Decision support systems for adoption in dental clinics: a survey

While most dental clinicians use some sort of information system, they are involved with administrative functions, despite the advisory potential of some of these systems. This paper outlines some current decision support systems (DSS) and the common barriers facing dentists in adopting them within their workflow. These barriers include lack of perceived usefulness, complicated social and economic factors, and the difficulty for users to interpret the advice given by the system. A survey of current systems found that although there are systems that suggest treatment options, there is no real-time integration with other knowledge bases. Additionally, advice on drug prescription at point-of-care is absent from such systems, which is a significant omission, in consideration of the fact that disease management and drug prescription are common in the workflow of a dentist. This paper also addresses future trends in the research and development of dental clinical DSS, with specific emphasis on big data, standards and privacy issues to fulfil the vision of a robust, user-friendly and scalable personalised DSS for dentists. The findings of this study will offer strategies in design, research and development of a DSS with sufficient perceived usefulness to attract adoption and integration by dentists within their routine clinical workflow, thus resulting in better health outcomes for patients and increased productivity for the clinic

Odontology & artificial intelligence

Neste trabalho avaliam-se os três fatores que fizeram da inteligência artificial uma tecnologia essencial hoje em dia, nomeadamente para a odontologia: o desempenho do computador, Big Data e avanços algorítmicos. Esta revisão da literatura avaliou todos os artigos publicados na PubMed até Abril de 2019 sobre inteligência artificial e odontologia. Ajudado com inteligência artificial, este artigo analisou 1511 artigos. Uma árvore de decisão (If/Then) foi executada para selecionar os artigos mais relevantes (217), e um algoritmo de cluster k-means para resumir e identificar oportunidades de inovação. O autor discute os artigos mais interessantes revistos e compara o que foi feito em inovação durante o International Dentistry Show, 2019 em Colónia. Concluiu, assim, de forma crítica que há uma lacuna entre tecnologia e aplicação clínica desta, sendo que a inteligência artificial fornecida pela indústria de hoje pode ser considerada um atraso para o clínico de amanhã, indicando-se um possível rumo para a aplicação clínica da inteligência artificial.There are three factors that have made artificial intelligence (AI) an essential technology today: the computer performance, Big Data and algorithmic advances. This study reviews the literature on AI and Odontology based on articles retrieved from PubMed. With the help of AI, this article analyses a large number of articles (a total of 1511). A decision tree (If/Then) was run to select the 217 most relevant articles-. Ak-means cluster algorithm was then used to summarize and identify innovation opportunities. The author discusses the most interesting articles on AI research and compares them to the innovation presented during the International Dentistry Show 2019 in Cologne. Three technologies available now are evaluated and three suggested options are been developed. The author concludes that AI provided by the industry today is a hold-up for the praticioner of tomorrow. The author gives his opinion on how to use AI for the profit of patients

Predicting dental student\u27s performance on national board dental examination (NBDE) part I: exploring demographic factors, dental admission test (DAT) factors, pre-program academic factors, and dental program academic performance.

Dental students need to successfully challenge a national licensure examination to be able to practice dentistry. Dental educators currently have difficulty in identifying candidates who are at risk of failing this examination. This non-experimental quantitative study examined existing dental student data from the 2017, 2018, and 2019 graduating classes, using a retrospective and correlational approach to identify possible markers for at risk students. Demographic factors, dental admission test (DAT) factors, pre-program academic factors, dental program academic performance, and National Board Dental Examination (NBDE) Part I performance. A series of independent t-tests and One-Way analysis of variances (ANOVAs) were used to examine the students’ performances regarding their gender and race. Logistic regression models were used to predict NBDE Part I performance at the first attempt from each categorical (demographic factor) and continuous predictor (pre-program academic performance, DAT performance, and dental program academic Performance). Gender and race were significantly associated with the student’s academic achievement at the undergraduate level and the DAT; however, the influence of these factors diminished in the dental program academic performance and the NBDE Part I. Student’s dental program performance were significantly associated with the NBDE Part I outcomes. Within the limitations of this study, dental students with different gender and race backgrounds all have the potential to successfully complete NBDE. Additional enrichment and bridge programs for the underrepresented minorities students may be used to maximize the future success of the enrolled diverse student body. The dental program can have performance benchmarks starting at program admission and continuing through the end of the second year to help identify at-risk students early and provide them with additional academic support

ImplantFormer: Vision Transformer based Implant Position Regression Using Dental CBCT Data

Implant prosthesis is the most appropriate treatment for dentition defect or dentition loss, which usually involves a surgical guide design process to decide the implant position. However, such design heavily relies on the subjective experiences of dentists. In this paper, a transformer-based Implant Position Regression Network, ImplantFormer, is proposed to automatically predict the implant position based on the oral CBCT data. We creatively propose to predict the implant position using the 2D axial view of the tooth crown area and fit a centerline of the implant to obtain the actual implant position at the tooth root. Convolutional stem and decoder are designed to coarsely extract image features before the operation of patch embedding and integrate multi-level feature maps for robust prediction, respectively. As both long-range relationship and local features are involved, our approach can better represent global information and achieves better location performance. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed ImplantFormer achieves superior performance than existing methods

The application of virtual reality and augmented reality in oral & maxillofacial surgery

Background: Virtual reality is the science of creating a virtual environment for the assessment of various anatomical regions of the body for the diagnosis, planning and surgical training. Augmented reality is the superimposition of a 3D real environment specific to individual patient onto the surgical filed using semi-transparent glasses to augment the virtual scene.. The aim of this study is to provide an over view of the literature on the application of virtual and augmented reality in oral & maxillofacial surgery. Methods: We reviewed the literature and the existing database using Ovid MEDLINE search, Cochran Library and PubMed. All the studies in the English literature in the last 10 years, from 2009 to 2019 were included. Results: We identified 101 articles related the broad application of virtual reality in oral & maxillofacial surgery. These included the following: Eight systematic reviews, 4 expert reviews, 9 case reports, 5 retrospective surveys, 2 historical perspectives, 13 manuscripts on virtual education and training, 5 on haptic technology, 4 on augmented reality, 10 on image fusion, 41 articles on the prediction planning for orthognathic surgery and maxillofacial reconstruction. Dental implantology and orthognathic surgery are the most frequent applications of virtual reality and augmented reality. Virtual planning improved the accuracy of inserting dental implants using either a statistic guidance or dynamic navigation. In orthognathic surgery, prediction planning and intraoperative navigation are the main applications of virtual reality. Virtual reality has been utilised to improve the delivery of education and the quality of training in oral & maxillofacial surgery by creating a virtual environment of the surgical procedure. Haptic feedback provided an additional immersive reality to improve manual dexterity and improve clinical training. Conclusion: Virtual and augmented reality have contributed to the planning of maxillofacial procedures and surgery training. Few articles highlighted the importance of this technology in improving the quality of patients’ care. There are limited prospective randomized studies comparing the impact of virtual reality with the standard methods in delivering oral surgery education

Situation Interpretation for Knowledge- and Model Based Laparoscopic Surgery

To manage the influx of information into surgical practice, new man-machine interaction methods are necessary to prevent information overflow. This work presents an approach to automatically segment surgeries into phases and select the most appropriate pieces of information for the current situation. This way, assistance systems can adopt themselves to the needs of the surgeon and not the other way around

Application of artificial intelligence in the dental field : A literature review

Purpose: The purpose of this study was to comprehensively review the literature regarding the application of artificial intelligence (AI) in the dental field, focusing on the evaluation criteria and architecture types. Study selection: Electronic databases (PubMed, Cochrane Library, Scopus) were searched. Full-text articles describing the clinical application of AI for the detection, diagnosis, and treatment of lesions and the AI method/architecture were included. Results: The primary search presented 422 studies from 1996 to 2019, and 58 studies were finally selected. Regarding the year of publication, the oldest study, which was reported in 1996, focused on “oral and maxillofacial surgery.” Machine-learning architectures were employed in the selected studies, while approximately half of them (29/58) employed neural networks. Regarding the evaluation criteria, eight studies compared the results obtained by AI with the diagnoses formulated by dentists, while several studies compared two or more architectures in terms of performance. The following parameters were employed for evaluating the AI performance: accuracy, sensitivity, specificity, mean absolute error, root mean squared error, and area under the receiver operating characteristic curve. Conclusion: Application of AI in the dental field has progressed; however, the criteria for evaluating the efficacy of AI have not been clarified. It is necessary to obtain better quality data for machine learning to achieve the effective diagnosis of lesions and suitable treatment planning

Two-Stream Regression Network for Dental Implant Position Prediction

In implant prosthesis treatment, the design of surgical guide requires lots of manual labors and is prone to subjective variations. When deep learning based methods has started to be applied to address this problem, the space between teeth are various and some of them might present similar texture characteristic with the actual implant region. Both problems make a big challenge for the implant position prediction. In this paper, we develop a two-stream implant position regression framework (TSIPR), which consists of an implant region detector (IRD) and a multi-scale patch embedding regression network (MSPENet), to address this issue. For the training of IRD, we extend the original annotation to provide additional supervisory information, which contains much more rich characteristic and do not introduce extra labeling costs. A multi-scale patch embedding module is designed for the MSPENet to adaptively extract features from the images with various tooth spacing. The global-local feature interaction block is designed to build the encoder of MSPENet, which combines the transformer and convolution for enriched feature representation. During inference, the RoI mask extracted from the IRD is used to refine the prediction results of the MSPENet. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TSIPR achieves superior performance than existing methods