The Future of Dental Care: The Manipulation of Dental Instruments & Preparation Towards Automated Tooth Cleaning.

Dentistry is an essential practice to maintain the health of the oral cavity. Recent advances in digitization and technology for oral examinations have improved the speed and ease of disease diagnosis and dental treatment. Dental robotics has emerged as a new field of dentistry and offers numerous benefits to dental professionals and society. This paper proposes an innovative design of a dental robot setup with a preliminary study on a head model for the preparation of automated dental exploration in MATLAB and discusses further considerations for automation.

Using Schlieren Imaging and a Radar Acoustic Sounding System for the Detection of Close-in Air Turbulence.

This paper presents a novel sensor for the detection and characterization of regions of air turbulence. As part of the ground truth process, it consists of a combined Schlieren imager and a Radar Acoustic Sounding System (RASS) to produce dual-modality "images" of air movement within the measurement volume. The ultrasound-modulated Schlieren imager consists of a strobed point light source, parabolic mirror, light block, and camera, which are controlled by two laptops. It provides a fine-scale projection of the acoustic pulse-modulated air turbulence through the measurement volume. The narrow beam 40 kHz/17 GHz RASS produces spectra based on Bragg-enhanced Doppler radar reflections from the acoustic pulse as it travels. Tests using artificially generated air vortices showed some disruption of the Schlieren image and of the RASS spectrogram. This should allow the higher-resolution Schlieren images to identify the turbulence mechanisms that are disrupting the RASS spectra. The objective of this combined sensor is to have the Schlieren component inform the interpretation of RASS spectra to allow the latter to be used as a stand-alone sensor on a UAV.

W-Band Multi-Aspect High Resolution Range Profile Radar Target Classification Using Support Vector Machines.

Millimeter-wave (W-band) radar measurements were taken for two maritime targets instrumented with attitude and heading reference systems (AHRSs) in a littoral environment with the aim of developing a multiaspect classifier. The focus was on resource-limited implementations such as short-range, tactical, unmanned aircraft systems (UASs) and dealing with limited and imbalanced datasets. Radar imaging and preprocessing consisted of recording high-resolution range profiles (HRRPs) and performing range alignment using peak detection and fast Fourier transforms (FFTs). HRRPs were used because of their simplicity, reliability, and speed. The features used were fixed-length, frequency domain range profiles. Two linear support vector machine (SVM)-based classifiers were developed which both yielded excellent results in their general forms and were simple to implement. The first approach utilized the positive predictive value (PPV) and negative predictive value (NPV) statistics of the SVM directly to generate target probabilities and consequently determine the optimal aspect transitions for classification. The second approach used the Kolmogorov-Smirnov test for dimensionality reduction, followed by concatenating feature vectors across several aspects. The latter approach is particularly well-suited to resource-constrained scenarios, potentially allowing for retraining and updating in the field.

Measured Hyperelastic Properties of Cervical Tissue with Shear-Wave Elastography.

The nonlinear mechanical behaviour of cervical tissue causes unpredictable changes in measured elastograms when pressure is applied. These uncontrolled variables prevent the reliable measurement of tissue elasticity in a clinical setting. Measuring the nonlinear properties of tissue is difficult due to the need for both shear modulus and strain to be taken simultaneously. A simulation-based method is proposed in this paper to resolve this. This study describes the nonlinear behaviour of cervical tissue using the hyperelastic material models of Demiray-Fung and Veronda-Westmann. Elastograms from 33 low-risk patients between 18 and 22 weeks gestation were obtained. The average measured properties of the hyperelastic material models are: Demiray-Fung-A1α = 2.07 (1.65-2.58) kPa, α = 6.74 (4.07-19.55); Veronda-Westmann-C1C2 = 4.12 (3.24-5.04) kPa, C2 = 4.86 (2.86-14.28). The Demiray-Fung and Veronda-Westmann models performed similarly in fitting to the elastograms with an average root mean square deviation of 0.41 and 0.47 ms-1, respectively. The use of hyperelastic material models to calibrate shear-wave speed measurements improved the consistency of measurements. This method could be applied in a large-scale clinical setting but requires updated models and higher data resolution.

Magnetorheological Gel Mimicking Cervical Ripening as a Potential Model for Evaluating Shear Wave Elastography.

The mechanical characteristics of tissue can reflect its biochemical content and, therefore, be a powerful tool in the diagnosis of diseases. Many different methods have been developed for testing the mechanical properties of tissue, such as aspiration, indentation and shear wave elastography. Soft tissues are, however, more complex in behaviour than current commercial tissue-mimicking materials and the models used in measurement methods. Complex behaviours of the tissue include anisotropy and heterogeneous elasticity. The oversimplified models assumed in different measuring methods often neglect the effects of these behaviours, resulting in inaccuracies. The aim of this study was to develop a tissue-mimicking material able to capture the complexity of tissue mechanical behaviour. It will be used to improve mechanical property measuring methods by quantitatively determining how complexities in tissue behaviour affect the measurements made and evaluating the effectiveness of methods designed to overcome it, and will be used to train users for consistency in measurement. The tissue-mimicking material designed in this study focuses on the mechanical properties of the cervix as measured by shear wave elastography. The characteristic behaviours of cervical tissues highlighted are anisotropy, a wide range of elasticity that changes with gestational age and an elasticity gradient across the tissue. Magnetorheological gels were used as their elastic properties can be tuned with the application of magnetic fields. The sample was simulated with the finite-element software COMSOL before being tested by shear wave elastography and the INSTRON universal material testing machine. It had an elasticity range of 6.75-11.06 kPa, which is similar to that of cervical tissue. It was determined that a change in the orientation of the probe with respect to the orientation of anisotropy can cause up to a 30 % increase in measured elasticity. There was a 16% decrease in elasticity across the sample.

The Identification and Tracking of Uterine Contractions Using Template Based Cross-Correlation.

The purpose of this paper is to outline a novel method of using template based cross-correlation to identify and track uterine contractions during labour. A purpose built six-channel Electromyography (EMG) device was used to collect data from consenting women during labour and birth. A range of templates were constructed for the purpose of identifying and tracking uterine activity when cross-correlated with the EMG signal. Peak finding techniques were applied on the cross-correlated result to simplify and automate the identification and tracking of contractions. The EMG data showed a unique pattern when a woman was contracting with key features of the contraction signal remaining consistent and identifiable across subjects. Contraction profiles across subjects were automatically identified using template based cross-correlation. Synthetic templates from a rectangular function with a duration of between 5 and 10 s performed best at identifying and tracking uterine activity across subjects. The successful application of this technique provides opportunity for both simple and accurate real-time analysis of contraction data while enabling investigations into the application of techniques such as machine learning which could enable automated learning from contraction data as part of real-time monitoring and post analysis.