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A novel method for controlling the rebound behavior of small balls made of Al2O3 with a radius of 2.381 mm is presented. It uses different types of micro-structured surfaces of soft magnetoactive elastomers. These surfaces were fabricated via laser micromachining and include fully ablated surfaces as well as micrometer-sized lamellas with a fixed width of 90 µm, height of 250 µm and three different gap sizes (15, 60 and 105 µm). The lamellas can change their orientation from edge-on to face-on configuration according to the direction of the external magnetic field from a permanent magnet. The orientation of the external magnetic field significantly influences the rebound behavior of the balls, from a coefficient of restitution e of to < 0.1. The highest relative change in the coefficient of restitution between zero field and face-on configuration of is observed for lamellas with a gap of 60 µm. Other characteristics of the ball rebound such as the penetration depth into an Magnetoactive elastomer and the maximum deceleration are investigated as well. The proposed method does not require a constant power supply due to the use of permanent magnets. It may find novel applications in the field of impact engineering.
Im Betrieb von Förderbändern auftretende Phänomene sind das Bandwandern in Achsrichtung der Walzen und die Bandverformung in der Bandebene [1, S. 142]. Das Bandwandern ist bisher detailliert untersucht [2–4], während die Bandverformung Gegenstand weniger Veröffentlichungen ist. Deshalb wird in diesem Artikel eine Methode zur Berechnung der sich im dynamischen Betrieb einstellenden Verformung eines elastischen Förderbandes vorgestellt. Dazu wird ein Mehrkörpersimulations-Modell basierend auf der Lumped-Mass-Modellierung verwendet. Als Untersuchungsbeispiel dient ein Zwei-Walzensystem mit flexiblen und zylindrischen Walzen, sowie ein flexibles Förderband. Es zeigt sich, dass mit der Lumped-Mass-Modellierung eine dynamische Bandverformung, die von der gewählten Diskretisierung abhängt, berechnet werden kann. Aufgrund dieser Abhängigkeit ist es notwendig eine Konvergenzanalyse durchzuführen. Zusätzlich ist darauf zu achten, dass die künstliche Anregung aufgrund der Lumped-Mass-Modellierung nicht mit einer Eigenfrequenz des Modells zusammenfällt.
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.
Background:
With the prevalence of burnout among surgeons posing a significant threat to healthcare outcomes, the mental toughness of medical professionals has come to the fore. Mental toughness is pivotal for surgical performance and patient safety, yet research into its dynamics within a global and multi-specialty context remains scarce. This study aims to elucidate the factors contributing to mental toughness among surgeons and to understand how it correlates with surgical outcomes and personal well-being.
Methods:
Utilizing a cross-sectional design, this study surveyed 104 surgeons from English and German-speaking countries using the Mental Toughness Questionnaire (MTQ-18) along with additional queries about their surgical practice and general life satisfaction. Descriptive and inferential statistical analyses were applied to investigate the variations in mental toughness across different surgical domains and its correlation with professional and personal factors.
Results:
The study found a statistically significant higher level of mental toughness in micro-surgeons compared to macro-surgeons and a positive correlation between mental toughness and surgeons' intent to continue their careers. A strong association was also observed between general life satisfaction and mental toughness. No significant correlations were found between the application of psychological skills and mental toughness.
Conclusion:
Mental toughness varies significantly among surgeons from different specialties and is influenced by professional dedication and personal life satisfaction. These findings suggest the need for targeted interventions to foster mental toughness in the surgical community, potentially enhancing surgical performance and reducing burnout. Future research should continue to explore these correlations, with an emphasis on longitudinal data and the development of resilience-building programs.
Digital Twins (DT) implementation in the Built Environment (BE) industry is still in its early stages. Aiming to increase the knowledge about DT, this study analyzes how DT can be understood in the BE sector and investigates its different potential benefits and expected challenges. To do so, the Systematic Literature Review (SLR) approach was employed. Using 228 publications, the current study presents a proposed definition and structure for DT systems. The proposed structure is based on four main layers: physical, digital, application, and user layers. The study also classified the applications of DT into six groups: sustainability and environmental, facility management, safety, health, and risk management, structural performance, construction management, and architectural and urban-related applications. The challenges of DT implementation were also grouped based on industry-related, social and organizational, economic, technological, and political and legal challenges. Based on the results, future research directions and practical recommendations were presented to support the successful deployment of the technology.
Background
This study evaluated the effect of an artificial intelligence (AI)-based clinical decision support system on the performance and diagnostic confidence of endoscopists in their assessment of Barrett’s esophagus (BE).
Methods
96 standardized endoscopy videos were assessed by 22 endoscopists with varying degrees of BE experience from 12 centers. 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 stand-alone sensitivity, specificity, and accuracy of 92.2%, 68.9%, and 81.3%, 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 69.8% [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 additional AI. BE experts and nonexperts remained significantly below the stand-alone performance of AI, suggesting that there may be other factors influencing endoscopists’ decisions to follow or discard AI advice.
Aims
Recent evidence suggests the possibility of intraprocedural phase recognition in surgical operations as well as endoscopic interventions such as peroral endoscopic myotomy and endoscopic submucosal dissection (ESD) by AI-algorithms. The intricate measurement of intraprocedural phase distribution may deepen the understanding of the procedure. Furthermore, real-time quality assessment as well as automation of reporting may become possible. Therefore, we aimed to develop an AI-algorithm for intraprocedural phase recognition during ESD.
Methods
A training dataset of 364385 single images from 9 full-length ESD videos was compiled. Each frame was classified into one procedural phase. Phases included scope manipulation, marking, injection, application of electrical current and bleeding. Allocation of each frame was only possible to one category. This training dataset was used to train a Video Swin transformer to recognize the phases. Temporal information was included via logarithmic frame sampling. Validation was performed using two separate ESD videos with 29801 single frames.
Results
The validation yielded sensitivities of 97.81%, 97.83%, 95.53%, 85.01% and 87.55% for scope manipulation, marking, injection, electric application and bleeding, respectively. Specificities of 77.78%, 90.91%, 95.91%, 93.65% and 84.76% were measured for the same parameters.
Conclusions
The developed algorithm was able to classify full-length ESD videos on a frame-by-frame basis into the predefined classes with high sensitivities and specificities. Future research will aim at the development of quality metrics based on single-operator phase distribution.
Aims
While AI has been successfully implemented in detecting and characterizing colonic polyps, its role in therapeutic endoscopy remains to be elucidated. Especially third space endoscopy procedures like ESD and peroral endoscopic myotomy (POEM) pose a technical challenge and the risk of operator-dependent complications like intraprocedural bleeding and perforation. Therefore, we aimed at developing an AI-algorithm for intraprocedural real time vessel detection during ESD and POEM.
Methods
A training dataset consisting of 5470 annotated still images from 59 full-length videos (47 ESD, 12 POEM) and 179681 unlabeled images was used to train a DeepLabV3+neural network with the ECMT semi-supervised learning method. Evaluation for vessel detection rate (VDR) and time (VDT) of 19 endoscopists with and without AI-support was performed using a testing dataset of 101 standardized video clips with 200 predefined blood vessels. Endoscopists were stratified into trainees and experts in third space endoscopy.
Results
The AI algorithm had a mean VDR of 93.5% and a median VDT of 0.32 seconds. AI support was associated with a statistically significant increase in VDR from 54.9% to 73.0% and from 59.0% to 74.1% for trainees and experts, respectively. VDT significantly decreased from 7.21 sec to 5.09 sec for trainees and from 6.10 sec to 5.38 sec for experts in the AI-support group. False positive (FP) readings occurred in 4.5% of frames. FP structures were detected significantly shorter than true positives (0.71 sec vs. 5.99 sec).
Conclusions
AI improved VDR and VDT of trainees and experts in third space endoscopy and may reduce performance variability during training. Further research is needed to evaluate the clinical impact of this new technology.
Aims
Artificial Intelligence (AI) systems in gastrointestinal endoscopy are narrow because they are trained to solve only one specific task. Unlike Narrow-AI, general AI systems may be able to solve multiple and unrelated tasks. We aimed to understand whether an AI system trained to detect, characterize, and segment early Barrett’s neoplasia (Barrett’s AI) is only capable of detecting this pathology or can also detect and segment other diseases like early squamous cell cancer (SCC).
Methods
120 white light (WL) and narrow-band endoscopic images (NBI) from 60 patients (1 WL and 1 NBI image per patient) were extracted from the endoscopic database of the University Hospital Augsburg. Images were annotated by three expert endoscopists with extensive experience in the diagnosis and endoscopic resection of early esophageal neoplasias. An AI system based on DeepLabV3+architecture dedicated to early Barrett’s neoplasia was tested on these images. The AI system was neither trained with SCC images nor had it seen the test images prior to evaluation. The overlap between the three expert annotations („expert-agreement“) was the ground truth for evaluating AI performance.
Results
Barrett’s AI detected early SCC with a mean intersection over reference (IoR) of 92% when at least 1 pixel of the AI prediction overlapped with the expert-agreement. When the threshold was increased to 5%, 10%, and 20% overlap with the expert-agreement, the IoR was 88%, 85% and 82%, respectively. The mean Intersection Over Union (IoU) – a metric according to segmentation quality between the AI prediction and the expert-agreement – was 0.45. The mean expert IoU as a measure of agreement between the three experts was 0.60.
Conclusions
In the context of this pilot study, the predictions of SCC by a Barrett’s dedicated AI showed some overlap to the expert-agreement. Therefore, features learned from Barrett’s cancer-related training might be helpful also for SCC prediction. Our results allow different possible explanations. On the one hand, some Barrett’s cancer features generalize toward the related task of assessing early SCC. On the other hand, the Barrett’s AI is less specific to Barrett’s cancer than a general predictor of pathological tissue. However, we expect to enhance the detection quality significantly by extending the training to SCC-specific data. The insight of this study opens the way towards a transfer learning approach for more efficient training of AI to solve tasks in other domains.
Aims
Endoscopic retrograde cholangiopancreaticography (ERCP) is the gold standard in the diagnosis as well as treatment of diseases of the pancreatobiliary tract. However, it is technically complex and has a relatively high complication rate. In particular, cannulation of the papillary ostium remains challenging. The aim of this study is to examine whether a deep-learning algorithm can be used to detect the major duodenal papilla and in particular the papillary ostium reliably and could therefore be a valuable tool for inexperienced endoscopists, particularly in training situation.
Methods
We analyzed a total of 654 retrospectively collected images of 85 patients. Both the major duodenal papilla and the ostium were then segmented. Afterwards, a neural network was trained using a deep-learning algorithm. A 5-fold cross-validation was performed. Subsequently, we ran the algorithm on 5 prospectively collected videos of ERCPs.
Results
5-fold cross-validation on the 654 labeled data resulted in an F1 value of 0.8007, a sensitivity of 0.8409 and a specificity of 0.9757 for the class papilla, and an F1 value of 0.5724, a sensitivity of 0.5456 and a specificity of 0.9966 for the class ostium. Regardless of the class, the average F1 value (class papilla and class ostium) was 0.6866, the sensitivity 0.6933 and the specificity 0.9861. In 100% of cases the AI-detected localization of the papillary ostium in the prospectively collected videos corresponded to the localization of the cannulation performed by the endoscopist.
Conclusions
In the present study, the neural network was able to identify the major duodenal papilla with a high sensitivity and high specificity. In detecting the papillary ostium, the sensitivity was notably lower. However, when used on videos, the AI was able to identify the location of the subsequent cannulation with 100% accuracy. In the future, the neural network will be trained with more data. Thus, a suitable tool for ERCP could be established, especially in the training situation.