Real-time optimization of prosthetic design for complete arch implant-supported treatments using finite element-based machine learning.

STATEMENT OF PROBLEM: The complete arch implant-supported treatment concept with 2 angled implants has been widely used for the prosthetic rehabilitation of edentulous patients. While mechanical analysis plays a pivotal role in minimizing suboptimal outcomes or premature failure, it is notably time-consuming. Consequently, clinical treatment planning relies heavily on dentists' subjective judgment and an optimization process is needed. PURPOSE: The purpose of this study was to develop an optimization process for providing immediate recommendations to support decision-making in configuring complete arch implant-supported prostheses. MATERIAL AND METHODS: This research was carried out in 2 phases. The first consisted of collecting a dataset from a total of 2800 finite element simulations by randomly configuring 10 implant design variables with 4 types of mandibles. The dataset was used to train an artificial neural network to predict the biomechanical performance of a given complete arch implant-supported prosthesis design configuration. In the second phase, the artificial neural network was used as the objective function predictor in a particle swarm optimization process to enable immediate recommendations for the implant placement. The optimization process was evaluated for accuracy, computing performance, and adaptability for unseen mandibles. RESULTS: Within the specified design space, the optimization process was able to find an optimal design based on an imported mandible model in 30 seconds. The optimized designs were found to improve peri-implant stress by 11.08 ±6.43%. When verified through finite element analysis, the prediction error was found to be 10.4 ±8.1%. Furthermore, the prediction of the optimal design was highly accurate when tested on 2 unseen mandibles, yielding an error of less than 1.7%. CONCLUSIONS: The suggested approach can quickly provide an optimal implant configuration for each individual and effectively reduce the average peri-implant stress in the mandible.

WiFi-Based Detection of Human Subtle Motion for Health Applications.

Neurodegenerative diseases such as Parkinson's disease affect motor symptoms with abnormally increased or reduced movements. Symptoms such as tremor and hand movement disorders can be subtle and vary daily such that the actual condition of the disease may not fully present in clinical sessions. Health examination and monitoring, if available in the living space, can capture comprehensive and quantitative information about a patient's motor symptoms, allowing physicians to make a precise diagnosis and devise a more personalized treatment. WiFi-based sensing is a potential solution for passively detecting human motion in a contactless way that collects no personally identifiable information. This study proposed an approach for human micromotion detection using the WiFi channel state information, which can be realized in a regular-sized room for home health monitoring and examination. Three types of motion were tested to evaluate the proposed method in quantifying micromotion using single and multiple WiFi links. The results show that micromotion could be captured at all distributed locations in the experimental environment (4.2 m × 7.9 m). Our computer algorithm computed the frequency and duration of simulated hand tremors with an average accuracy of 90.9% (single WiFi link)-95.7% (multiple WiFi links).

Subtle Motion Detection Using Wi-Fi for Hand Rest Tremor in Parkinson's Disease.

Parkinson's disease (PD) affects 1% of the population over the age of 60, and its prevalence increases with age. The disease progresses over time, and the condition can vary significantly in a day, which makes it difficult for precise diagnosis and medication based on short clinical sessions. Therefore, home health monitoring can play an important role in improving the healthcare of the PD patients. In this study, we proposed a method to detect, classify, and quantify daily movements and motor symptoms of PD by using the wireless sensing technology. With the presence of human movements in a space with the Wi-Fi coverage, the channel state information (CSI) of the wireless signal was transformed into images. The images were used to train a deep learning model to distinguish between different daily movements and simulated tremor. The results showed that our method obtained 99.59% and 100% accuracy of recognizing the tremor with modified VGG19 and modified Resnet152, respectively. In addition, the tremor movement was then successfully segmented out and quantified for the frequency and duration.

A Multilayered, Lesion-Embedded Ultrasound Breast Phantom with Realistic Visual and Haptic Feedback for Needle Biopsy.

Anthropomorphic phantoms have been used to provide residents with training in ultrasound-guided breast biopsy. However, different individuals differ in terms of the acoustic properties and stiffness of their breast tissues. The individual differences should be reflected in the training breast phantoms. This study aimed to develop a breast tissue-mimicking phantom that offers realistic haptic feedback and ultrasound imaging during needle insertion. We investigated the tunability of the mechanical and acoustic properties of breast tissue-mimicking materials (TMMs) to emulate fat, glandular and tumor tissues. The Design of experiments (DOE) methods and physician's feedback were used to reveal the effect of component concentration on Young's modulus and acoustic properties of breast TMMs. Furthermore, the relative backscatter power of the TMM was studied to adjust the contrast between the simulated tumor and background glandular tissue. The results indicated that Young's moduli of TMMs could be altered by adjusting the concentrations of glycerol, agar and olive oil. Changing the concentration of silicon carbide in a TMM could enhance the contrast between the target and the background materials in an ultrasound image. Finally, a series of TMMs were suggested for fat, glandular, benign tumor and malignant tumor tissues. A breast phantom with a tunability appropriately reflecting the individual differences of breast tissues was developed.

Investigating inlay designs of class II cavity with deep margin elevation using finite element method.

BACKGROUND: This study evaluates the mechanical performance of deep margin elevation technique for carious cavities by considering the shape designs and material selections of inlay using a computational approach combined with the design of experiments method. The goal is to understand the effects of the design parameters on the deep margin elevation technique and provide design guidelines from the biomechanics perspective. METHODS: Seven geometric design parameters for defining an inlay's shape of a premolar were specified, and the influence of cavity shape and material selection on the overall stress distribution was investigated via automated modelling. Material selection included composite resin, ceramic, and lithium disilicate. Finite element analysis was performed to evaluate the mechanical behavior of the tooth and inlay under a compressive load. Next, the analysis of variance was conducted to identify the parameters with a significant effect on the stress occurred in the materials. Finally, the response surface method was used to analyze the stress responses of the restored tooth with different design parameters. RESULTS: The restored tooth with a larger isthmus width demonstrated superior mechanical performance in all three types of inlay materials, while the influence of other design parameters varied with the inlay material selection. The height of the deep margin elevation layer insignificantly affected the mechanical performance of the restored tooth. CONCLUSIONS: A proper geometric design of inlay enhances the mechanical performance of the restored tooth and could require less volume of the natural dentin to be excavated. Furthermore, under the loading conditions evaluated in this study, the deep margin elevation layer did not extensively affect the strength of the tooth structure.

Simultaneously Reducing Cutting Force and Tissue Damage in Needle Insertion With Rotation.

Rotational needle insertion is commonly used in needle biopsy to improve cutting performance. The application of rotational motion for needle insertion has been shown to efficiently reduce the cutting force. However, studies have found that needle rotation can increase tissue damage due to the tissue winding effect. The bidirectional rotation of a needle during insertion can be a solution to avoid tissue winding while maintaining a low cutting force. In this study, needle insertion with bidirectional rotation was investigated by conducting mechanical and optical experiments. First, needle insertion tests were performed on gelatin-based tissue phantom samples to understand the effect of bidirectional needle rotation on the cutting force. Subsequently, the effective strain, which is an indicator of tissue damage, was observed at the cross-sections of samples in the axial and radial directions of the needle by using the digital image correlation (DIC) technology. The primary findings of this study are as follows: (1) higher needle insertion speeds result in higher cutting forces and effective strains that occur at the axial cross-section, (2) increase in the needle rotation reduces the cutting force and effective strain at the axial cross-section but increases the effective strain at the radial cross-section, (3) application of bidirectional rotation decreases the mean effective strain at the radial cross-section by 10%-25% while maintaining a low cutting force. In clinical applications, bidirectional rotation can be a useful strategy to simultaneously reduce the cutting force and tissue damage, which leads to better cutting performance and lower risks of bleeding and hematoma.

A computational approach to investigate optimal cutting speed configurations in rotational needle biopsy cutting soft tissue.

The rotational cutting method has been used in needle biopsy technologies to sample tough tissues, such as calcifications in the breast. The rotational motion of the needle introduces shear forces to the cutting surface such that the cutting force in the axial direction is reduced. As a result, tissue samples with large volume and better quality can be obtained. In order to comprehensively understand the effect of the needle rotation to the axial cutting force under a wide range of the needle insertion speed, this paper demonstrates a computational approach that incorporates the surface-based cohesive behavior to simulate a rotating needle cutting soft tissue. The computational model is validated by comparing with a cutting test dataset reported in the literature. The validated model is then used to generate response surfaces of the axial cutting force and torque in a large parameter space of needle rotation and insertion speeds. The results provide guidelines for selecting optimal speed configurations under different design situations.

Optimizing Design With Extensive Simulation Data: A Case Study of Designing a Vacuum-Assisted Biopsy Tool.

Design by Dragging (DBD) [1] is a virtual design tool, which displays three-dimensional (3D) visualizations of many simulation results obtained by sampling a large design space and ties this visual display together with a new user interface. The design space is explored through mouse-based interactions performed directly on top of the 3D data visualizations. Our previous study [1] introduced the realization of DBD with a simplistic example of biopsy needle design under a static bending force. This paper considers a realistic problem of designing a vacuum-assisted biopsy (VAB) needle that brings in more technical challenges to include dynamic tissue reaction forces, nonlinear tissue deformation, and progressive tissue damage in an integrated visualization with design suggestions. The emphasis is placed on the inverse design strategy in DBD, which involves clicking directly on a stress (or other output field parameter) contour and dragging it to a new (usually preferable) position on the contour. Subsequently, the software computes the best fit for the design variables for generating a new output stress field based on the user input. Three cases demonstrated how the inverse design can assist users in intuitively and interactively approaching desired design solutions. This paper illustrates how virtual prototyping may be used to replace (or reduce reliance on) purely experimental trial-and-error methods for achieving optimal designs.

Design by dragging: an interface for creative forward and inverse design with simulation ensembles.

We present an interface for exploring large design spaces as encountered in simulation-based engineering, design of visual effects, and other tasks that require tuning parameters of computationally-intensive simulations and visually evaluating results. The goal is to enable a style of design with simulations that feels as-direct-as-possible so users can concentrate on creative design tasks. The approach integrates forward design via direct manipulation of simulation inputs (e.g., geometric properties, applied forces) in the same visual space with inverse design via 'tugging' and reshaping simulation outputs (e.g., scalar fields from finite element analysis (FEA) or computational fluid dynamics (CFD)). The interface includes algorithms for interpreting the intent of users' drag operations relative to parameterized models, morphing arbitrary scalar fields output from FEA and CFD simulations, and in-place interactive ensemble visualization. The inverse design strategy can be extended to use multi-touch input in combination with an as-rigid-as-possible shape manipulation to support rich visual queries. The potential of this new design approach is confirmed via two applications: medical device engineering of a vacuum-assisted biopsy device and visual effects design using a physically based flame simulation.