly (p < 0.05)...
This study demonstrated that 3D-DCGAN could be utilised to design personalised dental crowns with high accuracy that can mimic both the morphology and biomechanics of natural teeth....
This study's purpose is to assess the stress distribution in the peri-implant bone, implants, and prosthetic framework using two different posterior implant angles. All-on-four maxillary prostheses fa...
The bone conditions of mandibular bone vary from patient to patient, and as a result, a patient-specific dental implant needs to be designed. The basal dental implant is implanted in the cortical regi...
Correct choice of the implant design and the occlusal scheme is important for the success of implant supported restorations. So, the aim of the current study was to find out the difference in the stre...
Two finite element models of the maxilla, implants, and prostheses were designed according to the All-on-4® concept. In the model TP, two piece dental implants were placed while in the model OP one pi...
The highest stress value was recorded in the model TP with the group function occlusion and the lowest stress value was in the model OP with the canine guidance occlusion....
The one-piece dental implants can be concluded to induce less stress compared to the two piece dental implants when used in the All-on-4® implant supported prosthesis in the different lateral occlusal...
Computer-aided design/computer-aided manufacturing (CAD/CAM) technology transformed the world of restorative dentistry. The objectives were to assess pre-doctoral dental students' CAD/CAM-related educ...
A total of 358 pre-doctoral dental students from 17 of the 68 US dental schools responded to a web-based anonymous survey....
CAD/CAM-related classroom-based education was likely to happen in lectures (87.2%) and simulated exercises as part of a class (86.9%). Faculty were most likely to provide CAD/CAM instruction (87.9%), ...
The majority of students in the US dental schools appreciate CAD/CAM technology, consider it to be the future of dentistry, and believe it makes them better dentists. The fact that the majority is not...