Applications In Medical Diagnosis Research Articles

A new association coefficient measure for the conflict management and its application in medical diagnosis

A new association coefficient measure for the conflict management and its application in medical diagnosis

Investigating The Flow Structure Of Dean Vortices In A Curved Microchannel; Implementing 3D-µPTV Using FVM And Near Wall Data Refinement

Microfluidic devices are now more commonly used for various applications in medical diagnosis, pharmaceutical and agricultural applications. Flow structure in these devices can be useful for passive manipulations. Therefore, developing an experimental investigation to study a common geometry in micro devices such as the flow in a curved channel can be a booster in designing high performance microfluidic devices. The paper provides a study on the implementation of the scanning μPTV in a micro device. It also investigates the application of a tessellation approach to refine the near wall data using a high quality triangular mesh. The out-of-plane velocity calculation method is also developed to reconstruct the 3D velocity field inside the microchannel device.

Novel BaY1-xGdxF5: Yb3+/Er3+/Bi3+ solid solution nanocrystals with enhanced upconversion luminescence and magnetism properties

Fluoride phosphors with enhanced upconversion luminescence performance and multi-functionalization are urgently explored for applications in modern bio-detection and medical imaging. Here, a series of BaY1-xGdxF5: Yb/Er/Bi (0 ≤ x ≤ 0.5) solid solution nanocrystals are designed and synthesized. The influence of Gd3+-doping concentration on microstructure, upconversion luminescence, and magnetic performances are deeply reported. XRD results demonstrate the formation of solid solution with cubic phase structure. With increasing Gd3+ concentration, the diffraction angle gradually shifts toward lower direction as a result of replacing Y3+ by Gd3+ with larger ionic radius. Microstructure results show that the size distribution is between 60 and 100 nm and reveal that Gd3+ facilitates the growth of crystal grains. Upon 980 nm excitation, the BaY1-xGdxF5: Yb/Er/Bi samples demonstrate two dominant green and red emissions of Er3+ at 546 nm and 658 nm, respectively. By substituting Y3+ with Gd3+, the characteristic green luminescence can efficiently lead to an obvious enhancement at x = 0.2. The detailed enhancement mechanism is presented. Besides, the obtained nanocrystals show excellent paramagnetism properties, and the magnetic magnitude is significantly enhanced with increasing Gd3+ concentration. These results indicate that the BaY1-xGdxF5: Yb/Er/Bi nanocrystals are promising three-mode probes with potential applications in medical imaging and disease diagnosis.

Quantitative detection of CpG methylation level on G-quadruplex and i-motif-forming DNA by recombinase polymerase amplification.

Detection of CpG methylation levels holds immense potential for application in medical diagnosis of various diseases. In this study, we report the development of a recombinase polymerase amplification (RPA)-based CpG methylation level sensing system on G-quadruplex (G4) and intercalated motif (i-motif)-forming regions, which are stabilized by CpG methylation. This detection system is based on the principle that DNA polymerase is stalled at the methylated G4 and i-motif-forming region, which results in a decrease in the initial elongation efficiency of RPA. This reduction in turn affects the onset of amplification depending on the extent of CpG methylation; therefore, the methylation level is quantified by RPA. We demonstrate that the onset of amplification was delayed by CpG methylation when PCR products containing the vascular endothelial growth factor (VEGF) G4 and i-motif-forming region were used as the template. Furthermore, onset of amplification was delayed with the increase in CpG methylation of the VEGF region on genomic DNA. These results demonstrate that the sensing system is capable of directly detecting the methylation level at a constant temperature (39 °C) within 30 min without performing bisulfite conversion or affinity capture of methylated DNA.

MACHINE LEARNING APPLICATIONS IN MEDICAL DIAGNOSIS : A REVIEW

Artificial Intelligence and Machine Learning are the two important techniques in the current era, playing important role in almost each and every field of our daily life. These techniques are preferred because of their accuracy and predictability. Nowadays, Machine Learning is getting importance in Medical fields also. Due to the result accuracy, Machine Learning Techniques are used in even critical medical diagnosis application. These techniques are used in prediction of various types of cancers such as brain cancer, Lung cancer, Breast cancer, in Brain MRI, in CT scan and various critical diagnoses. In this paper a review is provided on the applications of Machine Learning techniques in various medical diagnosis applications. As a future work, we will implement some approach for medical diagnosis using ML Techniques.

Highly Sensitive Thin‐Film Sensing with a Toroidal Dipole Resonance in the Midinfrared Supported by an E‐Shaped Germanium Metasurface

Optical sensing applications based on resonant structures always demand on novel optical resonances with large quality factors to achieve high sensitivities. Herein, it is experimentally demonstrated that a toroidal dipole (TD) response working in the midinfrared, supported by a metasurface structure composed of E‐shaped germanium elements on a CaF2 substrate, can be harnessed to achieve high‐performance optical sensors. By elaborately designing the geometrical parameters of the E‐shaped element, a TD resonance can be supported, which is verified by multipole decomposition of the scattering spectrum. Thin films of polymethyl methacrylate (PMMA) with different thicknesses coated on top of the metasurface structures are used as an example to demonstrate the sensing characteristics of the TD resonance. Both numerical and experimental results show that the TD spectral position is highly dependent on the PMMA thickness and a minimum measurable thickness below 10 nm can be steadily achieved. The results demonstrate that the use of TD resonances provides an effective and viable approach for thin‐film sensing, which may find broad applications in biological and medical diagnosis.

Tunable liquid crystal grating based holographic 3D display system with wide viewing angle and large size

As one of the most ideal display approaches, holographic 3-dimensional (3D) display has always been a research hotspot since the holographic images reproduced in such system are very similar to what humans see the actual environment. However, current holographic 3D displays suffer from critical bottlenecks of narrow viewing angle and small size. Here, we propose a tunable liquid crystal grating-based holographic 3D display system with wide viewing angle and large size. Our tunable liquid crystal grating, providing an adjustable period and the secondary diffraction of the reconstructed image, enables to simultaneously implement two different hologram generation methods in achieving wide viewing angle and enlarged size, respectively. By using the secondary diffraction mechanism of the tunable liquid crystal grating, the proposed system breaks through the limitations of narrow viewing angle and small size of holographic 3D display. The proposed system shows a viewing angle of 57.4°, which is nearly 7 times of the conventional case with a single spatial light modulator, and the size of the reconstructed image is enlarged by about 4.2. The proposed system will have wide applications in medical diagnosis, advertising, education and entertainment and other fields.

More than Meets the Eye: Using Textural Analysis and Artificial Intelligence as Decision Support Tools in Prostate Cancer Diagnosis-A Systematic Review.

(1) Introduction: Multiparametric magnetic resonance imaging (mpMRI) is the main imagistic tool employed to assess patients suspected of harboring prostate cancer (PCa), setting the indication for targeted prostate biopsy. However, both mpMRI and targeted prostate biopsy are operator dependent. The past decade has been marked by the emerging domain of radiomics and artificial intelligence (AI), with extended application in medical diagnosis and treatment processes. (2) Aim: To present the current state of the art regarding decision support tools based on texture analysis and AI for the prediction of aggressiveness and biopsy assistance. (3) Materials and Methods: We performed literature research using PubMed MeSH, Scopus and WoS (Web of Science) databases and screened the retrieved papers using PRISMA principles. Articles that addressed PCa diagnosis and staging assisted by texture analysis and AI algorithms were included. (4) Results: 359 papers were retrieved using the keywords “prostate cancer”, “MRI”, “radiomics”, “textural analysis”, “artificial intelligence”, “computer assisted diagnosis”, out of which 35 were included in the final review. In total, 24 articles were presenting PCa diagnosis and prediction of aggressiveness, 7 addressed extracapsular extension assessment and 4 tackled computer-assisted targeted prostate biopsies. (5) Conclusions: The fusion of radiomics and AI has the potential of becoming an everyday tool in the process of diagnosis and staging of the prostate malignancies.

A Fast-Response Breathing Monitoring System for Human Respiration Disease Detection

This paper presents a sensing system for the real-time monitoring of human respiration. The system is equipped with a fast response thermoresistive micro calorimetric flow (TMCF) sensor and a dedicated data processing algorithm. The TMCF sensor is designed with a proposed nonlinear sensor model and fabricated in a CMOS compatible process, which obtains a high sensitivity of 114 mV/SLM and a fast response time of less than 6 ms. By using this high-performance micro flow sensor and its proprietary data processing algorithm, critical human respiration information including respiratory rate (RR) and minute ventilation (MV) can be easily obtained. The proposed sensing system achieves a very small mean absolute error (MAE) of less than 2.7 mHz for RR, and the extracted MV is also in good agreement with the commonly reported value of 4 - 6 L/min. In addition, benefiting from the very short time constant of the developed TMCF sensor, the proposed sensing system can successfully distinguish different respiratory diseases, such as apnea, hypopnea, polypnea, etc. Therefore, this proposed human respiration monitoring system will be a promising sensing technology for respiration diagnosis in medical applications.

Realizing the second harmonic acoustic focusing based on an artificial bubble array

Ultrasonic imaging technologies possess extensive applications in modern medical diagnosis and treatment. In this paper, a designed artificial bubble array is utilized to establish a multi-scattering sound field model to describe the nonlinear vibration of the artificial bubble array, combining nonlinear acoustics and multi-scattering theory. With the results of the effective medium model, the correctness and reliability of the multi-scattering model are verified. Furthermore, under different incident frequencies and different bubble array parameters, the distributions of fundamental and second harmonic sound pressure fields produced by nonlinear vibration of the artificial bubble array are investigated. Via a judicious design of structural parameters, both fundamental and second harmonic acoustic focusing at specific locations inside the bubble array and second harmonic acoustic focusing outside the array under a specific incident frequency are realized.

Remote Gait Analysis as a Proxy for Traditional Gait Laboratories: Utilizing Smartphones for Subject-Driven Gait Assessment across Differing Terrains

Gait analysis has applications in medical diagnosis, biometrics, and development of therapeutic rehabilitation interventions (such as orthotics, prosthetics, and exoskeletons). While offering accurate measurements, gait laboratories are expensive, not scalable, and not easily accessible. In a pandemic-afflicted world, where telemedicine is crucial, there is need for subject-driven data remote collection. This study proposed a remote and purely subject-driven procedure for reproducible and scalable collection of real-life gait data. To evaluate the feasibility of our proposed procedure, the spatiotemporal parameters of gait were compared across two real-life terrains using a smartphone application on a focus population of healthy middle-aged individuals. Previous research validated smartphone motion sensors as accurate instruments for gait analysis, but required highly supervised, controlled environments on smaller sample sizes, thereby limiting application in real-life gait analysis. To this end, a custom-designed mobile application was developed to record lower extremity angular velocities on 69 healthy middle-aged adults; factoring in a subject-driven data submission error rate (DSER) of 17.4%, there were 57 usable data sets for analysis. Comparisons of spatiotemporal gait parameters across primary outcome measures on grass versus asphalt revealed significant measurable increases in gait duration (stride time), valley depth (max swing phase), and peak-to-valley (max stance phase to max swing phase). These results demonstrated the feasibility of using smartphones for a remote and fully subject-driven gait data collection. Additionally, our data analysis showed that even in short trials, a physical environmental load has a substantial and measurable effect on the gait of the understudied middle-aged population.

Fuzzy Nano z-locally Closed Sets, Extremally Disconnected Spaces, Normal Spaces, and Their Application

In this paper, we introduce fuzzy nano (resp. δ, δS, P and Z) locally closed set and fuzzy nano (resp. δ, δS, P and Z) extremally disconnected spaces in fuzzy nano topological spaces. Also, we introduce some new spaces called fuzzy nano (resp. δ, δS, P and Z) normal spaces and strongly fuzzy nano (resp. δ, δS, P and Z) normal spaces with the help of fuzzy nano (resp. δ, δS, P and Z)-open sets in fuzzy nano topological space. Numerical data is used to quantify the provided features. Furthermore, using fuzzy nano topological spaces, an algorithm for multiple attribute decision-making (MADM) with an application in medical diagnosis is devised.

Broadband achromatic polarization-insensitive metalens in the mid-wave infrared range.

Infrared imaging is widely used in astronomical observation, medical diagnosis, and military applications. In recent years, metasurface technology has provided an unparalleled platform for the development of miniaturized and integrated infrared imaging systems. However, metasurfaces normally have inevitable chromatic aberration due to the high phase dispersion of the building blocks, which makes broadband achromatic infrared imaging difficult to realize. In this paper, we propose a polarization-insensitive metalens with a numerical aperture of 0.38 that can eliminate chromatic aberration for unpolarized incidences with the wavelength ranging from 3 to 5 µm. The simulated results show that within the design bandwidth, the proposed device achieves near-diffraction limit focusing and can increase the fill factor of infrared focal plane array pixels by 2.3 times, from 11.1% to 36.4%, with an excellent optical crosstalk performance of about 2.72%. Our work may pave the way for the practical application of achromatic metalenses in mid-wave infrared imaging equipment.

Carbon Dots Synthesized from Cinchona Pubescens Vahl. An Efficient Antibacterial Nanomaterial and Bacterial Detector.

AbstractCarbon dots (CDs) synthesized using Cinchona pubescens Vahl from the hydrothermal method displayed blue emission radiation, pH dependency, homogeneous sizes, colloidal stability in water, and antibacterial features. They consist of graphene oxide and reduced graphene oxide, attributed to the antioxidant species extracted from the Cinchona pubescens Vahl that simultaneously synthesized and reduced graphene oxide. They reach a bacterial minimal inhibitory concentration of 62.5 μg mL−1 against Gram‐positive Staphylococcus aureus and Gram‐negative Pseudomonas aeruginosa strains, which is better than traditional antibiotics and other CDs obtained from similar natural sources and methods. The photoluminescent CDs were also used to detect bacteria, an essential application in microbiology, biotechnology, medical diagnosis, and food safety, showing the capability of distinguishing between gram‐positive and gram‐negative strains and between dead and alive gram‐positive bacteria. Thus, CDs obtained by green resources represent a promising platform to produce functional antibacterial agents and bacteria detectors.

Self-powered and multi-mode flexible sensing film with patterned conductive network for wireless monitoring in healthcare

The flexible and multi-functional strain sensors have attracted extensive research attention due to their promising applications in various wearable electronics. However, it remains a huge challenge to design and fabricate the self-powered flexible sensing micro-system based on a single multifunctional material to relieve the dependence on external rigid and heavy battery. Herein, a self-powered and multi-mode flexible sensing system integrated with triboelectric nanogenerator (TENG), flexible solid-state supercapacitors (FSSC) and strain sensor is developed based on thermoplastic polyurethane film with asymmetric and cross conductive networks through a facile screen printing process. The unique conductive network endows the prepared strain sensor with an independent anisotropic response to the strain applied in different directions. The TENG in single-electrode mode is presented to generate electricity from ubiquitously bio-mechanical energy and stored it in the FSSC immediately, realizing continuous green power supply for the sensor. As proof-of-concept, an all-in-one smart device consisting of energy supply and sensing module is constructed, demonstrating the great convenience and feasibility for practical application in sustainable wearable electronics. Benefiting from the excellent comprehensive sensing performance, the wireless wearable device assembled by the prepared sensor exhibits excellent detection and recognition for various human motion, expressions, phonation, and physiological signal, revealing the broad potential applications in rehabilitation training, human-machine interaction and medical diagnosis. This work proposes an innovative and scalable approach to manufacture multifunctional micro-systems, which will bring new vitality and more possibilities to a new generation of wearable electronics.

Deep Learning-Based Medical Image Fusion Using Integrated Joint Slope Analysis with Probabilistic Parametric Steered Image Filter

Medical image fusion plays a significant role in medical diagnosis applications. Although the conventional approaches have produced moderate visual analysis, still there is a scope to improve the performance parameters and reduce the computational complexity. Thus, this article implemented the hybrid fusion method by using the novel implementation of joint slope analysis (JSA), probabilistic parametric steered image filtration (PPSIF), and deep learning convolutional neural networks (DLCNNs)-based SR Fusion Net. Here, JSA decomposes the images to estimate edge-based slopes and develops the edge-preserving approximate layers from the multi-modal medical images. Further, PPSIF is used to generate the feature fusion with base layer-based weight maps. Then, the SR Fusion Net is used to generate the spatial and texture feature-based weight maps. Finally, optimal fusion rule is applied on the detail layers generated from the base layer and approximate layer, which resulted in the fused outcome. The proposed method is capable of performing the fusion operation between various modalities of images, such as MRI-CT, MRI-PET, and MRI-SPECT combinations by using two different architectures. The simulation results show that the proposed method resulted in better subjective and objective performance as compared to state of art approaches.

Rapid Nondestructive Detection Enabled by an Ultra‐Broadband NIR pc‐LED

AbstractThe rapid development of near‐infrared (NIR) spectroscopic techniques has greatly stimulated the discovery of novel broadband NIR‐emitting phosphors as advanced light sources. Herein, a novel double‐perovskite phosphor La2MgHfO6:Cr3+/Yb3+ that displays ultra‐broadband NIR emissions with a full‐width at half maximum (FWHM) of 333 nm is reported. The remarkable luminescence property stems from the multiple crystallographic sites, relatively weak crystal field, and efficient Cr3‐to‐Yb3+ energy transfer (ET). The site occupation of Cr3+ is elaborately verified by the Rietveld refinement and first‐principles calculation. By controlling the ET process, the internal/external quantum efficiency (IQE/EQE), bandwidth, and thermal stability of NIR emissions are substantially improved. The as‐prepared phosphors are further integrated into a miniaturized NIR light‐emitting diode (LED) package, demonstrating superior performance in rapid nondestructive detection of structural failure in thin electronic cables. The results described here provide a novel pointcut for designing broadband NIR‐emitting phosphors with desired optical properties toward applications in industrial inspection and medical diagnosis.

Improving local SNR of a single-channel 54.6 mT MRI system using additional LC-resonator

Very-low field magnetic resonance imaging (VLF-MRI, B0 < 0.1T) has an essential application in medical imaging diagnosis because of its light weight and low cost. For single-channel RF coil VLF-MRI system, a planar spiral LC-resonator placed on the surface of samples was designed to improve the local SNR. First, an equivalent circuit model was established to evaluate the boosting effects on radiofrequency (RF) magnetic field and SNR. Second, the relationship between the resonant capacitance and the transmission coefficient was deduced according to the circuit model, and the appropriate resonant capacitance was obtained. Then, the influence of the diameter and the number of turns of the LC-resonator on the SNR is considered, and the structure of the LC-resonator was optimized to maximize the SNR. Finally, a phantom MRI experiment was carried out with our home-built 54.6 mT MRI system to compare the SNR of the experiment with the calculation, the SNR enhancement trend of the two was consistent. Additional experiments were conducted using orange and chicken leg to demonstrate the SNR enhancement abilities of the LC-resonator. The enhancement of SNR reached up to 1.8-fold and 2.2-fold depending on the distance between the sample and LC-resonator. For comparison, we conducted imaging experiments on surface receiving coil with the same parameters, and the results show that the SNR of the LC resonator is comparable to that of the surface coil. The reported LC-resonator provide a low-cost local enhancement method for VLF-MRI.

Study on the luminescence behavior of Dy3+ ions activated, modifier dependent alkali boro-tellurite glasses for white LED application

In the present study, an exclusive set of modifiers incorporated Dy3+ activated alkali boro-tellurite glasses is fabricated via classic melt-quenching process. The structural and molecular studies are made through XRD, FTIR and Raman analyses. The spectroscopic and radiative characteristics of the prepared glasses are explored through absorption, luminescence and decay spectral studies. The bonding nature between dopant and ligand field is analysed with the bonding parameters which detected the nature of the glasses to be ionic. The computed JO parameters trail the course Ω2 > Ω4 > Ω6 consistently for all the samples that depicts their asymmetric nature. Among the three observed transitions, a strong yellow emission is detected due to the increase in population density of the 6H13/2 level (4F9/2→6H13/2). The evaluated radiative features of the glasses prove their lasing capability. The zinc containing glass (BTZNKD) owns higher stimulated emission cross-section (σsec) which makes it applicable for lasing actions and w−LED application in medical diagnosis. The stimulation of white light for the prepared glasses is examined through colorimetric investigations including CCT, color purity and Y/B ratio. The radiative lifetime is calculated for all the glasses through the non-exponential decay and the estimated luminescence efficiency is higher for the BTZNKD (78%) glass. In summary, the BTZNKD glass is more suitable for white light generation and lasing applications.

A q-rung orthopair basic probability assignment and its application in medical diagnosis

Dempster-Shafer theory is widely used in decision-making and considered as one of the potential mathematical tools in order to fuse the evidence. However, existing studies in this theory show disadvantage due to conflicting nature of standard evidence set and the combination rule of evidence. In this paper, we have constructed the framework of q-rung evidence set to address the issue of conflicts based on the q-rung fuzzy number due to its more comprehensive range of advantage compared to the other fuzzy or discrete numbers. The proposed q-rung evidence set has the flexibility in assessing a parameter through the q-rung orthopair basic probability assignment consisting of membership and non-membership belief degree. Moreover, as the proposed q-rung orthopair basic probability assignment consists of pair of belief degrees, the possibility of conflicts cannot be ignored entirely. In this regard, a new association coefficient measure is introduced where each component of the belief degrees is modified through the weighted average mass technique. This paper uses various concept such as fuzzy soft sets, Deng entropy, association coefficient measure and score function for decision-making problem. Firstly, to obtain the initial q-rung belief function, we have implemented the Intuitionistic fuzzy soft set to assess the parameter of the alternatives and Deng entropy to find the uncertainty of the parameters. Secondly, the association coefficient measure is used to avoid the conflict through the modified form of evidence. Finally, we combined the evidence and found the score value of the Intuitionistic fuzzy numbers for the ranking of the alternatives based on the score values of alternatives. This study is validated with the case study in the medical diagnosis problem from the existing paper and compared the ranking of alternatives based on the score function of belief measures of the alternatives.