Outlook: March 1996

Alumni publication of the Boston University School of Dental Medicine

Technology-Dependent Pedagogical Process Redesign: Leveraging Lean Methods

Purpose This research compared the efficacy of process outcomes leveraging lean methods versus traditional pedagogy applied to dental education dependent on emerging technology. The pedagogical objective was to improve system efficiency without compromising traditional outcomes of effectiveness (quality). Design/methodology/approach The research team tested the efficacy of a lean A3 framework to identify, remove waste and redesign a technology-dependent simulation laboratory course (CAD/CAM/IR Restorative Dentistry). Students were also sensitized to time-in-chair to introduce a stronger patient focus. Baseline data collected from a control group were statistically compared to the research group\u27s data after the course redesign. In addition, course time allocations were measured and then compared. Findings The results showed the interventions significantly reduced procedure cycle times without compromising quality. Additionally, the course was more efficiently conducted as measured by course time allocations. Practical implications This research demonstrated that the use of the A3 framework enhanced learning through process documentation, reengineering and systems optimization resolving issues of inefficiency associated with the CAD/CAM/IR pedagogy. This work is significant because it demonstrates the practice of using lean interventions to redesign and improve a technology-based healthcare course to maximize benefits. Originality/value This research is the first to examine how to leverage lean methods in a healthcare simulation laboratory, dependent on innovative technology, to educate and train future practitioners. This research applied statistical rigor in a controlled experiment to maximize its applicability and generalizability

Comparison of digital scanning and polyvinyl siloxane impression techniques by DMD students : instructional efficiency and attitudes toward technology.

The aim of this research was compare the ability of dental students to learn an digital scanner (3M LAVA) and material-based (PVS) impression techniques and to determine attitudes and preferences towards each. D2 dental students (n=25) were recruited and instructed on the use of 3M LAVA and PVS techniques using three pedagogic methods: 1) video lecture (P1), 2) investigator-led demonstration (P2), and 3) independent clinical exercise (P3). The amount of time for each pedagogic method was measured, averaged (± s.d.) and compared using Wilcoxon Signed-Ranks tests. A pre- and post-test was administered assessing their attitudes towards both techniques using a Likert scale and compared using dependent t-tests. Instructional time for 3M LAVA was higher for each pedagogic method (P1; 15.95 vs.10.07 min, p=0.0000: P2; 8.68 vs. 4.51 min, p=0.000: P3; 20.37 vs.14.17, p=0.000). Prior to instruction, students were more familiar with the PVS techniques (3.96 vs. 1.95, p=.0000) and expected both to be similar in difficulty (3.52 and 3.84, p=.106). After instruction, PVS techniques were considered easier to perform than expected (4.08, p=.002) with no change in perceived difficulty for 3M LAVA (3.56, p=.106). 96% of participants expected 3M LAVA to become their primary impression technique in their career

Craniofacial Growth Series Volume 56

https://deepblue.lib.umich.edu/bitstream/2027.42/153991/1/56th volume CF growth series FINAL 02262020.pdfDescription of 56th volume CF growth series FINAL 02262020.pdf : Proceedings of the 46th Annual Moyers Symposium and 44th Moyers Presymposiu

파노라마방사선영상을 이용한 딥러닝 기반의 데이터베이스 구축 및 개인식별 수행의 자동화

학위논문(박사) -- 서울대학교대학원 : 치과대학 치의과학과, 2022. 8. 허민석.2003년 2월에 발생한 대한민국 대구지하철 화재 참사 및 2011년 3월에 발생한 동일본 대지진 등 대형 참사가 발생하였을 때 희생자 개인식별은 법치의학적으로 매우 중요한 주제이다. 인체에서 강도가 높은 조직 중 하나인 치아를 개인식별에 사용하는 것은 잠재적인 후보자를 압축하고 개인식별의 정확도를 높이는데 도움이 된다. 치과 진료가 보편화되면서 한 개인이 평생 동안 한 장 이상의 파노라마방사선영상 기록을 남길 가능성이 매우 높아졌고, 평균 수명과 함께 구강위생수준이 증가함에 따라 잔존 치아수도 증가했다. 이로 인해 개인이 가지는 치과 치료 패턴은 더욱 다양해지고 개별화되어 한 개인을 나타내는 유일무이한 특징점이 될 수 있다. 본 연구에서는 파노라마방사선영상을 이용하여 딥러닝으로 사전 훈련된 객체인식 모델을 이용하여 치아의 변화를 인식 후 개인의 치열에 대한 데이터베이스를 자동으로 구축하고, 개인식별을 자동화하는 새로운 방법을 제안하고자 한다. 2000년 1월 1일부터 2020년 11월 30일까지 서울대학교치과병원에 진료목적으로 내원하여 최소 2장 이상의 파노라마 방사선사진을 촬영한 20-49세 환자 1,029명의 방사선사진 중 최근 및 과거 사진을 각각 사후(postmortem) 및 사전(antemortem) 영상으로 가정하여 쌍을 이루어 영상을 수집하였다. 상기 영상과 중복되지 않는 1,638장의 파노라마방사선영상으로 사전 훈련된 딥러닝 모델을 사용하여 총 2,058장의 영상에서 치아 번호 및 자연치, 보철물, 근관치료가 수행된 근관, 임플란트 정보를 자동으로 탐지하였다. 탐지된 정보는 최종적으로 6가지의 치아 상태를 결정할 수 있도록 수치화되었다. 6개의 상태는 자연치, 처치치(근관치료가 수행되지 않음), 처치치(근관치료가 수행됨), 발치, 가공치, 임플란트이다. 1,029명의 가장 최근 영상과 과거 영상이 각각 촬영된 시점 간 시간 기간을 일수로 계산하여 이 시간 간격에 따른 유사도 점수가 통계학적으로 유의한 차이를 보이는지 Student’s t-test을 시행하였다. 개인식별 시뮬레이션 과정은 미지의 사후 영상이 제시되었다고 가정하고 이 사후 영상의 치열을 기준으로 총 세 단계로 구성하였다. 첫번째 단계에서는 모든 개인의 사전 치열 정보를 상기 6가지 상태에 근거하여 데이터베이스로 구축하였다. 두번째 단계에서는 미지의 사후 영상과 기존 1,029명의 사전 영상과의 유사도 점수(similarity score)를 계산하였다. 최종 단계에서는 점수화된 유사도를 내림차순으로 정렬하여 상위 20.0%, 10.0%, 5.0% 후보군을 추출하여 미지의 사후 영상과 매칭되는 사전 영상의 순위를 측정한 뒤, 해당 순위의 백분율을 성공률(success rate)로 변환하였다. 또한, 성별군 안에서 각각 동일한 방식으로 성공률을 추가적으로 계산하였다. 한편, 유사도 점수가 통계학적으로 유의미한 차이를 보이는 cut-off value보다 촬영시점 간 기간이 적은 구간을 한정하여 상기 성공률을 동일한 방식으로 도출하였다. 본 연구에 참가한 개인의 성별 분포는 남성 465명(45.19%), 여성 564명(54.81%)이었고 평균 연령은 35.49±15.27세였다. 또한 최근 및 과거 파노라마 방사선사진 촬영시점 간 기간의 평균값은 2,197.5±1,934.7일이었다. 사전 훈련된 객체인식 딥러닝 모델의 성능은 자연치, 보철물, 근관치료가 시행된 근관, 임플란트에 대하여 평균 정밀도(average precision)가 각각 99.1%, 80.6%, 81.2%, 96.8%로 나타났다. 한편 평균 재현율(average recall)은 동일한 항목에 대하여 각각 99.6%, 84.3%, 89.2%, 98.1%로 나타났다. 촬영시점 간 기간에 따른 유사도 점수의 Student’s t-test 시행 결과 약 17.7년을 기점으로 통계학적으로 유의미한 차이를 나타내었다. 미지의 사후 영상에 대해 매칭된 사전 영상은 상위 20.0% 후보군을 추출하였을 때 83.2%의 성공률을 나타내었으며, 상위 10.0%, 5.0% 후보군을 추출하였을 경우에는 각각 72.1%, 59.4%의 성공률을 보였다. 성공률은 성별 간 차이를 나타내었는데, 남성의 경우 상위 20.0%, 10.0%, 5.0% 후보군 추출시 71.3%, 64.0%, 52.0%의 성공률을 보였고, 여성의 경우 같은 경우에 97.2%, 81.1%, 66.5%의 성공률을 보였다. 한편, 촬영시점 간 기간이 17.7년보다 짧은 구간에 한정하여 동일한 방식으로 성공률을 도출하였다. 상위 20.0% 후보군을 추출하였을 때 84.0%의 성공률을 나타내었으며, 상위 10.0%, 5.0% 후보군을 추출하였을 경우에는 각각 72.7%, 59.4%의 성공률을 보였다. 남성의 경우 상위 20.0%, 10.0%, 5.0% 후보군 추출시 71.3%, 63.6%, 51.8%의 성공률을 보였고, 여성의 경우 같은 경우에 97.8%, 81.8%, 66.9%의 성공률을 보였다. 본 연구에서는 개인의 치열 변화 가능성을 고려한 데이터베이스 검색 알고리즘이 개인식별을 수행하는데 있어서 효과적이며 딥러닝을 통한 자동화가 가능함을 확인하였다.Purpose: The aim of this study was to construct a database of individuals’ dentition automatically with dental panoramic radiographs (DPRs), and to propose a novel method to identify individuals by recognizing their dentition changes using a pretrained object detection network which was a convolutional neural network modified by EfficientDet-D3. The feasibility of this method was evaluated by simulating an automated human identification process. Materials and Methods: Among adults aged 20 to 49 years who took DPRs more than two times, recent and past images were assumed to be postmortem (PM) and antemortem (AM), respectively. The dataset contained a total of 2,058 paired DPRs per patient. The simulation algorithm was composed of three phases based on the dentition of unknown PMs. When constructing a database of AM dentition in phase 1, information on each individual’s teeth state was distinguished in six different states: natural teeth, treated teeth without canal filling, treated teeth with canal filling, missing teeth, pontics, and implants. In phase 2, the degree of similarity was calculated for every pair of 1,029 individuals. In the final phase 3, the scored similarities were sorted in descending order and a matched AM’s rank identical to an unknown PM was measured by extracting the top 20.0%, 10.0%, and 5.0% candidate groups. Finally, the percentage of that rank was calculated as the success rate. Additionally, the values of similarity score were compared to analyze whether the similarity scores according to the imaging time interval showed a statistically significant difference. Results: The similarity showed a statistically significant difference between the two groups based on the period between the date of the most recent DPR and that of the past DPR imaging at 17.7 years. The matched AM was ranked in the candidate group with a success rate of 83.2%, 72.1%, and 59.4% in the entire imaging time interval for extraction of the top 20.0%, 10.0%, and 5.0% candidate group, respectively. On the other hand, the success rate in a group with less than 6,450 days of imaging time interval was 84.0%, 72.7%, and 59.4% for same order, respectively. The success rate was dependent upon the sex. The success rate of the top 20.0%, 10.0% and 5.0% candidate groups in the entire imaging time interval was 71.3%, 64.0% and 52.0%, respectively, among the male subjects, while that of the same candidate groups was 97.2%, 81.1% and 66.5%, respectively, among the female subjects. In the imaging time interval of fewer than 6,450 days, the success rate of the top 20.0%, 10.0% and 5.0% candidate groups was 71.3%, 63.6% and 51.8%, respectively, among the male subjects, while that of the same candidate groups was 97.8%, 81.8% and 66.9%, respectively, among the female subjects. Conclusion: In the forensic human identification process, the developed method was useful for dental professionals, effectively to reduce the size of the AM candidate group to be reviewed. If a large database would be constructed by adding various conditions other than teeth information, the accuracy of human identification would be improved even further.Introduction 1 Literature Review 4 Material and Methods 7 Results 17 Discussion 28 Conclusion 39 References 40 Abstract in Korean 48박

Dental Education

The dental curriculum is like a living organism—it has developed through time, manifesting regional, cultural, and scientific heritage, and reflecting modern trends. The undergraduate dental curriculum is periodically rebuilt to ensure the harmonization of higher education systems between countries, especially in Europe. Structure, content, learning, and assessment in undergraduate and postgraduate dental education and auxiliary dental personnel training are shaped based on professional consensus. Constant updates on recent technological innovations and evidence-based best practice are necessary.In modern times, ethical issues are raised more than ever. Can we teach our students how to be dedicated health professionals and manage a successful practice at the same time? Does the commercialization of our profession also affect the dental curriculum today?The COVID-19 pandemic has imposed new challenges, moving us from lecture rooms and clinics to an online environment.This Special Issue is dedicated to developing the understanding of dental education

Dental Students’ Perceived Preparedness to Treat Patients in Clinic After a Fixed Prosthodontics Course: Survey Results of a Case Study

Previous research regarding dental students has found modest predictive value in preclinical didactic course grades in predicting clinical performance, but systematic assessment of students' feedback on their perceived preparedness has received little attention as a preclinical assessment methodology. The aim of this study was to assess the perceptions of the dental students at one U.S. academic dental institution regarding their preparedness for clinical performance following the preclinical fixed prosthodontics course. Third- and fourth-year dental students participated in a survey about their perceived preparedness to diagnose and treat patients with fixed prosthodontics needs in the school's dental clinics. The respondents (79 out of 161 students, for a response rate of 49%) rated each item on a five-point Likert scale. Responses about which preclinical procedures of the course prepared students the least and the best were consistent for the third- and fourth-year students. Less than 60% of all responding students felt prepared for planning complex cases and performing laboratory-related procedures. The findings of this study indicate that improvement is required in teaching students about laboratory procedures and problem-solving to adequately prepare them for clinical treatment of patients with fixed prosthodontics needs

Effectiveness of CAD-CAM Application for the Development design and implementation of maintenance tools

This study aims to evaluate the effectiveness of using Computer-Aided Design and Computer-Aided Manufacturing (CAD-CAM) applications in developing the design and implementation of maintenance tools. The use of CAD-CAM has become a major trend in the modern manufacturing industry because it provides various advantages such as time efficiency, high precision and increased productivity. However, it is important to assess the true effectiveness of this technology in the context of maintenance tool development to fully understand its potential benefits. The literature review analysis method was used to compile an in-depth review of the latest research and publications related to the use of CAD-CAM in the design development and implementation of maintenance tools. A number of case studies and field experiments were also included in the analysis to provide further insight into the application of this technology in various industrial environments. The results of the analysis show that the use of CAD-CAM in the development of maintenance tool designs has brought significant positive changes. This application is able to reduce development cycle time, enable model-based engineering, and improve modeling accuracy. Apart from that, CAD-CAM also facilitates better collaboration between design teams, engineers and other stakeholders, which contributes to improving the quality of the final product. However, despite the many benefits offered by CAD-CAM, there are also challenges that need to be overcome to increase the effectiveness of its use. Some of these are high initial investment costs, the need for higher technical skills, and complex integration with existing infrastructure. In conclusion, CAD-CAM has proven effective in developing the design and implementation of maintenance tools. With this technology, companies can increase operational efficiency, improve product quality, and gain a competitive advantage in the market. By understanding the benefits and challenges of its use, professionals can more effectively adopt CAD-CAM and fully exploit its potential in the modern manufacturing industry

Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs

Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early; the treatment will be relatively easy; which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However; the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu’s threshold image enhancement technology; this research solves the problem that the original cutting technology cannot extract certain single teeth; and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN); which can identify caries and restorations from the bitewing images. Moreover; it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image; which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization; (2) a dental image cropping procedure to obtain individually separated tooth samples; and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks; namely; AlexNet; GoogleNet; Vgg19; and ResNet50; experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%; respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film