Comparative study of platinum electroplating to improve micro gold electrode arrays with LCP laminate

Decoding the cellular network interaction of neurons and glial cells are important in the development of new therapies for diseases of the central nervous system (CNS). Electrophysiological in vivo studies in mice will help to understand the highly complex network. In this paper, the optimization of epidural liquid crystal polymer (LCP) electrodes for different platinum electroplating parameters are presented and compared. Constant current and pulsed current electroplating varied in strength and duration was used to decrease the electrode impedance and to increase the charge storage capacity (CSCC). In best cases, both methods generated similar results with an impedance reduction of about 99%. However, electroplating with pulsed currents was less parameter-dependent than the electroplating with constant current. The use of ultrasound was essential to generate platinum coatings without plating defects. Electrode model parameters extracted from the electrode impedance reflected the increase in surface porosity due to the electroplating processes.

Stresses and deformations of an osteosynthesis plate in a lateral tibia plateau fracture

This study has the aim to investigate the strain and stress in an anterolateral locking plate applied for the fixation of a lateral split fracture. To simulate a complex fracture situation, three segments were separated. With a FEM analysis, representative places for strain and stress measurement were determined. A locked osteosynthesis plate was instrumented with strain gauges and tested on a fractured and a non-fractured Saw Bone model. To simulate different loading situations, four different points of force application, from the center of the condyles to a 15 mm posterior position, were used with a medial-lateral load distribution of 60:40. The simulations as well as the biomechanical tests demonstrated that two deformations dominate the load on the plate: a bending into posterior direction and a bulging of the plate head. Shifting the point of application to the posterior direction resulted in increasing maximum stress, from 1.16 to 6.32 MPa (FEM analysis) and from 3.04 to 7.00 MPa (biomechanical study), respectively. Furthermore, the comparison of the non-fractured and fractured models showed an increase in maximum stress by the factor 2.06–2.2 (biomechanical investigation) and 1.5–3.3 (FEM analysis), respectively.

Analysis of objective quality metrics in computed tomography images affected by metal artifacts

Image quality (IQ) assessment plays an important role in the medical world. New methods to evaluate image quality have been developed, but their application in the context of computer tomography is yet limited. In this paper the performance of fifteen well-known full reference (FR) IQ metrics is compared with human judgment using images affected by metal artifacts and processed with metal artifact reduction methods from a phantom. Five region of interest with different sizes were selected. IQ was evaluated by seven experienced radiologists completely blinded to the information. To measure the correlation between FR-IQ, and the score assigned by radiologists non-parametric Spearman rank-order correlation coefficient and Kendall’s Rank-order Correlation coefficient were used; so as root mean square error and the mean absolute error to measure the prediction accuracy. Cohen’s kappa was employed with the purpose of assessing inter-observer agreement. The metrics GMSD, IWMSE, IWPSNR, WSNR and OSS-PSNR were the best ranked. Inter-observer agreement was between 0.596 and 0.954, with p<0.001 in all study. The objective scores predicted by these methods correlate consistently with the subjective evaluations. The application of this metrics will make possible a better evaluation of metal artifact reduction algorithms in future works.

A fuzzy sensitivity analysis approach to estimate brain effective connectivity and its application to epileptic seizure detection

The brain is considered to be the most complicated organ in human body. Inferring and quantification of effective (causal) connectivity among regions of the brain is an important step in characterization of its complicated functions. The proposed method is comprised of modeling multivariate time series with Adaptive Neurofuzzy Inference System (ANFIS) and carrying out a sensitivity analysis using Fuzzy network parameters as a new approach to introduce a connectivity measure for detecting causal interactions between interactive input time series. The results of simulations indicate that this method is successful in detecting causal connectivity. After validating the performance of the proposed method on synthetic linear and nonlinear interconnected time series, it is applied to epileptic intracranial Electroencephalography (EEG) signals. The result of applying the proposed method on Freiburg epileptic intracranial EEG data recorded during seizure shows that the proposed method is capable of discriminating between the seizure and non-seizure states of the brain.

Endoscopic confocal laser-microscopy for the intraoperative nerve recognition: is it feasible?

Surgeons lose most of their tactile tissue information during minimal invasive surgery and need an additional tool of intraoperative tissue recognition. Confocal laser microscopy (CLM) is a well-established method of tissue investigation. The objective of this study was to analyze the feasibility and diagnostic accuracy of CLM nervous tissue recognition. Images taken with an endoscopic CLM system of sympathetic ganglions, nerve fibers and pleural tissue were characterized in terms of specific signal-patterns ex-vivo. No fluorescent dye was used. Diagnostic accuracy of tissue classification was evaluated by newly trained observers (sensitivity, specificity, PPV, NPV and interobserver variability). Although CLM images showed low CLM image contrast, assessment of nerve tissue was feasible without any fluorescent dye. Sensitivity and specificity ranged between 0.73 and 0.9 and 0.55–1.0, respectively. PPVs were 0.71–1.0 and the NPV range was between 0.58 and 0.86. The overall interobserver variability was 0.36. The eCLM enables to evaluate nervous tissue and to distinguish between nerve fibers, ganglions and pleural tissue based on backscattered light. However, the low image contrast and the heterogeneity in correct tissue diagnosis and a fair interobserver variability indicate the limit of CLM imaging without any fluorescent dye.

Voice pathology detection and classification from speech signals and EGG signals based on a multimodal fusion method

Automatic voice pathology detection and classification plays an important role in the diagnosis and prevention of voice disorders. To accurately describe the pronunciation characteristics of patients with dysarthria and improve the effect of pathological voice detection, this study proposes a pathological voice detection method based on a multi-modal network structure. First, speech signals and electroglottography (EGG) signals are mapped from the time domain to the frequency domain spectrogram via a short-time Fourier transform (STFT). The Mel filter bank acts on the spectrogram to enhance the signal’s harmonics and denoise. Second, a pre-trained convolutional neural network (CNN) is used as the backbone network to extract sound state features and vocal cord vibration features from the two signals. To obtain a better classification effect, the fused features are input into the long short-term memory (LSTM) network for voice feature selection and enhancement. The proposed system achieves 95.73% for accuracy with 96.10% F1-score and 96.73% recall using the Saarbrucken Voice Database (SVD); thus, enabling a new method for pathological speech detection.

Reliability and validity varies among smartphone apps for range of motion measurements of the lower extremity: a systematic review

The aim of this review was to determine whether smartphone applications are reliable and valid to measure range of motion (RoM) in lower extremity joints. A literature search was performed up to October 2020 in the databases PubMed and Cochrane Library. Studies that reported reliability or validity of smartphone applications for RoM measurements were included. The study quality was assessed with the QUADAS-2 tool and baseline information, validity and reliability were extracted. Twenty-five studies were included in the review. Eighteen studies examined knee RoM, whereof two apps were analysed as having good to excellent reliability and validity for knee flexion (“DrGoniometer”, “Angle”) and one app showed good results for knee extension (“DrGoniometer”). Eight studies analysed ankle RoM. One of these apps showed good intra-rater reliability and excellent validity for dorsiflexion RoM (“iHandy level”), another app showed excellent reliability and moderate validity for plantarflexion RoM (“Coach’s Eye”). All other apps concerning lower extremity RoM had either insufficient results, lacked study quality or were no longer available. Some apps are reliable and valid to measure RoM in the knee and ankle joint. No app can be recommended for hip RoM measurement without restrictions.

Modular 3D printable orthodontic measuring apparatus for force and torque measurements of thermoplastic/removable appliances

The magnitude of forces and moments applied on teeth during orthodontic treatment is crucial to achieve the desired tooth movement. The aim of this study is to introduce a modular 3D printable orthodontic measurement apparatus (M3DOMA), which can be used for measurements of forces and moments acting on teeth during treatment with aligners. The measurement device was characterized regarding signal to noise ratio (SNR) of the sensors, repeatability of measurements, influence of thermoforming, as well as reliability. Forces and moments were evaluated for an activation range of 0.1–0.4 mm, comparing them among different activation patterns with two aligner thicknesses. The sensors exhibited a SNR from 13–33 dB. Repeatability with repeated measurements showed standard deviations ≤0.015 N and 0.769 Nmm. The influence of thermoforming represented by standard deviation of forces ranges from 0.019–0.147 N. The device showed a range of intra class correlation (ICC) for repeated measurements for all sensors from 0.932 to 0.999. Hence the reliability of the device has been proven to be excellent.

Hippocampus segmentation and classification for dementia analysis using pre-trained neural network models

The diagnostic and clinical overlap of early mild cognitive impairment (EMCI), mild cognitive impairment (MCI), late mild cognitive impairment (LMCI) and Alzheimer disease (AD) is a vital oncological issue in dementia disorder. This study is designed to examine Whole brain (WB), grey matter (GM) and Hippocampus (HC) morphological variation and identify the prominent biomarkers in MR brain images of demented subjects to understand the severity progression. Curve evolution based on shape constraint is carried out to segment the complex brain structure such as HC and GM. Pre-trained models are used to observe the severity variation in these regions. This work is evaluated on ADNI database. The outcome of the proposed work shows that curve evolution method could segment HC and GM regions with better correlation. Pre-trained models are able to show significant severity difference among WB, GM and HC regions for the considered classes. Further, prominent variation is observed between AD vs. EMCI, AD vs. MCI and AD vs. LMCI in the whole brain, GM and HC. It is concluded that AlexNet model for HC region result in better classification for AD vs. EMCI, AD vs. MCI and AD vs. LMCI with an accuracy of 93, 78.3 and 91% respectively.