Applications In Medical Diagnosis Research Articles

Stable Polyhedron-Cluster-Based Rigid Fluoride Framework toward Zero-Thermal-Quenching Near-Infrared Emission for Prolonged LED Applications.

Development of highly thermally stable broadband near-infrared (NIR) luminescence materials is crucial for advancing the prolonged stable application of smart NIR light sources. In this study, a zero-thermal-quenching and reversible temperature-dependent broadband NIR-emitting Cs2NaAl3F12:Cr3+ phosphor is demonstrated, benefiting from its stable polyhedron-cluster-building rigid structure. The excellent thermal stability of Cs2NaAl3F12:Cr3+ is rooted in its stable [Al6Na4F45] cluster building unit, which provides a rigid structure with a weak electron-phonon coupling effect and a wide band gap with a huge thermal activated barrier. Such characteristics are well revealed by multiple studies on crystal structure, electronic structure, Huang-Rhys factor S, configuration coordinate model, and Debye temperature. The incorporation of Li or K instead of Na weakens the luminescence thermal stability, directly proving the importance of the stable [Al6Na4F45] cluster for stable Cr3+ substitution and rigid structure construction. Furthermore, Cs2NaAl3F12:Cr3+ presents much superior thermal stability compared to traditional rigid garnet-type fluorides Na3X2Li3F12:Cr3+ (X = Al, Ga, In). A high-power NIR LED is presented, utilizing the high quantum efficiency (∼71%) and extremely thermally stable broadband NIR emission around 750 nm of Cs2NaAl3F12:Cr3+. It realizes clear vein and cartilage imaging in the human hand, demonstrating its potential in medical diagnosis applications. This result provides important insights for designing new-type rigid crystal structures using stable polyhedron clusters as basic units, advancing the development of highly thermally stable NIR-emitting phosphors.

Development of a novel electrochemical biosensor based on plastic antibodies for detection of STEAP1 biomarker in cancer

STEAP1 is a cell surface protein of the STEAP family whose main function focuses on intercellular communication and cell growth. STEAP1 is considered a promising putative biomarker and a candidate target for prostate cancer treatment.For specific and selective detection of STEAP1, a molecularly imprinted polymers (MIP) was developed on a screen-printed electrode (C-SPE) whose surface was modified with a nanocomposite based on carbon nanotubes decorated with dendritic platinum nanoparticles (CNTs- PAH /Pt). Then, the MIPs were produced on the modified C-SPE by electropolymerization of a mixture of STEAP1 and a monomer (pyrrole-2-carboxylic acid). Then, the protein was removed from the polymeric network by enzymatic treatment with trypsin, which created the specific template cavities for further STEAP1 detection. Electrochemical techniques such as EIS and CV were used to follow the chemical modification steps of C-SPE. The analytical performance of the biosensor was evaluated by SWV in PBS buffer and in lysates of neoplastic prostate cancer cells (LNCaP) extracts. The MIP material showing a linear range from 130 pg/ml to 13 µg/ml. Overall, the biosensor exhibits essential properties such as selectivity, sensitivity and reproducibility for its application in medical and clinical research diagnosis and/or prognosis of prostate cancer.

Nonlinear ultrasound in liquid containing multiple coated microbubbles: effect of buckling and rupture of viscoelastic shell on ultrasound propagation

With promising applications in medical diagnosis and therapy, the behavior of shell-encapsula-ted ultrasound contrast agents (UCAs) has attracted considerable attention. Currently, second-generation contrast agents stabilized by a phospholipid membrane are widely used and studies have focused on the dynamics of single phospholipid shell-encapsulated microbubbles. To improve the safety and the efficiency of the methods using the propagation or targeted ultrasound, a better understanding of the propagation of ultrasound in liquids containing multiple encapsulated microbubbles is required. By incorporating the Marmottant–Gompertz model into the multiple scale analysis of two-phase model, this study derived a Korteweg–de Vries–Burgers equation as a weakly nonlinear wave equation for one-dimensional ultrasound in bubbly liquids. It was found that the wave propagation characteristics changed with the initial surface tension, highlighting two notable features of the phospholipid shell: buckling and rupture. These results may provide insights into the suitable state of microbubbles, and better control of ultrasound for medical applications, particularly those that require high precision.

An ultra-high sensitivity methane gas sensor based on Vernier effect in two parallel optical fiber Sagnac loops

Methane gas sensing has attracted great interest for its applications in agriculture production, industrial manufacturing, medical diagnosis, and environmental monitoring. In this paper, we proposed an ultra-high sensitivity methane gas sensor based on Vernier effect in two parallel optical fiber Sagnac loops. The reference arm contained a polarization maintaining photonic crystal fiber (PM-PCF), while sensing arm consists of a methane gas sensitive film coated PM-PCF. Vernier effect (VE) was generated and as a result the sensitivity was improved by multiple times. The finite element method (FEM) was used to assess the methane gas sensor’s sensing capabilities. According to the numerical simulation, the average sensitivity based on two parallel Sagnac loops in the methane gas concentration of 0-3.5% was 197.775 nm/%, which was 5.5 times in contrast to a single Sagnac loop. To our best knowledge, we presented the methane gas sensor based on VE in two parallel optical fiber Sagnac loops for the first time. The designed optical fiber methane gas sensor possessed the merits of ultra-high sensitivity, providing helpful guidelines for the fabrication of high-performance methane gas sensors.

Classification of Images of Skin Lesion Using Deep Learning

Skin cancer is among common and rapidly increasing human malignancies, which can be diagnosed visually. The diagnosis begins with preliminary medical screening and by dermoscopic examination, histopathological examination, and proceeding to the biopsy. This screening and diagnosis can be automated using machine learning tools and techniques. Artificial neural networks are helping a lot in medical diagnosis applications. In this research, skin images are classified into 7 different classes of skin cancer using deep learning methodology, then analyzed the results w.r.t to their respective precision, recall, support, and accuracy to find its practical applicability. This model is efficient in comparison to the detection of skin cancer with human eyes. Human eyes detection can be 79% accurate at most. Thus, having a scientific method of diagnosis can help the doctors and practitioners to accurately identify the cancer and its type. The model provides 80% accuracy on average for all 7 types of skin diseases, thus being more reliable than human eye examination. It will help the doctors to diagnose the skin diseases more confidently. The model has only 2 misclassified predictions for Basal cell carcinoma and Vascular lesions. However, Actinic keratosis diagnosis is most accurately predicted.

Picture Fuzzy Soft Prioritized Aggregation Operators and Their Applications in Medical Diagnosis

A medical diagnosis is one the most efficient processes of determining a disease based on a person’s symptoms and signs. In recent days, due to the complexities of the same type of diseases, it is very difficult to diagnose a disease by using old methods and techniques. In this way, new and efficient medical diagnosis methods can help a lot in reaching an accurate conclusion, depending upon the timing and sequences of symptoms and medical history. The physician relies on other clues like medical tests and imaging tests. So, in this way, a list of possible diagnoses can be determined, which are referred to as different diagnoses. To handle these types of issues in this manuscript, additional information is identified, and possible disease is confirmed. Under the consideration of classical data, it is a very difficult task to deal with complex and asymmetric sorts of data. Fuzzy set theory has a wide range of applications, from engineering to the medical field. Different methods and techniques have been proposed to support the decision-making process in medical fields. Picture fuzzy soft sets are more generalized structures and efficient tools to formalize the information more decently and accurately. So, devoted from this notion, in this article based on picture fuzzy soft settings, we firstly have established some basic operational laws for picture fuzzy soft number; then based on these operational laws, we have developed some aggregation operators named as picture fuzzy soft prioritized average and geometric aggregation operators. In real-world problems, these operators can be useful in analyzing uncomfortable and asymmetric information. Furthermore, some basic properties of the introduced operators have been initiated and discussed briefly. Moreover, to show the effective use of this developed approach to medical diagnoses, we have proposed an algorithm, along with a descriptive example. Additionally, a comparative analysis of the proposed work shows the superiority and effectiveness of the introduced approach.

Some New Correlation Coefficient Measures Based on Fermatean Fuzzy Sets using Decision Making Approach in Pattern Analysis and Supplier Selection

Fermatean fuzzy set (FFS) is an effective tool to depict expert reasoning information in the decision‐making process than fuzzy sets (FS), intuitionistic fuzzy sets (IFS), and Pythagorean fuzzy sets (PFS). Keeping in mind the importance of correlation coefficient and application in medical diagnosis, decision making and pattern recognition, several studies on correlation coefficient measures have been proposed in the literature. As there does not exist any study concerning correlation coefficient measures for FFS, in this communication, we propose novel entropy-correlation measures for Fermatean fuzzy sets and applied it decision making problems of pattern analysis and multi-criteria decision making for supplier selection. With the help of proposed correlation coefficient, we establish some weighted measures for FFS. Using numerical computations, we determine the efficacy of the suggested measures over other measures. The aim of this study is to propose a novel and efficient methodology for evaluation of supplier’s selection with uncertain information. Finally, we establish the comparative study of our developed measures over the existing correlation coefficient measures. The analysis showed that the suggested methodology is reliable, flexible, and consistent with the existing techniques.

Tongue Color Analysis and Diseases Detection Based on a Computer Vision System

The tongue reflects the abnormal condition and behavior of the internal organs of the body, such as problems of the heart, liver, pancreas, stomach, intestines, blood diseases and others, which lead to changes in some of the features and characteristics of the tongue. The most important of these is tongue color, which can be adopted as a biometric that can be used in Computerized Tongue Diagnostic Systems (CTDS). Quantitative diagnosis of the tongue requires some devices, including image acquisition devices such as cameras, light sources, filters, color checkers, image analysis and processing devices through the application of some algorithms or image processing and color correction software, as well as a computer. This study proposes a real-time imaging system to analyze tongue color and diagnose diseases using a webcam under specific conditions. The proposed system was designed in a Matlab GUI environment. After testing the system on a data set of more than 100 images, the preliminary results showed that the proposed system gives a disease diagnosis with an accuracy rate of no less than 86.667%. The proposed system contributed to the diagnosis of several diseases in real time, with an accuracy of 95.45%, with ease of use, implementation and low cost. This gives impetus to further studies to apply computerized diagnosis in medical applications, to enhance the medical reality, monitor patient health, and make an accurate diagnosis.

Nucleic Acid Amplification by Template-Dominated Click Chemistry for Ultrasensitive DNA/RNA Detection on an Electrochemical Readout Platform.

As an enzyme-free exponential nucleic acid amplification method, the click chemistry-mediated ligation chain reaction (ccLCR) has shown great prospects in the molecular diagnosis. However, the current optics-based ccLCR is challenged by remarkable nonspecific amplification, severely hindering its future application. This study demonstrated that the severe nonspecific amplification was generated probably due to high random collision in the high DNA probe concentration (μM level). To solve this hurdle, a nucleic acid template-dominated ccLCR was constructed using nM-level DNA probes and read on an electrochemical platform (cc-eLCR). Under the optimal conditions, the proposed cc-eLCR detected a low-level nucleic acid target (1 fM) with a single-base resolution. Furthermore, this assay was applied to detect the target of interest in cell extracts with a satisfactory result. The proposed cc-eLCR offers huge possibility for click chemistry-mediated enzyme-free exponential nucleic acid amplification in the application of medical diagnosis and biomedical research.

Generalization of negation of a probability distribution

Negation is absolutely essential in all conversations that allows for denial, contradiction and other countering aspects of human linguistic systems. Intuitively, the negative sentences are less informative and less specific than the affirmative ones. In other words, it has more uncertainty embedded in it. If an event is uncertain, negating it will require some sort of probabilistic formulation. Yager (IEEE Trans Fuzzy Syst 25:1899–1902, 2015) gave a probabilistic definition of negation of a probability distribution based on unbiased distribution of probabilities. In the present work, a generalization of negation based on the concept of biased distribution is proposed. Some numerical examples have been discussed for determining the validity and efficacy of the proposed generalization. Finally an application in medical diagnosis has been considered.

A Medical Image Segmentation Method Based on Improved UNet 3+ Network.

In recent years, segmentation details and computing efficiency have become more important in medical image segmentation for clinical applications. In deep learning, UNet based on a convolutional neural network is one of the most commonly used models. UNet 3+ was designed as a modified UNet by adopting the architecture of full-scale skip connections. However, full-scale feature fusion can result in excessively redundant computations. This study aimed to reduce the network parameters of UNet 3+ while further improving the feature extraction capability. First, to eliminate redundancy and improve computational efficiency, we prune the full-scale skip connections of UNet 3+. In addition, we use the attention module called Convolutional Block Attention Module (CBAM) to capture more essential features and thus improve the feature expression capabilities. The performance of the proposed model was validated by three different types of datasets: skin cancer segmentation, breast cancer segmentation, and lung segmentation. The parameters are reduced by about 36% and 18% compared to UNet and UNet 3+, respectively. The results show that the proposed method not only outperformed the comparison models in a variety of evaluation metrics but also achieved more accurate segmentation results. The proposed models have lower network parameters that enhance feature extraction and improve segmentation performance efficiently. Furthermore, the models have great potential for application in medical imaging computer-aided diagnosis.

Research Advances on Fiber-Optic SPR Sensors with Temperature Self-Compensation.

The fiber-optic surface plasmon resonance sensor has very promising applications in environmental monitoring, biochemical sensing, and medical diagnosis, due to the superiority of high sensitivity and novel label-free microstructure. However, the influence of ambient temperature is inevitable in practical sensing applications, and even the higher the sensitivity, the greater the influence. Therefore, how to eliminate temperature interference in the sensing process has become one of the hot issues of this research field in recent years, and some accomplishments have been achieved. This paper mainly reviews the research results on temperature self-compensating fiber-optic surface plasmon sensors. Firstly, it introduces the mechanism of a temperature self-compensating fiber-optic surface plasmon resonance sensor. Then, the latest development of temperature self-compensated sensor is reviewed from the perspective of various fiber-optic sensing structures. Finally, this paper discusses the most recent applications and development prospects of temperature self-compensated fiber-optic surface plasmon resonance sensors.

Piezoresistive Pressure Sensor Based on a Conductive 3D Sponge Network for Motion Sensing and Human-Machine Interface.

Flexible sensors have attracted increasing attention owing to their important applications in human activity monitoring, medical diagnosis, and human-machine interaction. However, the rational design of low-cost sensors with desirable properties (e.g., high sensitivity and excellent stability) and extended applications is still a great challenge. Herein, a simple and cost-effective strategy is reported by immersing polyurethane (PU) sponge in graphene oxide solution followed by in situ chemical reduction to construct a reduced graphene oxide (RGO)-wrapped PU sponge sensor. Ascribed to the excellent compressive resilience of PU sponge and an electrically conductive RGO layer, the constructed flexible sensor exhibits satisfactory sensing performance with high sensitivity (17.65 kPa-1) in a low-load range (0-3.2 kPa), a wide compression strain range (0-80%), and reliable stability (8000 cycles). In addition, these sensors can be successfully applied to monitor human movements and identify the weight of objects. Through the use of a sensor array integrated with a signal acquisition circuit, the reasonably designed sensors can realize tactile feedback via mapping real-time spatial distribution of pressure in complicated tasks and show potential applications in flexible electronic pianos, electronic skin, and remote real-time control of home electronics.

Programming an antibody-peptide sandwich procedure for selectivity and ultrasensitive detection of fibrin using double-stranded DNA (dsDNA) hemp string probe

A novel, highly sensitive and selective sandwich-like enzyme-linked immunoassay procedure for the detection of fibrin was developed. Firstly the monoclonal anti-fibrinogen which was coated on the surface of microtiter plate can interact with fibrin, and the probe of double-stranded DNA (dsDNA) hemp string containing the homing peptide can secondarily recognize the fibrin. On the other hand, combined with the hybridization chain reaction (HCR), the terminal of dsDNA can be folded to a long G-quadruplex-hemin DNAzyme, which can catalyze luminol–H2O2 system to generate strong chemiluminescence signal. Under the optimal experimental conditions, the assay showed a linear toward fibrin concentration in the range of 0.14–72.5 pM with detection limit of 0.04 pM. Because of high selectivity and ultra-sensitivity, the designed procedure provides great potential for applications in medical diagnosis.

Biomedical microwave-induced thermoacoustic imaging

Microwave thermoacoustic imaging (MTAI) is an exciting imaging technique rooted from the underlying principle of exploiting the distinct electrical properties of biological tissues. By using short-pulsed microwaves as a stimulation source and their interaction with the human body, MTAI has paved the way for revolutionary advancements in medical imaging. When microwaves are absorbed by polar molecules and ions within the tissues, an ingenious thermoelastic effect gives rise to ultrasound waves. These ultrasound waves, brimming with invaluable pathological and physiological insights, propagate outward, carrying the essence of the composition and functionality of biological tissue. Through a meticulous collection of ultrasound signals from all directions surrounding the tissue, it becomes possible to reconstruct intricate internal structures and visualize the tissue's functional dynamics. The MTAI excels in non-invasiveness, capable of delving several centimeters beneath the surface with a microscopic resolution on the order of micrometers. The magic lies in converting microwave energy into ultrasound waves, entering into the hidden depths of tissues without causing harm. This groundbreaking imaging modality unlocks a realm of possibilities for acquiring profound insights into the intricate structures and functionality of deep-seated tissues. Furthermore, the inherent polarization characteristics of microwaves empower MTAI to capture additional dimensions of information, unraveling the intricate polarization properties and illuminating a richer understanding of the tissue's complexity. The great potential of MTAI extends far and wide within the medicine field. It has made remarkable achievements in non-invasive imaging of brain structures, screening breast tumors, visualizing human arthritis, and detecting liver fat content. These accomplishments have laid a solid foundation, firmly establishing MTAI as a trailblazing medical imaging technique. The present study offers a comprehensive and in-depth exploration of the physical principles underpinning MTAI, the sophisticated system devices involved, and the recent groundbreaking research breakthroughs. Moreover, it delves into the exciting prospects and challenges that lie ahead in the future development of MTAI. As the technology continues to progress by leaps and bounds, MTAI is ready to break down barriers, and usher in a new era of unmatched imaging quality and performance. This, in turn, will open the floodgates for transformative innovations and applications in medical diagnosis and treatment. The anticipation is palpable as MTAI strives to make substantial contributions to the ever-developing medical imaging field, bestowing upon humanity more accurate, reliable, and life-enhancing diagnostic capabilities.

Confidence Level Aggregation Operators Based on Intuitionistic Fuzzy Rough Sets With Application in Medical Diagnosis

In recent days, due to the complexities of different diseases of similar types, it becomes very difficult to diagnose an accurate type of disease, and so medical diagnosis becomes a difficult task for the experts working in health departments. Many researchers try to develop new methods and techniques to over the difficulties that come across in the way of medical diagnosis. In this paper, we try to develop some novel techniques that will help experts to diagnose diseases accurately. Based on a more advanced structure of intuitionistic fuzzy rough sets, in this article, we establish confidence-level intuitionistic fuzzy average/geometric aggregation operators to incorporate the familiarity degree of experts with evaluated objects for an initial assessment while intuitionistic fuzzy rough average/geometric aggregation operators cannot do so. Moreover, we have given some basic properties of the initiated operators. To show the effective use of these operators we have proposed an algorithm with an illustrative example. Furthermore, based on the intuitionistic fuzzy rough model, we have also established a medical diagnosis model to incorporate the difficulty that occurs in the diagnosis of disease. Furthermore, a comparative analysis demonstrates the efficiency of our proposed methods.

Application and development of augmented reality in the medical field

This paper provides a systematic review of augmented reality (AR) technologies and their applications in medical diagnosis and treatment from 1988-2023. The paper is categorised by key technologies of augmented reality and medical diagnosis and treatment applications. For AR key technologies, 67 articles were researched. Among them, AR key technologies can be divided into three methods: display, calibration and tracking, with video perspective display technology dominating the field. For calibration technologies, 17 articles were included in the review, and we discussed methods based on graphic markers, optical markers and markerless calibration, respectively; for positional tracking technologies, 15 articles were counted, and we discussed methods based on markers, simultaneous localisation and map building, and optical flow-based tracking, respectively; for application areas of AR technologies, simulation teaching and training, surgical guidance and telemedicine were included in the review Scope.

Distance measures of picture fuzzy sets and interval-valued picture fuzzy sets with their applications

<abstract><p>Picture fuzzy sets (PFSs) are a versatile generalization of fuzzy sets and intuitionistic fuzzy sets (IFSs), providing a robust framework for modeling imprecise, uncertain, and inconsistent information across various fields. As an advanced extension of PFSs, interval-valued picture fuzzy sets (IvPFSs) offer superior capabilities for handling incomplete and indeterminate information in various practical applications. Distance measures have always been an important topic in fuzzy sets and their variants. Some existing distance measures for PFSs have shown limitations and may yield counterintuitive results under certain conditions. Furthermore, there are currently few studies on distance measures for IvPFSs. To solve these problems, in this paper we devised a series of novel distance measures between PFSs and IvPFSs inspired by the Hellinger distance. Specifically, all the distance measures were divided into two parts: One considered the positive membership degree, neutral membership degree and negative membership degree, and the other added the refusal membership degree. Moreover, the proposed distance measures met some important properties, including boundedness, non-degeneracy, symmetry, and consistency, but also showed superiority compared to the existing measures, as confirmed through numerical comparisons. Finally, the proposed distance measures were validated in pattern recognition and medical diagnosis applications, indicating that the proposed distance measures can deliver credible, reasonable results, particularly in similar cases.</p></abstract>

A novel distance measure for intuitionistic fuzzy sets and its application in pattern classification, medical diagnosis, and career determination

A novel distance measure for intuitionistic fuzzy sets and its application in pattern classification, medical diagnosis, and career determination

ECA-TFUnet: A U-shaped CNN-Transformer network with efficient channel attention for organ segmentation in anatomical sectional images of canines.

Automated organ segmentation in anatomical sectional images of canines is crucial for clinical applications and the study of sectional anatomy. The manual delineation of organ boundaries by experts is a time-consuming and laborious task. However, semi-automatic segmentation methods have shown low segmentation accuracy. Deep learning-based CNN models lack the ability to establish long-range dependencies, leading to limited segmentation performance. Although Transformer-based models excel at establishing long-range dependencies, they face a limitation in capturing local detail information. To address these challenges, we propose a novel ECA-TFUnet model for organ segmentation in anatomical sectional images of canines. ECA-TFUnet model is a U-shaped CNN-Transformer network with Efficient Channel Attention, which fully combines the strengths of the Unet network and Transformer block. Specifically, The U-Net network is excellent at capturing detailed local information. The Transformer block is equipped in the first skip connection layer of the Unet network to effectively learn the global dependencies of different regions, which improves the representation ability of the model. Additionally, the Efficient Channel Attention Block is introduced to the Unet network to focus on more important channel information, further improving the robustness of the model. Furthermore, the mixed loss strategy is incorporated to alleviate the problem of class imbalance. Experimental results showed that the ECA-TFUnet model yielded 92.63% IoU, outperforming 11 state-of-the-art methods. To comprehensively evaluate the model performance, we also conducted experiments on a public dataset, which achieved 87.93% IoU, still superior to 11 state-of-the-art methods. Finally, we explored the use of a transfer learning strategy to provide good initialization parameters for the ECA-TFUnet model. We demonstrated that the ECA-TFUnet model exhibits superior segmentation performance on anatomical sectional images of canines, which has the potential for application in medical clinical diagnosis.