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Generative deep learning approaches for the design of dental restorations: A narrative review
(2024)
Objectives:
This study aims to explore and discuss recent advancements in tooth reconstruction utilizing deep learning (DL) techniques. A review on new DL methodologies in partial and full tooth reconstruction is conducted.
Data/Sources:
PubMed, Google Scholar, and IEEE Xplore databases were searched for articles from 2003 to 2023.
Study selection:
The review includes 9 articles published from 2018 to 2023. The selected articles showcase novel DL approaches for tooth reconstruction, while those concentrating solely on the application or review of DL methods are excluded. The review shows that data is acquired via intraoral scans or laboratory scans of dental plaster models. Common data representations are depth maps, point clouds, and voxelized point clouds. Reconstructions focus on single teeth, using data from adjacent teeth or the entire jaw. Some articles include antagonist teeth data and features like occlusal grooves and gap distance. Primary network architectures include Generative Adversarial Networks (GANs) and Transformers. Compared to conventional digital methods, DL-based tooth reconstruction reports error rates approximately two times lower.
Conclusions:
Generative DL models analyze dental datasets to reconstruct missing teeth by extracting insights into patterns and structures. Through specialized application, these models reconstruct morphologically and functionally sound dental structures, leveraging information from the existing teeth. The reported advancements facilitate the feasibility of DL-based dental crown reconstruction. Beyond GANs and Transformers with point clouds or voxels, recent studies indicate promising outcomes with diffusion-based architectures and innovative data representations like wavelets for 3D shape completion and inference problems.
Clinical significance:
Generative network architectures employed in the analysis and reconstruction of dental structures demonstrate notable proficiency. The enhanced accuracy and efficiency of DL-based frameworks hold the potential to enhance clinical outcomes and increase patient satisfaction. The reduced reconstruction times and diminished requirement for manual intervention may lead to cost savings and improved accessibility of dental services.
Case study research is one of the most widely used research methods in Information Systems (IS). In recent years, an increasing number of publications have used case studies with few sources of evidence, such as single interviews per case. While there is much methodological guidance on rigorously conducting multiple case studies, it remains unclear how researchers can achieve an acceptable level of rigour for this emerging type of multiple case study with few sources of evidence, i.e., multiple mini case studies. In this context, we synthesise methodological guidance for multiple case study research from a cross-disciplinary perspective to develop an analytical framework. Furthermore, we calibrate this analytical framework to multiple mini case studies by reviewing previous IS publications that use multiple mini case studies to provide guidelines to conduct multiple mini case studies rigorously. We also offer a conceptual definition of multiple mini case studies, distinguish them from other research approaches, and position multiple mini case studies as a pragmatic and rigorous approach to research emerging and innovative phenomena in IS.
Academic self-regulation is a key factor for motivation and learning achievement. Yet with the large range of individual factors, this is not a one-size-fits-all proposition. This study of L2 Technical English students at two German universities explored learners’ expectations and motivations, in particular regarding self-regulation and self-efficacy via the individual’s time investment in self-led study. In an initial survey, learners (N=1646) reported on their English skill levels and anticipated learning habits. Complementarily, the retrospective survey investigated learners’ (N=796) actual behavior during the course, their perceptions of language skill improvement, and their satisfaction. The initial survey indicates a clear understanding that time investment in self-regulated study will lead to greater improvement, an outcome confirmed in the retrospective survey. Additionally, students who invested more time in their coursework were more satisfied with their achievement, although most learners acknowledge they should have studied more. The results verify that learners recognize the nexus between self-regulation and language skill improvement, yet university students are not satisfied with their capacity to self-regulate their language learning strategies. While differences in students’ skill levels and academic self-efficacy result in divergent degrees of progress, students of all types report benefits to their language skills when motivated to self-regulated study.
Effect of AI on performance of endoscopists to detect Barrett neoplasia: A Randomized Tandem Trial
()
Background and study aims
To evaluate the effect of an AI-based clinical decision support system (AI) on the performance and diagnostic confidence of endoscopists during the assessment of Barrett's esophagus (BE).
Patients and Methods
Ninety-six standardized endoscopy videos were assessed by 22 endoscopists from 12 different centers with varying degrees of BE experience.
The assessment was randomized into two video sets: Group A (review first without AI and second with AI) and group B (review first with AI and second without AI). Endoscopists were required to evaluate each video for the presence of Barrett's esophagus-related neoplasia (BERN) and then decide on a spot for a targeted biopsy. After the second assessment, they were allowed to change their clinical decision and confidence level.
Results
AI had a standalone sensitivity, specificity, and accuracy of 92.2%, 68.9%, and 81.6%, respectively. Without AI, BE experts had an overall sensitivity, specificity, and accuracy of 83.3%, 58.1 and 71.5%, respectively. With AI, BE nonexperts showed a significant improvement in sensitivity and specificity when videos were assessed a second time with AI (sensitivity 69.7% (95% CI, 65.2% - 74.2%) to 78.0% (95% CI, 74.0% - 82.0%); specificity 67.3% (95% CI, 62.5% - 72.2%) to 72.7% (95 CI, 68.2% - 77.3%). In addition, the diagnostic confidence of BE nonexperts improved significantly with AI.
Conclusion
BE nonexperts benefitted significantly from the additional AI. BE experts and nonexperts remained below the standalone performance of AI, suggesting that there may be other factors influencing endoscopists to follow or discard AI advice.
We investigate the influence of the geometry and doping level on the performance of n-type silicon nanowire field emitters on silicon pillar structures. Therefore, multiple cathodes with 50 by 50 pillar arrays (diameter: 5 μm, height: 30 μm, spacing: 50 μm) were fabricated and measured in diode configuration. In the first experiment, we compared two geometry types using the same material. Geometry 1 is black silicon, which is a highly dense surface covering a forest of tightly spaced silicon needles resulting from self-masking during a plasma etching process of single crystal silicon. Geometry 2 are silicon nanowires, which are individual spaced-out nanowires in a crownlike shape resulting from a plasma etching process of single crystal silicon. In the second experiment, we compared two different silicon doping levels [n-type (P), 1–10 and <0.005 Ω cm] for the same geometry. The best performance was achieved with lower doped silicon nanowire samples, emitting 2 mA at an extraction voltage of 1 kV. The geometry/material combination with the best performance was used to assemble an integrated electron source. These electron sources were measured in a triode configuration and reached onset voltages of about 125 V and emission currents of 2.5 mA at extraction voltages of 400 V, while achieving electron transmission rates as high as 85.0%.
Increasingly stringent emission limits have made particulate filters necessary for gasoline engines. Similar to diesel applications, gasoline particulate filters (GPFs) can be monitored by differential pressure measurement or by the radio-frequency-based filter diagnosis (RF sensor). In addition to measuring the soot loading, ash detection is critical for monitoring the GPF over the entire vehicle lifetime. Because the RF sensor detects the filter loading through a change in the dielectric properties of the GPF, it can detect not only soot but also ash. In diesel applications, the RF sensor has already demonstrated its potential for ash detection. To verify the feasibility of simultaneous ash and soot monitoring for GPFs, filters were loaded with ash on an engine test bench and measured on a lab test bench under defined synthetic exhaust gas conditions. By evaluating resonant modes, soot and ash could be clearly distinguished, as ash mainly affects the resonant frequency, while soot also changes the quality factor due to its high dielectric losses. However, higher soot loadings could not be detected by the resonant parameters, but instead by a frequency-averaged transmission signal. While the presence of ash caused an offset in this signal, its sensitivity to soot was not affected. Thus, the influence of ash can be corrected if the signal in the soot-free filter state is known, e.g., from the behavior of the resonant parameters. Therefore, even with a continuously increasing ash loading over the lifetime of a vehicle, an accurate soot detection is possible with the RF sensor.
In fatigue strength assessment, the methods based on ideal elastic stresses according to Basquin and the less established method based on elastic-plastic stress quantities according to Manson, Coffin and Morrow are applied. The former calculates loads using linear-elastic stresses, the latter requires elasticplastic evaluation parameters, such as stresses and strains. These can be determined by finite element analysis (FEA) with a linear-elastic constitutive law, and subsequent conversion to elastic-plastic loads, using the macro support formula by Neuber. In this contribution, an alternative approach to approximate elastic-plastic parameters proposed by Glinka is compared to the the strain-life method using Neuber's formula, as well as the stress-life method of Basquin. Several component tests on 42CrMoS4-QT specimens are investigated. To determine the input data for the fatigue strength evaluations, the entire test setup is computed by FEA. The nodal displacements from these validated full-model simulations are used as boundary conditions for a submodel simulation of a notch, whose results serve as input for the fatigue strength assessments. It is shown that all approaches provide a reliable assessment of components. Our key result is that the strain-life method using the concept by Glinka for notch stress computation, yields improved results in fatigue strength assessments.
Commercially available contact angle (CA) measuring devices usually do not allow for the application of magnetic fields to the sample under test. A setup for measuring the CA of liquids on magnetosensitive surfaces has been developed specifically for investigating the surfaces of magnetoactive elastomers (MAEs). The addition of a programmable linear stage, which moves a permanent magnet, allows for fine control of the magnetic field applied to the MAE without the need for large and power-consuming electromagnets. Paired with a custom control and evaluation software, this measurement setup operates semiautomatically, limiting operator error and increasing precision, speed, as well as repeatability of static and dynamic CA measurements for different magnetoactive materials. The software is equipped with robust droplet fitting algorithms to avoid experimental challenges arising with soft magnetoactive materials, such as the curling of sample edges or diffuse non-reflective surfaces. Several application examples on MAE surfaces, both processed and unprocessed, are presented.
Eine Herausforderung performancebasierter Prüfverfahren zur Bewertung der Dauerhaftigkeit ist die Beschleunigung natürlicher Mechanismen. Dies ist notwendig, um innerhalb kurzer Zeit die Langzeit-Leistungsfähigkeit ermitteln zu können. Die Bestimmung der Frost-Tausalzbeständigkeit von Beton kann durch den CDF-Test erfolgen, welcher für Betone mit ausreichendem Frost-Tausalz-Widerstand sehr gut funktioniert. Bei Proben mit einer unzureichenden bzw. unbekannten Performance können erhöhte Randabwitterungen auftreten, welche durch das Ablösen des seitlichen Abdichtbands entstehen. Der zunehmende Randeinfluss führt so zu einer Verfälschung der Ergebnisse und zu einer Unterschätzung der tatsächlichen Performance des Bauteils. In diesem Beitrag werden Mörtel- und Betonprobekörper mit unterschiedlichen Abwitterungsraten in Anlehnung an das CDF-Verfahren untersucht. Zusätzlich wird die Oberflächenschädigung der Proben anhand einer neuartigen Messmethode mittels hochauflösendem 3D-Laserscanning ausgewertet. Die Ergebnisse zeigen, dass die Randeffekte mit der Laserscan-Methode umgangen und vergleichbare Ergebnisse zu den CDF-Untersuchungen erzielt werden können. Somit können Betone ohne Randeinflüsse charakterisiert und eine präzise Prognose der Langzeitbeständigkeit getroffen werden.
Die Carbonatisierung von Beton kann einerseits zu Schäden bei Stahlbetonbauwerken führen, andererseits kann der Beton so CO2 speichern und damit seinen CO2-Fußabdruck verringern. In diesem Beitrag wird dargestellt, wie eine effiziente Beschleunigung der Carbonatisierung durch Anlegen eines geringen äußeren Drucks erreicht werden kann. Damit wird neben der Diffusion der wesentlich schneller ablaufende Transportmechanismus der Permeation ausgenutzt, der das CO2 tiefer in das Probeninnere transportiert und damit in kurzer Zeit die Bestimmung des Carbonatisierungswiderstand zementgebundener Materialien ermöglicht. Hierzu wurde eine Prüfeinrichtung entwickelt, die es ermöglicht, die CO2-Konzentration und in gewissen Grenzen auch relative Luftfeuchte sowie die Temperatur gezielt einzustellen und zu steuern, um so Mörtel- und Betonproben schnell und präzise zu carbonatisieren. Der Einfluss verschiedener Druckstufen und Wechselzyklen wird dargelegt und die resultierenden chemischen und physikalischen Veränderungen der Proben bestimmt. Als besonders effizient und gleichzeitig realitätsnah erwies sich die konstante CO2-Beaufschlagung mit 3 Vol.-% CO2 mit zusätzlichem, moderatem Gasdruck. Das hier entwickelte Verfahren kann daher genutzt werden, um Betone schnell und effizient in zeitraffenden Materialtests hinsichtlich Carbonatisierungswiderstand zu charakterisieren.