Showing papers by "Neeraj Sharma published in 2021"

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Abstract: Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most.

Automatic segmentation of brain tumour in MR images using an enhanced deep learning approach

Abstract: The presented manuscript proposes a fully automatic deep learning method to quantify the tumour region in brain Magnetic Resonance images as the accurate diagnosis of brain tumour region is necessa...

A Multicenter Study on Carotid Ultrasound Plaque Tissue Characterization and Classification Using Six Deep Artificial Intelligence Models: A Stroke Application

Abstract: Atherosclerotic plaque in carotid arteries can ultimately lead to cerebrovascular events if not monitored The objectives of this study are (a) to design a set of artificial intelligence (AI)-based tissue characterization and classification (TCC) systems (Atheromatic 20, AtheroPoint, CA, USA) using ultrasound-based carotid artery plaque scans collected from multiple centers and (b) to evaluate the AI performance We hypothesize that symptomatic plaque is more scattered than asymptomatic plaque Therefore, the AI system can learn, characterize, and classify them automatically We developed six kinds of AI systems: four machine learning (ML) systems, one transfer learning (TL) system, and one deep learning (DL) architecture with different layers Atheromatic 20 uses two types of plaque characterization: (a) an AI-based mean feature strength (MFS) and (b) bispectrum analysis Three kinds of data were collected: London, Lisbon, and Combined (London + Lisbon) We balanced and then augmented five folds to conduct 3-D optimization for optimal number of AI layers versus folds Using K10 (90% training, 10% testing), the mean accuracies for DL, TL, and ML over the mean of the three data sets were 9355%, 9455%, and 89%, respectively The corresponding mean AUCs were 0938, 0946, and 0889 ( $p ), respectively AI paradigms showed an improvement by 1041% and 332% for London and Lisbon in comparison to Atheromatic 10, respectively On characterization, for all three data sets, MFS (symptomatic) > MFS (asymptomatic) by 4656%, 1940%, and 5384%, respectively, thus validating our hypothesis Atheromatic 20 showed consistent and stable results and is useful for carotid plaque tissue classification and characterization for vascular surgery applications

Encoder Modified U-Net and Feature Pyramid Network for Multi-class Segmentation of Cardiac Magnetic Resonance Images

Abstract: Cardiovascular diseases are leading cause of death worldwide. Timely and accurate detection of disease is required to reduce load on healthcare system and number of deaths. For this, accurate and f...

An Augmented Deep Learning Network with Noise Suppression Feature for Efficient Segmentation of Magnetic Resonance Images

Abstract: The segmentation of cardiac MR images requires extensive attention as it needs a high level of care and analysis for the diagnosis of affected part. The advent of deep learning technology has paved...

FSR and IMU sensors-based human gait phase detection and its correlation with EMG signal for different terrain walk

Abstract: The purpose of this paper is to present gait analysis for five different terrains: level ground, ramp ascent, ramp descent, stair ascent and stair descent,Gait analysis has been carried out using a combination of the following sensors: force-sensitive resistor (FSR) sensors fabricated in foot insole to sense foot pressure, a gyroscopic sensor to detect the angular velocity of the shank and MyoWare electromyographic muscle sensors to detect muscle’s activities All these sensors were integrated around the Arduino nano controller board for signal acquisition and conditioning purposes In the present scheme, the muscle activities were obtained from the tibialis anterior and medial gastrocnemius muscles using electromyography (EMG) electrodes, and the acquired EMG signals were correlated with the simultaneously attained signals from the FSR and gyroscope sensors The nRF24L01+ transceivers were used to transfer the acquired data wirelessly to the computer for further analysis For the acquisition of sensor data, a Python-based graphical user interface has been designed to analyze and display the processed data In the present paper, the authors got motivated to design and develop a reliable real-time gait phase detection technique that can be used later in designing a control scheme for the powered ankle-foot prosthesis,The effectiveness of the gait phase detection was obtained in an open environment Both off-line and real-time gait events and gait phase detections were accomplished for the FSR and gyroscopic sensors Both sensors showed their usefulness for detecting the gait events in real-time, ie within 10 ms The heuristic rules and a zero-crossing based-algorithm for the shank angular rate correctly identified all the gait events for the locomotion in all five terrains,This study leads to an understanding of human gait analysis for different types of terrains A real-time standalone system has been designed and realized, which may find application in the design and development of ankle-foot prosthesis having real-time control feature for the above five terrains,The noise-free data from three sensors were collected in the same time frame from both legs using a wireless sensor network between two transmitters and a single receiver Unlike the data collection using a treadmill in a laboratory environment, this setup is useful for gait analysis in an open environment for different terrains

Denoising and Spike Removal from Raman Spectra using Double Density Dual-Tree Complex Wavelet Transform

Abstract: We aim to show the effectiveness of the double density dual-tree complex wavelet transform to denoise the Raman signal. A comparative study is carried out of the double density dual-tree complex wavelet transform with the discrete wavelet transform, dual-tree complex wavelet transforms, and Savitzky–Golay smoothing method to show its capability and effectiveness. Results show that denoising based on the double density dual-tree complex wavelet transform can improve the quantitative and qualitative analysis of the Raman signal.

Denoising of magnetic resonance images using discriminative learning-based deep convolutional neural network

Abstract: BACKGROUND The noise in magnetic resonance (MR) images causes severe issues for medical diagnosis purposes. OBJECTIVE In this paper, we propose a discriminative learning based convolutional neural network denoiser to denoise the MR image data contaminated with noise. METHODS The proposed method incorporates the use of depthwise separable convolution along with local response normalization with modified hyperparameters and internal skip connections to denoise the contaminated MR images. Moreover, the addition of parametric RELU instead of normal conventional RELU in our proposed architecture gives more stable and fine results. The denoised images were further segmented to test the appropriateness of the results. The network is trained on one dataset and tested on other dataset produces remarkably good results. RESULTS Our proposed network was used to denoise the images of different noise levels, and it yields better performance as compared with various networks. The SSIM and PSNR showed an average improvement of (7.2 ± 0.002) % and (8.5 ± 0.25) % respectively when tested on different datasets without retaining the network. An improvement of 5% and 6% was achieved in the values of mean intersection over union (mIoU) and BF score when the denoised images were segmented for testing the relevancy in biomedical imaging applications. The statistical test suggests that the obtained results are statistically significant as p< 0.05. CONCLUSION The denoised images obtained are more clinically suitable for medical image diagnosis purposes, as depicted by the evaluation parameters. Further, external clinical validation was performed by an experienced radiologist for testing the validation of the resulting images.

Computer-aided automatic approach for denoising of magnetic resonance images

Abstract: This paper proposes a dual path deep convolution network based on discriminative learning for denoising MR images. The noise in MR images causes problems in identifying the regions of interest. The...

Multimodal sensor to measure the concurrent electrical and mechanical activity of muscles for controlling a hand prosthesis

Abstract: A wearable sensor was fabricated to provide simultaneous electromyography (EMG) and force myography (FMG) information, from the same muscle location for controlling multi-functional hand prosthesis...

An affordable transradial prosthesis based on force myography sensor

Abstract: Forearm amputation abruptly affects the patient’s life by limiting their body functionality. Available myoelectric prosthesis somewhat can regain the lost capability of amputees. Nevertheless, there are some limitations associated with these prosthetic devices: (1) their price is excessive (2) their function mainly depends on electromyography (EMG) signals, which are quite susceptible to sweat, motion artifact, electrode shift, and other electrical interference (3) these have sophisticated hardware as well as the control system. This paper introduces an affordable transradial prosthesis controlled by the force myography (FMG) signal. In this work, a unique FMG sensor was designed for the reliable detection of muscular contractions from the remaining forearm of amputees. The sensor was fabricated using a unique mechanical assembly and specific signal conditioning circuitry. A 3D printed prosthetic hand was prepared with an individual position control strategy that receives input from the designed sensor. The designed sensor was validated by determining characteristics such as sensitivity, repeatability, hysteresis, and frequency response. Moreover, its ability to detect muscular contractions was compared with that of an EMG sensor. The designed sensor showed a good correlation (r>0.88) and higher SNR values (>42 dB) as compared to the EMG sensor. Furthermore, the developed hand prototype with the implemented control scheme was successfully verified on five amputees for performing various grasping activities. The amputees were able to control the grasping force of hand fingers with the intent of muscle contraction. The hand offered fast and intuitive operation with input from the FMG sensor.

Functional Behavior of the Primary Cortical Neuronal Cells on the Large Surface of TiO₂ and SnO₂ Based Biosensing Device

Abstract: In this study, we report the fabrication of poly-L-lysine (PLL) coated large surface TiO2 and SnO2 based biosensing devices to analyze the influence of the functional behaviour of primary cortical neuronal cells Through frequency-dependent impedance study, we observed an increase in the impedance values initially most likely due to cell adhesion, proliferation and differentiation processes leading to an increase in both the single-cell mass as well as overall cellular mass; however, it got decreased eventually with the progression of various other cellular functions including neural activity, synapse formation and neuron-neuron communication Typically, formation and regulation of the neuronal junction ie, synapses noticeably affected the functional behaviour of the fabricated biosensing device by increasing the neuronal communication and thereby improving the flow of current by altering the thin film resistance and capacitance Further, the neuro-electrical phenomenon is validated by fitting the experimental impedance data to an equivalent electrical circuit model A significant shift in the Nyquist plot was also observed visually, which indicates that this alternation is primarily due to change in characteristic behaviour of the fabricated biosensing device Hence, we anticipate that the fabricated PLL coated large surface TiO2 and SnO2 based biosensing device can serve as a promising tool to monitor the influence of the functional behaviour of neuronal cells

Extended Large Area Si/ZnO Heterojunction Biosensor for Assessing Functional Behavior of Primary Cortical Neuronal Cells

Abstract: In this study, we report the poly-L-lysine (PLL) coated extended large area p-Si/n-ZnO heterojunction based biosensing device to analyze the influence of functional behavior of primary cortical neuronal cells. Wherein, the proliferation of rat embryonic neurons and neural stem cells is highly influenced by the physicochemical and structural properties of the underlying substrate mimicking the extracellular microenvironment. We observed a significant increase in the impedance values while adhesion of the neuronal cells on the substrate; however, it further got significantly decreased with the progression of various cellular functions neurite outgrowth and network formation. Such neuronal processes might have increased the propagation of flow of current by reducing the resistivity of the PLL treated n-ZnO thin film sensing area. Additionally, through the Nyquist plot, we observed a noticeable decrease in the magnitude of impedance values of the fabricated device. Hence, we believe that the fabricated PLL coated extended large area p-Si/n-ZnO heterojunction biosensor can serve as a favourable device to monitor the influence of functional behavior of neuronal cells.

Study of inner retinal neurodegeneration in Diabetes Mellitus using spectral domain optical coherence tomography.

Abstract: PURPOSE To study inner retinal neurodegeneration in Diabetes Mellitus using spectral domain optical coherence tomography (OCT). METHODS This cross-sectional study included 40 eyes of age matched healthy subjects (group N), 40 eyes of diabetic patients without diabetic retinopathy (group D) and 160 eyes with diabetic retinopathy (group R) having 40 each in subgroups R1 (mild), R2 (moderate), R3 (severe/very severe) non-proliferative stages and R4 (proliferative stage). Spectral domain OCT was used to measure the ganglion cell-inner plexiform layer (GC-IPL) thickness and retinal nerve fibre layer (RNFL) thickness. RESULTS The average GC-IPL thickness was significantly lower, both in groups D (p = 0.005) and R (p = 0.009), when compared with group N. The minimum GC-IPL thickness was also significantly lower in groups D (p < 0.001) and R (p < 0.001). There was no statistically significant difference in the average RNFL thickness among the groups. The minimum RNFL thickness was significantly lesser in group D (p = 0.027). The minimum RNFL thickness had a strongly negative correlation with the severity of DR (R = -0.828; p = 0.042). The thinning of both GC-IPL and RNFL was most pronounced in subgroup R4. CONCLUSION There is a significant reduction in the GC-IPL thickness and the RNFL thickness in diabetes even before the onset of DR. The changes in both GC-IPL thickness and RNFL thickness are most pronounced after the onset of PDR. There is a strongly negative correlation between the minimum RNFL thickness with the severity of DR.

Evaluation of a Learning-based Deformable Registration Method on Abdominal CT Images

Abstract: Background Reliable image comparisons, based on fast and accurate deformable registration methods, are recognized as key steps in the diagnosis and follow-up of cancer as well as for radiation therapy planning or surgery. In the particular case of abdominal images, the images to compare often differ widely from each other due to organ deformation, patient motion, movements of gastrointestinal tract or breathing. As a consequence, there is a need for registration methods that can cope with both local and global large and highly non-linear deformations. Method Deformable registration of medical images traditionally relies on the iterative minimization of a cost function involving a large number of parameters. For complex deformations and large datasets, this process is computationally very demanding, leading to processing times that are incompatible with the clinical routine workflow. Moreover, the highly non-convex nature of these optimization problems leads to a high risk of convergence toward local minima. Recently, deep learning approaches using Convolutional Neural Networks (CNN) have led to major breakthroughs by providing computationally fast unsupervised methods for the registration of 2D and 3D images within seconds. Among all the proposed approaches, the VoxelMorph learning-based framework pioneered to learn in an unsupervised way the complex mapping, parameterized using a CNN, between every couple of 2D or 3D pairs of images and the corresponding deformation field by minimizing a standard intensity-based similarity metrics over the whole learning database. Voxelmorph has so far only been evaluated on brain images. The present study proposes to evaluate this method in the context of inter-subject registration of abdominal CT images, which present a greater challenge in terms of registration than brain images, due to greater anatomical variability and significant organ deformations. Results The performances of VoxelMorph were compared with the current top-performing non-learning-based deformable registration method “Symmetric Normalization” (SyN), implemented in ANTs, on two representative databases: LiTS and 3D-IRCADb-01. Three different experiments were carried out on 2D or 3D data, the atlas-based or pairwise registration, using two different similarity metrics, namely (MSE and CC). Accuracy of the registration was measured by the Dice score, which quantifies the volume overlap for the selected anatomical region. All the three experiments exhibit that the two deformable registration methods significantly outperform the affine registration and that VoxelMorph accuracy is comparable or even better than the reference non-learning based registration method ANTs (SyN), with a drastically reduced computation time. Conclusion By substituting a time consuming optimization problem, VoxelMorph has made an outstanding achievement in learning-based registration algorithm, where a registration function is trained and thus, able to perform deformable registration almost accurately on abdominal images, while reducing the computation time from minutes to seconds and from seconds to milliseconds in comparison to ANTs (SyN) on a CPU.

Segmentation of Brain Tumour in MR Images Using Modified Deep Learning Network

Abstract: This paper presents a modified segmentation network for brain tumour segmentation in Magnetic Resonance Images. The early detection of brain tumour is quite mandatory for planning the treatment. This work proposes a computer-based automatic approach for the segmentation of brain tumour. The network proposed in this paper effectively delineated the boundaries of the brain tumour region. Exceedingly good results were obtained when the trained network was fed with other datasets. The network also showed a good improvement in the results when it was tested on real-time MRI datasets. An improvement of 7.6% and 7% was observed in the mIoU and BF score when the real time MR dataset of brain tumour was applied to the network. The network was incorporated using depthwise separable convolution.

Design and analysis of magnetorheological damper based ankle-foot prosthesis prototype

Abstract: Purpose This paper aims to design and analyze a controlled magnetorheological damper-based ankle-foot prosthesis prototype. Design/methodology/approach The ankle-foot prostheses prototype is proposed using the lightweight three dimensional (3 D)-printed parts, MR damper and digital servomotor. Initially, the computer-aided design (CAD) model of the prosthetic foot, leaf spring, retention spring and the various connecting parts required to connect the pylon and damper actuator assemblies are designed using CAD software. Later, the fused deposition modeling 3 D printer-based technique prints a prosthetic foot and other connecting parts using Acrylonitrile Butadiene Styrene filament. The prototype consists of two control parts: the first part controls the MR actuator that absorbs the impacts during walking. The second part is the control of the electric actuator intended to generate the dorsiflexion and plantar flexion movements. Finally, the prototype is tested on a transtibial amputee under the supervision of a prosthetist. Findings The ANalysis SYStems software-based analysis has shown that the prosthetic foot has a factor of safety values between 4.7 and 8.7 for heel strike, mid-swing and toe-off; hence, it is safe from mechanical failure. The designed MR damper-based ankle-foot prosthesis prototype is tested on an amputee for a level-ground walk; he felt comfortable compared to his passive prosthesis. Originality/value The design of an MR damper-based prosthesis prototype offers a better dynamic range for locomotion than passive prostheses. It reduces the injuries and provides relief to the transtibial amputees.

Aluminium Oxide Thin-Film Based In Vitro Cell-Substrate Sensing Device for Monitoring Proliferation of Myoblast Cells

Abstract: We demonstrate cell-substrate interaction on aluminium oxide thin-film in metal-insulator-metal structure followed by the change in dielectric characteristics of Al2O3 as a function of progression of cellular growth. The theoretical calculation of the fabricated biosensor reveals that the changes in the intrinsic elemental parameters are mainly attributed to the cell-induced behavioural changes.

Comparative Analysis of SVM and DNN for Multiple Terrain Classification Using Hybrid Sensor

Abstract: The gait phase plays a vital role in the assessment of the intricacies of human locomotion. A significant part of gait analysis is the calculation of various events like heel-strike, toe-off, and mid-swing. From these events, we can determine stance and swing phases. For this study, we take into consideration electromyographic and acceleration signals recorded using a hybrid sensor from two major leg muscles, tibialis anterior, and medial gastrocnemius. The signal is recorded for five terrains, namely level ground, ramp ascent, ramp descent, stair ascent, and stair descent. For each terrain type, the gait phase was detected, and from each gait cycle, both stance and swing phases were isolated. These signals are then labeled according to their corresponding terrain. In this study, we did a comparative analysis using a support vector machine and a deep neural network classifier. In this study, we obtained a classification accuracy of more than 95% for both classifiers; however, the support vector machine classifier outperforms deep neural network classifiers in terms of execution time.

Partial edentulism and its correlation to age, gender, socioeconomic status and incidence of various Kennedy’s classes - A cross-sectional study

Abstract: A simple estimation of the proportion of the partial edentulous persons is a rough indication of dental diseases and also the success or failure of dental care. The epidemiological features of partial edentulousness of one community or one village can be evaluated on the basis of a cross-sectional houseto-house survey. In this study a cross-sectional house-to-house survey was carried out at lower Garhwal region of Uttarakhand, India. The objective of this study was to determine the prevalence of the level of edentulism and its relevance to other co-factors such as age, gender and socioeconomic status. Keywords: Partially edentulism, Socioeconomic status, Kennedy’s classes, Survey, Garhwal.

Constraints Perceived by Camel Owners Related to Management and Marketing Practices

Abstract: Majority of the world's camel population is of dromedary type except small population of Bactrian camels in central Asia (Simenew et al., 2013). There are about 35 million of camel according to FAO in which 2.5 lakh are in India (Anonymous, 2019). Camel is one of the important components of the desert ecosystem from time immemorial and is also designated as the “Ship of the desert”. Humans depend on camel not just for meat, milk and hide but also as one of the most important mode of transport in the desert. Camels play an important socio-economic role within the pastoral and agricultural system in arid and semi-arid zones of Asia and Africa (Gwida et al., 2011). Camels contribute significantly to the livelihood of the pastoralist and agro-pastoralists living in the harsh environments (Tura et al., 2010). There are many communities and pastoralist International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 10 Number 01 (2021) Journal homepage: http://www.ijcmas.com

Cedrus Deodara Needle Oil and Bark Extract as Potential Therapeutic Candidate for Glaucoma

Abstract: Glaucoma is a neuropathic disease that causes loss of vision if not treated. Major factors that are involved in glaucoma include elevated intraocular pressure, retinal ischemia, neurotoxicity, and oxidative stress. These factors lead to the demise of retinal ganglion cells which leads to vision loss. Various pathways govern these processes. One such pathway is the glutamate–glutamine cycle that regulates the amount of glutamine. Glu is an important neurotransmitter that plays a vital role in many neurological processes. Excess Glu has also been linked to the development of glaucoma. Cedrus deodara is a coniferous tree found in the Himalayan region. The essential oil extracted from pine needles is mainly composed of terpenoids and aromatic compounds. The bark of C. deodara also contains many important compounds like neophytadiene, which has anti-oxidant and anti-inflammatory properties. In the present study, we targeted oxidative stress and the Glu-Gln cycle. Bark extract showed significant antioxidant activity. We also performed in silico analysis of two compounds, beta-Cadinene from oil and neophytadiene from the extract. The simulation studies showed stability in a protein–ligand complex. Thus, we can conclude that these components of C. deodara can be used as therapeutic candidates for glaucoma.