Applications In Medical Diagnosis Research Articles

Circular Linear Diophantine Fuzzy Sets and Their Application in Medical Diagnosis

Circular Linear Diophantine Fuzzy Sets and Their Application in Medical Diagnosis

Innovations in ion-selective optodes: a comprehensive exploration of modern designs and nanomaterial integration.

In recent years, ion-selective optodes (ISOs) have remarkably progressed, driven by innovative modern designs and nanomaterial integration. This review explored the development of modern ISO by describing state-of-the-art strategies to improve their sensitivity, selectivity, and real-time monitoring capacity. The review reported the traditional membrane based-optodes, and investigated the latest research, current design principles, and the use of essential components, such as ionophores, indicator dyes, polymer membranes, and nanomaterials, in ISO fabrication. Special attention was given to nanomaterials (e.g., quantum dots, polymer dots, nanospheres, nanorods and nanocapsules) and particularly on how rare earth elements can further enhance their potential. It also described innovative ISO designs, including wearable optodes, smartphone-based optodes, and disposable paper-based optodes. As the pursuit of highly sensitive, selective, and adaptable ion sensing devices continues, this summary of the current knowledge sets the stage for upcoming innovations and applications in different domains (pharmaceutical formulations, medical diagnosis, environmental monitoring, and industrial applications).

Enabling Visible-Light-Charged Near-Infrared Persistent Luminescence in Organics by Intermolecular Charge Transfer.

Visible light is a universal and user-friendly excitation source; however, its use to generate persistent luminescence (PersL) in materials remains a huge challenge. Herein, the concept of intermolecular charge transfer (xCT) is applied in typical host-guest molecular systems, which allows for a much lower energy requirement for charge separation, thus enabling efficient charging of near-infrared (NIR) PersL in organics by visible light (425-700nm). Importantly, NIR PersL in organics occurs via the trapping of electrons from charge-transfer aggregates (CTAs) into constructed trap states with trap depths of 0.63-1.17eV, followed by the detrapping of these electrons by thermal stimulation, resulting in a unique light-storage effect and long-lasting emission up to 4.6 h at room temperature. The xCT absorption range is modulated by changing the electron-donating ability of a series of acenaphtho[1,2-b]pyrazine-8,9-dicarbonitrile-based CTAs, and the organic PersL is tuned from 681 to 722nm. This study on xCT interaction-induced NIR PersL in organic materials provides a major step forward in understanding the underlying luminescence mechanism of organic semiconductors and these findings are expected to promote their applications in optoelectronics, energy storage, and medical diagnosis.

An attribution graph-based interpretable method for CNNs

Convolutional Neural Networks (CNNs) have demonstrated outstanding performance in various domains, such as face recognition, object detection, and image segmentation. However, the lack of transparency and limited interpretability inherent in CNNs pose challenges in fields such as medical diagnosis, autonomous driving, finance, and military applications. Several studies have explored the interpretability of CNNs and proposed various post-hoc interpretable methods. The majority of these methods are feature-based, focusing on the influence of input variables on outputs. Few methods undertake the analysis of parameters in CNNs and their overall structure. To explore the structure of CNNs and intuitively comprehend the role of their internal parameters, we propose an Attribution Graph-based Interpretable method for CNNs (AGIC) which models the overall structure of CNNs as graphs and provides interpretability from global and local perspectives. The runtime parameters of CNNs and feature maps of each image sample are applied to construct attribution graphs (At-GCs), where the convolutional kernels are represented as nodes and the SHAP values between kernel outputs are assigned as edges. These At-GCs are then employed to pretrain a newly designed heterogeneous graph encoder based on Deep Graph Infomax (DGI). To comprehensively delve into the overall structure of CNNs, the pretrained encoder is used for two types of interpretable tasks: (1) a classifier is attached to the pretrained encoder for the classification of At-GCs, revealing the dependency of At-GC’s topological characteristics on the image sample categories, and (2) a scoring aggregation (SA) network is constructed to assess the importance of each node in At-GCs, thus reflecting the relative importance of kernels in CNNs. The experimental results indicate that the topological characteristics of At-GC exhibit a dependency on the sample category used in its construction, which reveals that kernels in CNNs show distinct combined activation patterns for processing different image categories, meanwhile, the kernels that receive high scores from SA network are crucial for feature extraction, whereas low-scoring kernels can be pruned without affecting model performance, thereby enhancing the interpretability of CNNs.

Enhancing gait recognition by multimodal fusion of mobilenetv1 and xception features via PCA for OaA-SVM classification

Gait recognition has become an increasingly promising area of research in the search for noninvasive and effective methods of person identification. Its potential applications in security systems and medical diagnosis make it an exciting field with wide-ranging implications. However, precisely recognizing and assessing gait patterns is difficult, particularly in changing situations or from multiple perspectives. In this study, we utilized the widely used CASIA-B dataset to observe the performance of our proposed gait recognition model, with the aim of addressing some of the existing limitations in this field. Fifty individuals are randomly selected from the dataset, and the resulting data are split evenly for training and testing purposes. We begin by excerpting features from gait photos using two well-known deep learning networks, MobileNetV1 and Xception. We then combined these features and reduced their dimensionality via principal component analysis (PCA) to improve the model's performance. We subsequently assessed the model using two distinct classifiers: a random forest and a one against all support vector machine (OaA-SVM). The findings indicate that the OaA-SVM classifier manifests superior performance compared to the others, with a mean accuracy of 98.77% over eleven different viewing angles. This study is conducive to the development of effective gait recognition algorithms that can be applied to heighten people’s security and promote their well-being.

Ultrasensitive detection of H2O2 via electrochemical sensor by graphene synergized with MOF-on-MOF nanozymes.

An electrochemical sensor was developed for the detection of hydrogen peroxide (H2O2),utilizing the synergistic effects of graphene (Gr) and MOF-on-MOF nanozymes (FeCu-NZs). Initially, Fe-MOF with peroxide-like activity is synthesized using a solvothermal method. Subsequently, the organic ligand on its surface binds Cu2+, enhancing the enzyme-like activity further. The resulting FeCu-NZs exhibit a distinctive electrochemical signal in response to H2O2. Moreover, integrating FeCu-NZs with Gr significantly amplifies the electrochemical signal and effectively reduces the sensor's detection limit. The developed sensor exhibited linear ranges of 0.1-3800 μM, with a limit of detection (LOD) of 0.06 μM. Additionally, FeCu-NZs catalyze H2O2 to generate abundant •OH radicals, and colorimetric detection of H2O2 is facilitated using the color rendering principle of 3,3',5,5'-tetramethylbenzidine (TMB). Notably, this detection method was applied to determine H2O2 concentrations in real samples, achieving a recovery exceeding 95.7%. In summary, this research provides a practical platform for the construction of traditional nanozymes and the integration of electrochemical systems, which have broad applications in food analysis, environmental monitoring, and medical diagnosis.

Retraction Note: Energy demand classification by probabilistic neural network for medical diagnosis applications

Retraction Note: Energy demand classification by probabilistic neural network for medical diagnosis applications

Stabilizing and Accelerating Secondary Flow in Ultralong Spiral Channel for High-Throughput Cell Manipulation.

Efficient cell manipulation is essential for numerous applications in bioanalysis and medical diagnosis. However, the lack of stability and strength in the secondary flow, coupled with the narrow range of practical throughput, severely restricts the diverse applications. Herein, we present an innovative inertial microfluidic device that employs a spiral channel for high-throughput cell manipulation. Our investigation demonstrates that the regulation of Dean-like secondary flow in the microchannel can be achieved through geometric confinement. Introducing ordered microstructures into the ultralong spiral channel (>90 cm) stabilizes and accelerates the secondary flow among different loops. Consequently, effective manipulation of blood cells within a wide cell throughput range (1.73 × 108 to 1.16 × 109 cells/min) and cancer cells across a broad throughput range (0.5 × 106 to 5 × 107 cells/min) can be achieved. In comparison to previously reported technologies, our engineering approach of stabilizing and accelerating secondary flow offers specific performance for cell manipulation under a wide range of high-throughput manner. This engineered spiral channel would be promising in biomedical analysis, especially when cells need to be focused efficiently on large-volume liquid samples.

Development of a patient health monitoring system based on a service-oriented architecture using artificial intelligence

The object of the study is a patient health monitoring system that uses service-oriented architecture (SOA) and artificial intelligence (AI) to integrate and analyze medical data. Such a system integrates data from a variety of sources, including medical devices, health apps, electronic health records, and wearables and physiological performance recorders, providing a comprehensive approach to health monitoring. Thanks to SOA, the system is able to process large arrays of data in real time, providing the opportunity to quickly process and analyze them. This allows medical professionals to get a comprehensive picture of patients' health, taking into account both long-term trends and real-time indicators. One of the most challenging areas is ensuring effective integration and processing of disparate data from various medical devices and applications for accurate diagnosis and prognosis of diseases. It is also important to create a system that is easily scalable and can be adapted to the needs of different medical facilities and various monitoring systems. As a result of the research, it is concluded that the use of SOA allows creating flexible and scalable systems capable of integrating a wide range of medical devices and applications. The use of AI in these systems makes it possible to automatically detect deviations in health indicators, recognize pathologies in the early stages and predict the possible development of diseases. This is due to the fact that the proposed architecture has a number of features, in particular, the ability to collect, process and analyze large volumes of medical data in real time. Artificial intelligence algorithms provide high accuracy of diagnosis and forecasting thanks to the ability to quickly process complex data and find hidden patterns. Thanks to this, it is possible to obtain accurate and reliable indicators of the state of health of patients. Compared to similar known systems, it provides such advantages as increased efficiency of medical care, reduced risk of complications, early detection of diseases and a personalized approach to patient treatment, as well as the concentration of all data in one system.

Terahertz refractometry of hard-to-access objects using the sapphire endoscope suitable for harsh environments

While terahertz (THz) technology offers a variety of applications in medical diagnosis, nondestructive testing, and quality control, its acceptance in these practical fields is hampered by the absence of endoscopic systems, capable of sensing the complex refractive index of the hard-to-access objects. In this paper, we develop the THz endoscope based on the hollow-core antiresonant waveguide, formed by a polytetrafluoroethylene (PTFE)-coated sapphire tube with the outer end closed by a monolithic sapphire window. The endoscope is attached to the backward wave oscillator spectrometer to measure the sample reflectivity. By studying the well-known liquid and solid samples, we demonstrate that analysis of the Fabry–Pérot resonance in the measured reflection spectra makes it possible to quantify the complex refractive index of an analyte. Thanks to the advanced chemical inertness and thermal strength of sapphire and PTFE, the developed endoscope is capable of operation in harsh environments, which broadens the range of its applications. Our findings pave the way for the THz technology use in a number of demanding practical fields.

A deep image classification model based on prior feature knowledge embedding and application in medical diagnosis

Aiming at the problem of image classification with insignificant morphological structural features, strong target correlation, and low signal-to-noise ratio, combined with prior feature knowledge embedding, a deep learning method based on ResNet and Radial Basis Probabilistic Neural Network (RBPNN) is proposed model. Taking ResNet50 as a visual modeling network, it uses feature pyramid and self-attention mechanism to extract appearance and semantic features of images at multiple scales, and associate and enhance local and global features. Taking into account the diversity of category features, channel cosine similarity attention and dynamic C-means clustering algorithms are used to select representative sample features in different category of sample subsets to implicitly express prior category feature knowledge, and use them as the kernel centers of radial basis probability neurons (RBPN) to realize the embedding of diverse prior feature knowledge. In the RBPNN pattern aggregation layer, the outputs of RBPN are selectively summed according to the category of the kernel center, that is, the subcategory features are combined into category features, and finally the image classification is implemented based on Softmax. The functional module of the proposed method is designed specifically for image characteristics, which can highlight the significance of local and structural features of the image, form a non-convex decision-making area, and reduce the requirements for the completeness of the sample set. Applying the proposed method to medical image classification, experiments were conducted based on the brain tumor MRI image classification public dataset and the actual cardiac ultrasound image dataset, and the accuracy rate reached 85.82% and 83.92% respectively. Compared with the three mainstream image classification models, the performance indicators of this method have been significantly improved.

XECGArch: a trustworthy deep learning architecture for interpretable ECG analysis considering short-term and long-term features

Deep learning-based methods have demonstrated high classification performance in the detection of cardiovascular diseases from electrocardiograms (ECGs). However, their blackbox character and the associated lack of interpretability limit their clinical applicability. To overcome existing limitations, we present a novel deep learning architecture for interpretable ECG analysis (xECGArch). For the first time, short- and long-term features are analyzed by two independent convolutional neural networks (CNNs) and combined into an ensemble, which is extended by methods of explainable artificial intelligence (xAI) to whiten the blackbox. To demonstrate the trustworthiness of xECGArch, perturbation analysis was used to compare 13 different xAI methods. We parameterized xECGArch for atrial fibrillation (AF) detection using four public ECG databases (n=9854\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n = 9854$$\\end{document} ECGs) and achieved an F1 score of 95.43% in AF versus non-AF classification on an unseen ECG test dataset. A systematic comparison of xAI methods showed that deep Taylor decomposition provided the most trustworthy explanations (+24%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$+24\\%$$\\end{document} compared to the second-best approach). xECGArch can account for short- and long-term features corresponding to clinical features of morphology and rhythm, respectively. Further research will focus on the relationship between xECGArch features and clinical features, which may help in medical applications for diagnosis and therapy.

Evaluating performance of novel similarity measures of Pythagorean fuzzy sets and their applications in pattern recognition and medical diagnosis

Evaluating performance of novel similarity measures of Pythagorean fuzzy sets and their applications in pattern recognition and medical diagnosis

Semi-liquid metal-based highly permeable and adhesive electronic skin inspired by spider web

Soft and stretchable electronics have garnered significant attention in various fields, such as wearable electronics, electronic skins, and soft robotics. However, current wearable electronics made from materials like conductive elastomers, hydrogels, and liquid metals face limitations, including low permeability, poor adhesion, inadequate conductivity, and limited stretchability. These issues hinder their effectiveness in long-term healthcare monitoring and exercise monitoring. To address these challenges, we introduce a novel design of web-droplet-like electronics featuring a semi-liquid metal coating for wearable applications. This innovative design offers high permeability, excellent stretchability, strong adhesion, and good conductivity for the electronic skin. The unique structure, inspired by the architecture of a spider web, significantly enhances air permeability compared to commercial breathable patches. Furthermore, the distribution of polyborosiloxane mimics the adhesive properties of spider web mucus, while the use of semi-liquid metals in this design results in remarkable conductivity (9 × 106 S/m) and tensile performance (up to 850% strain). This advanced electronic skin technology enables long-term monitoring of various physiological parameters and supports machine learning recognition functions with unparalleled advantages. This web-droplet structure design strategy holds great promise for commercial applications in medical health monitoring and disease diagnosis.

Rough Fermatean Neutrosophic Sets and its Applications in Medical Diagnosis

Rough Fermatean Neutrosophic Sets and its Applications in Medical Diagnosis

Single-step batch fabrication of microfluidic paper-based analytical devices with a 3D printer and their applications in nanoenzyme-enhanced visual detection of dopamine.

Wax printing is the most widely used method for fabricating microfluidic paper-based analytical devices (μPADs), but it still suffers from disadvantages like discontinuation of wax printers and need for additional equipment for heating treatment. To address these issues, this work initially describes a new class of wax printing approach for high-precision, batch fabrication of μPADs using a household 3D printer. It only involves a one patterning step of printing polyethylene wax into rice paper body. Under optimized parameters, a fabrication resolution, namely the minimum hydrophilic channel width, down to ~189 ± 30 μm could be achieved. In addition, the analytical applicability of such polyethylene wax-patterned μPADs was demonstrated well with enhanced colorimetric detection of dopamine as a model analyte by combining metal-organic framework (MOF) based nanoenzymes (ZIF-67) with a smartphone (for portable quantitative readout). The developed nanosensor could linearly detect dopamine over a concentration range from 10 to 1000 μM, with a detection limit of ca. 2.75 μM (3σ). The recovery results for analyzing several real samples (i.e., pig feed, chicken feed, pork and human serum) were between 91.82 and 102.79%, further validating its good detection accuracy for potential practical applications in food safety and medical diagnosis.

Fuzzy Soft Set Theory Applications in Medical Diagnosis: A Comprehensive Review and the Roadmap for Future Studies

This paper provides a literature review addressing the use of soft sets in medical diagnosis. Distinguishing itself from the existing literature, the study offers a comprehensive analysis of how fuzzy soft sets can be integrated into diagnostic processes, highlighting a novel fusion of fuzzy and soft sets in medical applications. Any soft set on a countably infinite universe can be regarded as a fuzzy set, recognizing the limitations of traditional diagnostic tools in dealing with vague and incomplete information, our research aims to utilize the flexibility and comprehensiveness of fuzzy soft sets to enhance decision-making accuracy in medical scenarios. The primary objective of this research is to present a thorough and critical analysis of fuzzy soft set theory in medical diagnosis, aiming to establish it as a fundamental approach in the field. By combining all of these results, we can generate a comprehensive picture of the connections between the many theories that account for fuzziness and imprecision, which helps to fill in the blanks left by recent surveys. The review will focus on identifying the main research trends in this field: the primary research topics that soft set theory addresses in medical applications, the community is currently facing and the major theoretical concepts used to study these topics. The primary objective of this review is to assist in the identifying of emerging research directions. Some of these trends are promising and will help shape a new role for soft set theory in the area of medical applications. The fusion of fuzzy and soft sets in medical applications represents a crucial and necessary stage in the research and diagnosis procedures. Key results include the study on development of algorithms and models that outperform traditional methods in accuracy and reliability.

Three-way decisions with dual hesitant fuzzy covering-based rough set and their applications in medical diagnosis

As an extension of partition, fuzzy β-covering can provide a more realistic and accurate description of incomplete information. In this paper, we mainly integrate the idea of fuzzy β-covering with dual hesitant fuzzy (DHF) information and construct some novel three-way decision (3WD) models with DHF covering-based rough set. Firstly, we propose the notions of DHF β-covering, DHF β-minimal description and DHF β-maximal description, and then construct three types of DHF neighborhood operators. Meanwhile, we introduce DHF conditional probability by using DHF neighborhood operator. Secondly, in terms of DHF conditional probability, DHF covering-based probabilistic rough set, DHF covering-based decision-theoretic rough set and their 3WD models are established. In light of DHF neighborhood operator, we propose two pairs of optimistic and pessimistic DHF decision evaluation functions and build two 3WD models through them. Lastly, a numerical example is employed to elaborate the application of the above models, which is effective and credible to medicine screening for Alzheimer’s disease.

Fabrication of Biomedical Electrodes Using Printing Approaches

AbstractIn this review, we focus on the fabrication of electrodes, using printing techniques. Generally speaking, electrodes are comprised of a metal conductor with a metal lead (sometimes the same material) for the conduction of electrical current. Different inorganic and organic materials including metal, polymers, carbon, as well as their composites thereof, have been used for electrodes on different substrates. While material-dependent characteristics, including conductivity, optical transparency, corrosion resistance and biocompatibility, determine the choice of material, printing, as the manufacturing method, offers precise control over the geometry and scale of electrodes for selective and sensitive performance. Both 2D- and 3D-printed electrodes have been widely used as sensors for electrochemical applications as well as quantification of biological compounds, establishing contact with biological surfaces and systems, finding application in medical diagnosis, therapy and treatment of various conditions. Costa et al. (Energy Storage Mater 28:216–234, 2020), Mensing et al. (Sustain Mater Technol 25:e00190, 2020) depict the difference between the 2D and 3D printing techniques which can be used for fabrication of 2D and 3D electrodes. The 3D structure of the electrode provides advantages over the 2d electrodes in terms of its catalytic properties through enhancement in its mass transfer process, adsorption efficiency and active exposure sites (Mensing et al. in Sustain Mater Technol 25:e00190, 2020).

Three-way decision method based on triangular norms in incomplete information systems and its applications in medical diagnosis

Three-way decision, as an extension of traditional two-way decision, was proposed by Yao in 2009, which can effectively avoid unnecessary losses caused by incorrect decisions in the decision-making process. Meanwhile, incomplete hybrid information systems represent the database of the relationship between objects and attributes, which refers to a system with multiple data and missing data. In this paper, based on the fact that many existing literatures involving incomplete hybrid information systems did not fully consider the impact of different conditional attributes on decision attributes and lacked effective aggregation methods to integrate weights and distances, we propose a new three-way decision method to deal with incomplete hybrid information systems with the help of triangular norms. First, in incomplete hybrid information systems, we redefine the distance between two objects based on conditional attributes and give the calculation formula of different data attributes in conditional attributes. At the same time, we define a new weight calculation method based on the relationship between conditional attributes and decision attributes. And then, by the distance between different conditional attributes and their corresponding weights, the hybrid distance is obtained using triangular norms. Furthermore, we use the hybrid distance to get the tolerance relation on the target set of an incomplete hybrid information system, thereby, using this tolerance relation, we get a new decision theoretic rough set model and the corresponding decision rules. Finally, by two experiments involving medical diagnosis, we demonstrate that our model has better classification ability, lower misclassification rate, and better stability compared to other corresponding models, thereby confirming that the proposed model provides a new and effective method for handling incomplete hybrid information systems in practical applications.