Three-dimensional Data Research Articles

Numerical biomechanical finite element analysis of different trimming line designs of orthodontic aligners: An in silico study

A finite element model was used to investigate the effect of different designs and thicknesses of orthodontic aligner margins on their biomechanical behavior. A three-dimensional data set of an upper jaw was imported into the 3-matic software. The upper right central incisor tooth (Tooth 11) was separated from the remaining model, and its periodontal ligament and surrounding bone were designed. Aligners were designed with four different trimming lines (scalloped, straight, scalloped extended, straight extended), each with four different thicknesses (0.3, 0.4, 0.5, and 0.6 mm). The models were imported into a finite element package (Marc/Mentat). A linear elastic constitutive material model was applied. A facial 0.2 mm bodily malalignment of tooth 11 was simulated. The maximum resultant force was in the range of 1.0 N to 2.2 N. The straight trimming designs deliver higher resultant forces compared with scalloped trimming designs. Increasing the aligner thickness and/or extending the aligner edge beyond the gingival line leads to an increase in the resultant force. All designs showed an uneven distribution of the normal contact forces over the tooth surface with a predominant concentration toward the cervical third and distal third, particularly with the extended trimming designs. All designs showed uncontrolled tipping of the tooth. Based on the current model outcomes, the use of a straight extended trimming line design for aligners is favored because of its positive impact on force distribution and, consequently, the control of tooth movement. These findings provide aligner companies and orthodontists a valuable biomechanical evidence and guidance to enhance control over tooth movement and therefore optimize treatment outcomes. This can be achieved by trimming the edges of aligners with a straight extended design and selecting the appropriate aligner thickness.

Comparative Analysis of the Diagonal Stride Technique during Roller Skiing and On-Snow Skiing in Youth Cross-Country Skiers.

Roller skiing is one primary form of training method as it is an off-snow equivalent to cross-country (XC) skiing during the dry land preseason training, but the results could only be applied to on-snow skiing with appropriate caution. The aim of this present study was to investigate the similarities and differences in roller skiing and on-snow skiing with the diagonal stride (DS) technique. Six youth (age: 14.3 ± 2.9 years) skiers participated in this study. Two high-definition video camcorders and FastMove 3D Motion 2.23.3.3101 were used to obtain the three-dimensional kinematic data. The cycle characteristics and joint angle ROM of the DS technique while skiing on different surfaces were similar. Almost all joint angle-time curves that were obtained from roller skiing showed a moderate-to-high degree of similarity to the angle-time curves obtained from on-snow skiing, except the hip adduction-abduction angle. The differences between roller skiing and on-snow skiing were mainly found in the body and calf anteversion angles, and the joint angles at critical instants. DS roller skiing can simulate DS on-snow skiing to a large extent in youth athletes. The hip movement, knee flexion, and calf anteversion at ski/roller ski touchdown and take-off, pole inclination at pole touchdown, body anteversion angle, and trunk anteversion angle at pole touchdown were the points that required caution when transferring preseason practice roller skiing to on-snow skiing.

Using craniofacial fluctuating asymmetry to examine the effects of sex, socioeconomic status, and early life experiences on adult age at death in industrial England.

Historical evidence from 18th- and 19th-century England suggests that industrialization's impacts on health were largely negative, especially among marginalized groups. However, available documentary evidence is often biased toward adult men and rarely sheds light on the experiences of other members of the population, such as women and children. Craniofacial fluctuating asymmetry (FA) can serve as a proxy measurement of developmental instability and stress during development. This study examines the associations among age, sex, socioeconomic status (SES), and FA in skeletal samples from industrial-era England. The sample for this study comes from four industrial-era cemeteries from England (A.D. 1711-1857). Geometric morphometric analyses of three-dimensional landmark coordinate data were used to generate a measure of FA for each individual (Mahalanobis distance). A three-way ANOVA was used to evaluate the impacts of sex, SES, and FA scores on adult age at death (n = 168). Significant associations existed between age at death and SES (p = 0.004) and FA scores (p = 0.094). Comparisons of the estimated means indicated that age at death was consistently higher among high SES individuals and individuals with FA scores less than one standard deviation from the mean. This study supports findings from previous studies that have suggested that the differences in resource access and environmental buffering generated by socioeconomic inequality can impact longevity and patterns of mortality among socioeconomic status groups. Likewise, stress in early life-evinced by craniofacial fluctuating asymmetry-can influence observed patterns of longevity in adults decades later.

A-SOiD, an active-learning platform for expert-guided, data-efficient discovery of behavior.

To identify and extract naturalistic behavior, two methods have become popular: supervised and unsupervised. Each approach carries its own strengths and weaknesses (for example, user bias, training cost, complexity and action discovery), which the user must consider in their decision. Here, an active-learning platform, A-SOiD, blends these strengths, and in doing so, overcomes several of their inherent drawbacks. A-SOiD iteratively learns user-defined groups with a fraction of the usual training data, while attaining expansive classification through directed unsupervised classification. In socially interacting mice, A-SOiD outperformed standard methods despite requiring 85% less training data. Additionally, it isolated ethologically distinct mouse interactions via unsupervised classification. We observed similar performance and efficiency using nonhuman primate and human three-dimensional pose data. In both cases, the transparency in A-SOiD's cluster definitions revealed the defining features of the supervised classification through a game-theoretic approach. To facilitate use, A-SOiD comes as an intuitive, open-source interface for efficient segmentation of user-defined behaviors and discovered sub-actions.

Biomechanical Analysis of a Newly Proposed Surgical Combination (MIS Screw-Rod System for Indirect Decompression+ Interspinous Fusion System for long Term Spinal Stability) in Treatment of Lumbar Degenerative Diseases

ObjectiveThe aim of this study is to analyze the biomechanical stability of a newly proposed surgical combination (minimally invasive surgery of screw-rod system for indirect decompression+ interspinous fusion system for long term spinal stability) in treatment of lumbar degenerative diseases Materials and methodsThe three-dimensional CT image data of an adult healthy male volunteer was selected. An intact model of L4/5 was further established and validated by using Mimic and 3-matic, 3D slicer, abaqus, Python. Four surgical models were constructed. The biomechanical stability among these surgical modes was compared and analyzed using finite element analysis. ResultsThe maximum von mises on fixation system in surgical models 2 and 3 exhibited comparable values. This finding suggested that the increase in interspinous fusion did not result in a significant elevation in maximum von mises on fixation system. Compared with the third surgical model, the fourth model, which received less average von mises experienced by the screw in contact with both cancellous and cortical bone. The findings indicated that the inclusion of facet joint fusion in surgical procedures might not be necessary to increase the average von Mises stress experienced by the screw in contact with both cancellous and cortical bone. ConclusionThe biomechanical stability of the newly proposed surgical combination (MIS screw-rod for indirect decompression + interspinous fusion for long term spinal stability technique) was not lower than that of the other surgical combination groups, and it might not be necessary to perform facet joint fusion during the surgery.

INTEGRATION OF 3D SPATIAL INFORMATION FOR MULTI-MODAL EXPERIENCE OF THE URBAN ARCHIVE

Abstract. This paper focused on the case of Semal Village, the region that will be demolished due to undergoing recent redevelopment in Paju. We experimented utilizing 3D spatial information to capture precise urban morphology of present status before redevelopment and to transform such data into multi-modal experience of the urban archive. We investigated feasible techniques of photogrammetry and hybrid 3D modelling that could render the situated reality of the village communicatively with various medium such as 3D-printed scale model, and augmented reality (AR) contents. Through drone photography, the entire Semal village was captured, and three-dimensional data were obtained using photogrammetry. Along with information from on-site surveys, the mesh model was segmented into buildings, terrain, and vegetation for focused work. Each models reconstructed and retextured using images from the on-site surveys. All data were compiled for full-colour 3D printing and assembly. The 3D-printed scale model replicating Semal is on display at the Paju Central Library. Additionally, an AR content was created using the 3D-printed scale model and ethnographic data, aiming to archive and share people's memories, thereby continuously building a sustainable archive of the village.

ACCURATE 3D MODEL OF VENICE: PRESERVING HISTORICAL DATA AND INTRODUCING SLAM IMMS FOR CHANGE DETECTION AND UPDATING PROCEDURES

Abstract. The Municipality of Venice, through Insula srl (Insula, 2024), started the RAMSES (Rilievo Altimetrico, Modellazione Spaziale E Scansione 3D) project in 2005 with the unprecedented intention of conducting a static laser scanner survey of an entire city. The authors of this paper, who have been involved in the project on behalf of the client, including drafting general contract technical specifications, wished to revisit the survey’s findings nearly two decades later. This contribution illustrates the procedures implemented to guarantee the future accessibility of the surveyed data. It is interesting to highlight how the detailed technical specifications outlined in the general technical contract section have facilitated the retrieval of the historical three-dimensional laser scanner measurements archive. Tests have been conducted to determine how the existing mobile mapping technologies may be utilised to update the three-dimensional historical data obtained in the Ramses project efficiently. Furthermore, the paper describes the surveying approach that has never been adequately described in the literature. The surveying and geo-referencing methodologies continue to have several interesting and relevant aspects, especially regarding how the topographic network has been implemented. The Ramses three-dimensional model represents an extraordinary, valuable digital archive containing portraits of the city’s conditions at the time of the mapping. Ramses 3D model, when enriched with field activities conducted using more updated technologies, can provide interesting and unique evaluations of the evolution of Venice’s landscape.

Enhancing biomechanical machine learning with limited data: generating realistic synthetic posture data using generative artificial intelligence.

Objective: Biomechanical Machine Learning (ML) models, particularly deep-learning models, demonstrate the best performance when trained using extensive datasets. However, biomechanical data are frequently limited due to diverse challenges. Effective methods for augmenting data in developing ML models, specifically in the human posture domain, are scarce. Therefore, this study explored the feasibility of leveraging generative artificial intelligence (AI) to produce realistic synthetic posture data by utilizing three-dimensional posture data. Methods: Data were collected from 338 subjects through surface topography. A Variational Autoencoder (VAE) architecture was employed to generate and evaluate synthetic posture data, examining its distinguishability from real data by domain experts, ML classifiers, and Statistical Parametric Mapping (SPM). The benefits of incorporating augmented posture data into the learning process were exemplified by a deep autoencoder (AE) for automated feature representation. Results: Our findings highlight the challenge of differentiating synthetic data from real data for both experts and ML classifiers, underscoring the quality of synthetic data. This observation was also confirmed by SPM. By integrating synthetic data into AE training, the reconstruction error can be reduced compared to using only real data samples. Moreover, this study demonstrates the potential for reduced latent dimensions, while maintaining a reconstruction accuracy comparable to AEs trained exclusively on real data samples. Conclusion: This study emphasizes the prospects of harnessing generative AI to enhance ML tasks in the biomechanics domain.

Retraction Note: An improved privacy-preserving data mining technique using singular value decomposition with three-dimensional rotation data perturbation

Retraction Note: An improved privacy-preserving data mining technique using singular value decomposition with three-dimensional rotation data perturbation

Study on Improved Computed Tomography Scanning Parameters for Patients with Nasal Bone Fracture Based on ITK Three-dimensional Labelling.

To investigate the influence of improved exposure parameters on the image quality of multi-slice spiral computed tomography in nasal bone fracture imaging. Fifty patients with optimised parameters combined with coronal scanning were allocated to the modified group and 50 patients with routine scanning parameters to the routine group. The image quality and nasal bone display of the two groups were assessed and statistically analysed, and the quality of scanned images before and after parameter optimisation was compared. The optimised image quality was better than that of conventional scanning parameters. The parameters used were 120 kv, 180 mA, a layer thickness of 0.625 mm, a layer spacing of 0.312 mm, a pitch of 0.516:1, a frame speed of 1 s, a scanning field of 12 cm and a reconstructed layer thickness for scanning of 0.625 mm; the scanned image was clear, and the parameter optimisation was achieved. This ensures that the annotation data in ITK labelling is more accurate. The optimised parameters and scanned coronal plane show the nasal bone and its surrounding structures more comprehensively, which is of high diagnostic value for nasal bone fractures. The three-dimensional annotation data based on ITK is more standardised, laying a foundation for the subsequent research of artificial intelligence modelling.

Enhancing Human Action Recognition with 3D Skeleton Data: A Comprehensive Study of Deep Learning and Data Augmentation

Human Action Recognition (HAR) is an important field that identifies human behavior through sensor data. Three-dimensional human skeleton data extracted from the Kinect depth sensor have emerged as a powerful alternative to mitigate the effects of lighting and occlusion of traditional 2D RGB or grayscale image-based HAR. Data augmentation is a key technique to enhance model generalization and robustness in deep learning while suppressing overfitting to training data. In this paper, we conduct a comprehensive study of various data augmentation techniques specific to skeletal data, which aim to improve the accuracy of deep learning models. These augmentation methods include spatial augmentation, which generates augmented samples from the original 3D skeleton sequence, and temporal augmentation, which is designed to capture subtle temporal changes in motion. The evaluation covers two publicly available datasets and a proprietary dataset and employs three neural network models. The results highlight the impact of temporal augmentation on model performance on the skeleton datasets, while exhibiting the nuanced impact of spatial augmentation. The findings underscore the importance of tailoring augmentation strategies to specific dataset characteristics and actions, providing novel perspectives for model selection in skeleton-based human action recognition tasks.

A framework based on subject-specific musculoskeletal models and Monte Carlo simulations to personalize muscle coordination retraining

Excessive loads at lower limb joints can lead to pain and degenerative diseases. Altering joint loads with muscle coordination retraining might help to treat or prevent clinical symptoms in a non-invasive way. Knowing how much muscle coordination retraining can reduce joint loads and which muscles have the biggest impact on joint loads is crucial for personalized gait retraining. We introduced a simulation framework to quantify the potential of muscle coordination retraining to reduce joint loads for an individuum. Furthermore, the proposed framework enables to pinpoint muscles, which alterations have the highest likelihood to reduce joint loads. Simulations were performed based on three-dimensional motion capture data of five healthy adolescents (femoral torsion 10°–29°, tibial torsion 19°–38°) and five patients with idiopathic torsional deformities at the femur and/or tibia (femoral torsion 18°–52°, tibial torsion 3°–50°). For each participant, a musculoskeletal model was modified to match the femoral and tibial geometry obtained from magnetic resonance images. Each participant’s model and the corresponding motion capture data were used as input for a Monte Carlo analysis to investigate how different muscle coordination strategies influence joint loads. OpenSim was used to run 10,000 simulations for each participant. Root-mean-square of muscle forces and peak joint contact forces were compared between simulations. Depending on the participant, altering muscle coordination led to a maximum reduction in hip, knee, patellofemoral and ankle joint loads between 5 and 18%, 4% and 45%, 16% and 36%, and 2% and 6%, respectively. In some but not all participants reducing joint loads at one joint increased joint loads at other joints. The required alteration in muscle forces to achieve a reduction in joint loads showed a large variability between participants. The potential of muscle coordination retraining to reduce joint loads depends on the person’s musculoskeletal geometry and gait pattern and therefore showed a large variability between participants, which highlights the usefulness and importance of the proposed framework to personalize gait retraining.

Comparative anatomy of the Sapajus sp. (bearded capuchin) hand with comments on tool use in a parallel evolution with the hominid pathway.

Introduction: Bearded capuchins display a wide variety of manipulatory skills and make routine use of tools in both captivity and the wild. The efficient handling of objects in this genus has led several investigators to assume near-human thumb movements, despite a lack of anatomical studies. Methods: Here, we performed an anatomical analysis of muscles and bones in the capuchin hand. Sapajus morphological traits were quantitatively compared with those of humans, chimpanzees, gorillas, and baboons. Results: The comparative analysis indicated that the Sapajus hand is more similar to that of baboons and least similar to that of humans according to the muscles, bones, and three-dimensional data. Furthermore, these findings suggest that bearded capuchins lack true thumb opponency. Regarding manipulatory skills, they display rather primitive hand traits, with limited resources for precision grasping using the opponens pollicis. Discussion: These findings suggest that bearded capuchins' complex use of tools depends more heavily on their high cognitive abilities than on a versatile hand apparatus. These findings offer crucial insights into the evolution of primate cognition.

Three-dimentional reconstruction of underwater side-scan sonar images based on shape-from-shading and monocular depth fusion

Modern marine research requires high-precision three-dimensional (3D) underwater data. Underwater environments experience severe visible light attenuation, which leads to inferior imaging compared with air. In contrast, sound waves are less affected underwater; hence side-scan sonar is used for underwater 3D reconstruction. Typically, the shape-from-shading algorithm (SfS) is widely used to reconstruct surface normal or heights from side-scan sonar images. However, this approach has challenges because of global information loss and noise. To address these issues, this study introduces a surface-normal fusion method. Specifically, we propose a frequency separation SfS algorithm using a discrete cosine transform, which provides a surface-normal map with less noise. We then fuse the surface-normal map with a novel depth estimation network to achieve high-precision 3D reconstruction of underwater side-scan sonar images. We conducted experiments on synthetic, NYU-depth-v2, and real side-scan sonar datasets to demonstrate the effectiveness of the proposed method.

Automated detection of fatal cerebral haemorrhage in postmortem CT data

During the last years, the detection of different causes of death based on postmortem imaging findings became more and more relevant. Especially postmortem computed tomography (PMCT) as a non-invasive, relatively cheap, and fast technique is progressively used as an important imaging tool for supporting autopsies. Additionally, previous works showed that deep learning applications yielded robust results for in vivo medical imaging interpretation. In this work, we propose a pipeline to identify fatal cerebral haemorrhage on three-dimensional PMCT data. We retrospectively selected 81 PMCT cases from the database of our institute, whereby 36 cases suffered from a fatal cerebral haemorrhage as confirmed by autopsy. The remaining 45 cases were considered as neurologically healthy. Based on these datasets, six machine learning classifiers (k-nearest neighbour, Gaussian naive Bayes, logistic regression, decision tree, linear discriminant analysis, and support vector machine) were executed and two deep learning models, namely a convolutional neural network (CNN) and a densely connected convolutional network (DenseNet), were trained. For all algorithms, 80% of the data was randomly selected for training and 20% for validation purposes and a five-fold cross-validation was executed. The best-performing classification algorithm for fatal cerebral haemorrhage was the artificial neural network CNN, which resulted in an accuracy of 0.94 for all folds. In the future, artificial neural network algorithms may be applied by forensic pathologists as a helpful computer-assisted diagnostics tool supporting PMCT-based evaluation of cause of death.

The Potential of Subsampling and Inpainting for Fast Low-Dose Cryo FIB-SEM Imaging.

Traditional image acquisition for cryo focused ion-beam scanning electron microscopy (FIB-SEM) tomography often sees thousands of images being captured over a period of many hours, with immense data sets being produced. When imaging beam sensitive materials, these images are often compromised by additional constraints related to beam damage and the devitrification of the material during imaging, which renders data acquisition both costly and unreliable. Subsampling and inpainting are proposed as solutions for both of these aspects, allowing fast and low-dose imaging to take place in the Focused ion-beam scanning electron microscopy FIB-SEM without an appreciable loss in image quality. In this work, experimental data are presented which validate subsampling and inpainting as a useful tool for convenient and reliable data acquisition in a FIB-SEM, with new methods of handling three-dimensional data being employed in the context of dictionary learning and inpainting algorithms using a newly developed microscope control software and data recovery algorithm.

Tropical cyclone trajectory based on satellite remote sensing prediction and time attention mechanism ConvLSTM model

The accurate and timely prediction of tropical cyclones is of paramount importance in mitigating the impact of these catastrophic meteorological events. Presently, methods for predicting tropical cyclones based on satellite remote sensing images encounter notable challenges, including the inadequate extraction of three-dimensional spatial features and limitations in long-term forecasting. As a response to these challenges, this study introduces the Temporal Attention Mechanism ConvLSTM (TAM-CL) model, designed to conduct thorough spatiotemporal feature extraction on three-dimensional atmospheric reanalysis data of tropical cyclones. By leveraging ConvLSTM with three-dimensional convolution kernels, our model enhances the extraction of three-dimensional spatiotemporal features. Furthermore, an attention mechanism is integrated to bolster long-term prediction accuracy by emphasizing crucial temporal nodes. In the evaluation of tropical cyclone track and intensity forecasts across 24, 48, and 72 h, TAM-CL demonstrates a notable reduction in prediction errors, thereby underscoring its efficacy in forecasting both cyclone tracks and intensities. This contributes to an effective exploration of the application of deep networks in conjunction with atmospheric reanalysis data.

Morphometric magnetic resonance imaging (MRI) postprocessing in MRI-negative patients with first unprovoked seizure.

The aim of the study was to evaluate the benefits of morphometric magnetic resonance imaging (MRI) postprocessing in patients presenting with a first seizure and negative MRI results and to investigate these findings in the context of the clinical and electroencephalographic data, seizure recurrence rates, and epilepsy diagnosis in these patients. We retrospectively reviewed 97 MRI scans of patients with first unprovoked epileptic seizure and no evidence of epileptogenic lesion on clinical routine MRI. Morphometric Analysis Program (MAP; v2018), automated postprocessing software, was used to identify subtle, potentially epileptogenic lesions in the three-dimensional T1-weighted MRI data. The resulting probability maps were examined together with the conventional MRI images by a reviewer who remained blinded to the patients' clinical and electroencephalographical data. Clinical data were prospectively collected between February 2018 and May 2023. Among the apparently MRI-negative patients, a total of 18 of 97 (18.6%) showed cortical changes suggestive of focal cortical dysplasia. Within the population with positive MAP findings (MAP+), seizure recurrence rates were 61.1% and 66.7% at 1 and 2 years after the first unprovoked seizure, respectively. Conversely, patients with negative MAP findings (MAP-) had lower seizure recurrence rates of 27.8% and 34.2% at 1 and 2 years after the first unprovoked seizure, respectively. Patients with MAP+ findings were significantly more likely to be diagnosed with epilepsy than those patients with MAP- findings (χ2 [1, n = 97] = 14.820, p < .001, odds ratio = 21.371, 95% CI = 2.710-168.531) during a mean follow-up time of 22.51 months (SD = 16.7 months, range = 1-61 months). MRI postprocessing can be a valuable tool for detecting subtle epileptogenic lesions in patients with a first seizure and negative MRI results. Patients with first seizure and MAP+ findings had high seizure recurrence rates, meeting the criteria for beginning epilepsy.

Pelvic Tilt in Adults With Cerebral Palsy and Its Relationship With Prior Hamstrings Lengthening.

Current studies assessing the change in pelvic tilt for ambulatory patients with cerebral palsy (CP) after surgical hamstring lengthening (SHL) lack a comparison cohort without prior SHL and are limited to younger patients. This study presents gait data of middle-aged adults with CP, primarily focusing on the pelvis, and compares pelvic tilt, trunk tilt, and knee flexion between those with and without prior SHL. A consecutive series of 54 adults with CP, a mean age of 36±13 years, and Gross Motor Function Classification System (GMFCS) levels I-III were included. Thirty-two (59%) had SHL performed at a mean age of 8±5 years. Three-dimensional gait analysis data prospectively collected at a mean of 28±14 years postoperatively were retrospectively analyzed. Chi-square tests were used to compare demographic and surgical history data and statistical parameter mapping was used to compare knee flexion during stance and pelvic and trunk tilts during the gait cycle between SHL and SHL-naive groups. Age, GMFCS level, sex, race, topography, and ethnicity were not different between the groups (P=.217-.612). Anterior pelvic tilt throughout gait was significantly greater in the SHL group compared with the SHL-naive group (63%-87%; P=.033). This difference was augmented after accounting for other surgical history and revision SHL (0%-32%, P=.019; and 46%-93%, P=.007). Within a cohort of adults with CP, GMFCS levels I-III, and a mean age of 36 years, those with a history of SHL, performed a mean of 28 years prior to 3-dimensional gait analysis, walked with increased anterior pelvic tilt compared with those without a history of SHL. [Orthopedics. 2024;47(5):270-275.].

Why flying insects gather at artificial light

Explanations of why nocturnal insects fly erratically around fires and lamps have included theories of “lunar navigation” and “escape to the light”. However, without three-dimensional flight data to test them rigorously, the cause for this odd behaviour has remained unsolved. We employed high-resolution motion capture in the laboratory and stereo-videography in the field to reconstruct the 3D kinematics of insect flights around artificial lights. Contrary to the expectation of attraction, insects do not steer directly toward the light. Instead, insects turn their dorsum toward the light, generating flight bouts perpendicular to the source. Under natural sky light, tilting the dorsum towards the brightest visual hemisphere helps maintain proper flight attitude and control. Near artificial sources, however, this highly conserved dorsal-light-response can produce continuous steering around the light and trap an insect. Our guidance model demonstrates that this dorsal tilting is sufficient to create the seemingly erratic flight paths of insects near lights and is the most plausible model for why flying insects gather at artificial lights.