Applications In Medical Diagnosis Research Articles

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.

Compact meta-differentiator for achieving isotropically high-contrast ultrasonic imaging

Ultrasonic imaging is crucial in the fields of biomedical engineering for its deep penetration capabilities and non-ionizing nature. However, traditional techniques heavily rely on impedance differences within objects, resulting in poor contrast when imaging acoustically transparent targets. Here, we propose a compact spatial differentiator for underwater isotropic edge-enhanced imaging, which enhances the imaging contrast without the need for contrast agents or external physical fields. This design incorporates an amplitude meta-grating for linear transmission along the radial direction, combined with a phase meta-grating that utilizes focus and spiral phases with a first-order topological charge. Through theoretical analysis, numerical simulations, and experimental validation, we substantiate the effectiveness of our technique in distinguishing amplitude objects with isotropic edge enhancements. Importantly, this method also enables the accurate detection of both phase objects and artificial biological models. This breakthrough creates new opportunities for applications in medical diagnosis and nondestructive testing.

Fuzzy reasoning method based on picture similarity measure and its application in medical diagnosis

In previous studies, based on picture fuzzy sets, full implication algorithm and quintuple implication principle have been studied. The problem is, full implication algorithm did not consider the relationship between the input and antecedent of rule, while quintuple implication principle only considered the inclusion relationship between the rule’s antecedent and input. Both algorithms did not consider the similarity of rule’s antecedent and input. This leads to unreasonable calculation results in some cases. Therefore, we study fuzzy reasoning methods based on picture similarity measure for generalized modus ponens and generalized modus tollens in this paper. First of all, based on picture biresiduation, a new picture similarity measure between picture fuzzy sets is constructed. Then, representations of solutions of fuzzy reasoning methods based on picture similarity measure are given. Moreover, reducibility and robustness of fuzzy reasoning methods based on picture similarity measure are studied. Finally, two issues related to medical diagnosis are addressed by the proposed methods, demonstrate the rationality and effectiveness of our suggested methods.

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.

Weighted probability kernel multi-granularity three-way decision integrating GRA and its application in medical diagnosis

Three-way decision, an outstanding method to handle decision-making uncertainties, relies on essentially the loss functions derived from the Bayesian risk decision process. Actually, there are plentiful loss functions that depend on the subjective judgment of decision-makers under different decision scenarios, lacking uniform and objective measurement frameworks. This study pays attention to the real clinical diagnosis, and constructs a weighted probability kernel multi-granularity three-way decision method (WKMG-TWD) integrating grey relation analysis (GRA) over a multi-source heterogeneous decision information system (MHDIS). The method establishes a standardized data-driven calculation framework of loss functions. Foremost, the multi-kernel probabilistic similarity is defined and granularity's weights with knowledge consistency are explored. Subsequently, a weighted probability kernel multi-granularity rough set (WKMGRS) is constructed in this paper. Secondly, to introduce the three-way decision, this study proposes the cost-sensitive individual loss functions considering the correlation determined by GRA between decision objects and different decision classes. Ultimately, this study establishes and applies a three-way iterative classification model to hypertension diagnosis. The experimental results confirm the effectiveness and superiority of the model. The main contribution of this paper is twofold. One is to offer a uniform calculation framework for loss functions and granularity's weights. The other is to furnish invaluable guidance for solving complex medical decision-making problems.

Gold and Silver Nanoparticles as Biosensors: Characterization of Surface and Changes in the Adsorption of Leucine Dipeptide under the Influence of Substituent Changes.

Early detection of diseases can increase the chances of successful treatment and survival. Therefore, it is necessary to develop a method for detecting or sensing biomolecules that cause trouble in living organisms. Disease sensors should possess specific properties, such as selectivity, reproducibility, stability, sensitivity, and morphology, for their routine application in medical diagnosis and treatment. This work focuses on biosensors in the form of surface-functionalized gold (AuNPs) and silver nanoparticles (AgNPs) prepared using a less-time-consuming, inexpensive, and efficient synthesis route. This allows for the production of highly pure and stable (non-aggregating without stabilizers) nanoparticles with a well-defined spherical shape, a desired diameter, and a monodisperse distribution in an aqueous environment, as confirmed by transmission electron microscopy with energy-dispersive X-ray spectroscopy (TEM-EDS), X-ray diffraction (XRD), photoelectron spectroscopy (XPS), ultraviolet-visible (UV-VIS) spectroscopy, and dynamic light scattering (DLS). Thus, these nanoparticles can be used routinely as biomarker sensors and drug-delivery platforms for precision medicine treatment. The NPs' surface was coated with phosphonate dipeptides of L-leucine (Leu; l-Leu-C(R1)(R2)PO3H2), and their adsorption was monitored using SERS. Reproducible spectra were analyzed to determine the orientation of the dipeptides (coating layers) on the nanoparticles' surface. The appropriate R2 side chain of the dipeptide can be selected to control the arrangement of these dipeptides. This allows for the proper formation of a layer covering the nanoparticles while also simultaneously interacting with the surrounding biological environment, such as cells, tissues, and biological fluids.

Graphene Aerogel‐Based Pressure Sensors with Gradient Structure for Subtle Physiological Signals and Motion Monitoring

AbstractHigh‐performance flexible wearable pressure sensors require the simple preparation method coupled with a well‐designed structure. Graphene aerogel has garnered significant attention as a flexible pressure sensor due to its exceptional features such as high porosity, favorable compressibility, and fine conductivity. However, it remains challenging to precisely control the 3D structure of the graphene aerogel within the sensors, which limits the detection sensitivity and application range. Here, a freeze‐assisted transfer printing strategy (FATPS) is proposed for fabricating a high‐performance graphene aerogel‐based sensor with the gradient structures. Compared to sensors prepared using mold methods, the sensitivity of the sensor produced through FATPS can be enhanced by nearly tenfold. Furthermore, the gradient structure empowers the graphene pressure sensor with excellent reliability and rapid response time (50 ms) within the pressure range of 0.1–50 kPa. The sensor exhibits the capability to monitor and recognize various physiological activities, including pulse, vocalization, coughing, swallowing, and finger bending. It is expected that the sensor has remarkable potential applications in medical diagnosis, disability aid, and athletic injury prevention and so on.

Inorganic metal oxide material BaSiO3:Eu2+ for convenient 3D X-ray imaging

X-ray storage phosphors are found wide-ranging applications in medical diagnosis, nondestructive testing and X-ray imaging. However, designing X-ray storage phosphors with high electron storage capacity still poses a daunting challenge. In this study, we designed a fluorescent powder based on Eu2+-doped BaSiO3, demonstrating excellent optical information storage characteristic. Under high-energy X-ray filling, shallow traps exhibit low electron capacity and only produce weak afterglow, which greatly helps to minimize the impact of afterglow on the image quality during X-ray imaging. In addition, the deep traps at 1.01 eV filled with high-energy X-ray allow the material to store information for a long time. The crystal structure, photoluminescence (PL) and thermoluminescence (TL) spectra were studied systematically. The electron trapping properties of the material were analyzed using TL spectroscopy. Finally, we fabricated a flexible film with phosphors, the flexible film has been utilized to demonstrate high-quality real-time X-ray imaging and achieve time-delayed X-ray imaging of curved objects, such as flexible circuit boards.

PrecisionLymphoNet: Advancing Malignant Lymphoma Diagnosis via Ensemble Transfer Learning with CNNs.

Malignant lymphoma, which impacts the lymphatic system, presents diverse challenges in accurate diagnosis due to its varied subtypes-chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL). Lymphoma is a form of cancer that begins in the lymphatic system, impacting lymphocytes, which are a specific type of white blood cell. This research addresses these challenges by proposing ensemble and non-ensemble transfer learning models employing pre-trained weights from VGG16, VGG19, DenseNet201, InceptionV3, and Xception. For the ensemble technique, this paper adopts a stack-based ensemble approach. It is a two-level classification approach and best suited for accuracy improvement. Testing on a multiclass dataset of CLL, FL, and MCL reveals exceptional diagnostic accuracy, with DenseNet201, InceptionV3, and Xception exceeding 90% accuracy. The proposed ensemble model, leveraging InceptionV3 and Xception, achieves an outstanding 99% accuracy over 300 epochs, surpassing previous prediction methods. This study demonstrates the feasibility and efficiency of the proposed approach, showcasing its potential in real-world medical applications for precise lymphoma diagnosis.

A Distance Measure of Fermatean Fuzzy Sets Based on Triangular Divergence and its Application in Medical Diagnosis

Fermatean fuzzy sets (FFSs), as one of the representative variants of fuzzy sets, have broad application prospects. FFSs have advantages in modeling uncertain information and therefore have been widely applied. However, how to perfectly quantify the differences between FFS remains an open question. This paper introduces a new distance measure for FFSs, utilizing triangular divergence. The proposed measure is designed to rectify the limitations in the current measure, offering a more effective solution for analyzing FFSs. Moreover, we demonstrate that the proposed distance measure satisfies some basic properties and further show its effectiveness through several numerical examples. Finally, we explore the performance of the proposed distance measure in a medical diagnosis application, and the results show that the proposed distance measure can well overcome the limitations of the current measure.

Heavy-to-light electron transition enabling real-time spectra detection of charged particles by a biocompatible semiconductor

The current challenge of wearable/implantable personal dosimeters for medical diagnosis and radiotherapy applications is lack of suitable detector materials possessing both excellent detection performance and biocompatibility. Here, we report a solution-grown biocompatible organic single crystalline semiconductor (OSCS), 4-Hydroxyphenylacetic acid (4HPA), achieving real-time spectral detection of charged particles with single-particle sensitivity. Along in-plane direction, two-dimensional anisotropic 4HPA exhibits a large electron drift velocity of 5 × 105 cm s−1 at “radiation-mode” while maintaining a high resistivity of (1.28 ± 0.003) × 1012 Ω·cm at “dark-mode” due to influence of dense π-π overlaps and high-energy L1 level. Therefore, 4HPA detectors exhibit the record spectra detection of charged particles among their organic counterparts, with energy resolution of 36%, (μt)e of (4.91 ± 0.07) × 10−5 cm2 V−1, and detection time down to 3 ms. These detectors also show high X-ray detection sensitivity of 16,612 μC Gyabs−1 cm−3, detection of limit of 20 nGyair s−1, and long-term stability after 690 Gyair irradiation.

Nonlinear optical response of heme solutions.

Heme is the prosthetic group for cytochrome that exists in nearly all living organisms and serves as a vital component of human red blood cells (RBCs). Tunable optical nonlinearity in suspensions of RBCs has been demonstrated previously, however, the nonlinear optical response of a pure heme (without membrane structure) solution has not been studied to our knowledge. In this work, we show optical nonlinearity in two common kinds of heme (i.e., hemin and hematin) solutions by a series of experiments and numerical simulations. We find that the mechanism of nonlinearity in heme solutions is distinct from that observed in the RBC suspensions where the nonlinearity can be easily tuned through optical power, concentration, and the solution properties. In particular, we observe an unusual phenomenon wherein the heme solution exhibits negative optical nonlinearity and render self-collimation of a focused beam at specific optical powers, enabling shape-preserving propagation of light to long distances. Our results may have potential applications in optical imaging and medical diagnosis through blood.

Probing the Excited Electronic Configuration and Associative Excitons in Pyrene‐Based X‐Ray Scintillating MOF Excimer: Bridging the Gap Between Theory and Experiments

AbstractScintillators are essential in converting ionizing X‐rays and γ‐rays into visible light, with applications in medical diagnosis, security inspection, and non‐invasive detection. However, current scintillating materials exhibit unclear structure–function relationships. The performance of scintillators is intricately linked to their electronic band structure, particularly the intrinsic properties of excited states. Therefore, investigating the structural factors affecting scintillation performance is crucial for developing new scintillators. Therefore, Ba‐SMOF 1 ([Ba2(PyTS)(CH3OH)2(H2O)4]n) is synthesized using a pyrene‐based organic motif and metal Ba(II) ions, where PyTS4− refers to pyrene‐1,3,6,8‐tetrasulfonate. For the first time, the excited state of a pyrene‐based X‐ray scintillating metal–organic framework (MOF) is determined through comprehensive all‐electron excitation studies, including in situ experimental electron density and wave‐function analysis. The electron density maps exhibit a continuous overlap, attributed to the associative exciton behaviors among the closely packed PyTS4− linkers. These linkers are identified as the sources of both static and dynamic excimer emissions. The electron density studies reveal the role of excimers in pyrene‐based luminophores, highlighting that interchromophoric interactions are key to enhancing the performance of scintillating MOFs. This discovery opens new avenues for the practical application of scintillating MOFs.

Interpretable Medical Imagery Diagnosis with Self-Attentive Transformers: A Review of Explainable AI for Health Care

Recent advancements in artificial intelligence (AI) have facilitated its widespread adoption in primary medical services, addressing the demand–supply imbalance in healthcare. Vision Transformers (ViT) have emerged as state-of-the-art computer vision models, benefiting from self-attention modules. However, compared to traditional machine learning approaches, deep learning models are complex and are often treated as a “black box” that can cause uncertainty regarding how they operate. Explainable artificial intelligence (XAI) refers to methods that explain and interpret machine learning models’ inner workings and how they come to decisions, which is especially important in the medical domain to guide healthcare decision-making processes. This review summarizes recent ViT advancements and interpretative approaches to understanding the decision-making process of ViT, enabling transparency in medical diagnosis applications.

Plasmonic photoconductive terahertz focal-plane array with pixel super-resolution

AbstractImaging systems operating in the terahertz part of the electromagnetic spectrum are attractive due to their ability to penetrate many opaque materials and provide unique spectral signatures of various chemicals. However, the use of terahertz imagers in real-world applications has been limited by the slow speed, large size, high cost and complexity of present systems, largely due to the lack of suitable terahertz focal-plane array detectors. Here we report a terahertz focal-plane array that can directly provide the spatial amplitude and phase distributions, along with the ultrafast temporal and spectral information of an imaged object. It consists of a two-dimensional array of ~0.3 million plasmonic photoconductive nanoantennas optimized to rapidly detect broadband terahertz radiation with a high signal-to-noise ratio. We utilized the multispectral nature of the amplitude and phase data captured by these plasmonic nanoantennas to image different objects, including super-resolved etched patterns in a silicon substrate and defects in battery electrodes. By eliminating the need for raster scanning and spatial terahertz modulation, our terahertz focal-plane array offers more than a 1,000-fold increase in the imaging speed compared with the state of the art and potentially suits a broad range of applications in industrial inspection, security screening and medical diagnosis, among others.

Artificial Intelligence Application in Medical Diagnosis, Treatment Recommendations and Patient Care

Artificial Intelligence Application in Medical Diagnosis, Treatment Recommendations and Patient Care

Principle and Application of Biophotonic Chips

Contemporarily, biophotonic chips have garnered significant attention from researchers due to their strong immunity to electromagnetic interference, high sensitivity, rapid response rate, and relatively simple structural design. They have found extensive applications in fields such as medical diagnosis, food quality and safety testing, environmental monitoring, and national security. This article provides an overview of the development history, fundamental principles, applications, and fabrication processes of biophotonic chips. The earliest development of biophotonic chips can be traced back to the end of the 20th century. The basic principle involves the interaction of light, utilizing technologies like photonic crystals, surface plasmon resonance, and fluorescence spectroscopy to achieve the detection and analysis of biological molecules. The article briefly describes the fabrication process of biophotonic chips, which involves intricate techniques such as photolithography and electron beam exposure. Furthermore, it analyzes and discusses their widespread applications in medical diagnosis, drug development, and environmental monitoring, among other fields. Lastly, the study evaluates the key challenges present in biophotonic chips and provides an analysis of their future development prospects.

Multi-scale multi-attention network for diabetic retinopathy grading

Objective. Diabetic retinopathy (DR) grading plays an important role in clinical diagnosis. However, automatic grading of DR is challenging due to the presence of intra-class variation and small lesions. On the one hand, deep features learned by convolutional neural networks often lose valid information about these small lesions. On the other hand, the great variability of lesion features, including differences in type and quantity, can exhibit considerable divergence even among fundus images of the same grade. To address these issues, we propose a novel multi-scale multi-attention network (MMNet). Approach. Firstly, to focus on different lesion features of fundus images, we propose a lesion attention module, which aims to encode multiple different lesion attention feature maps by combining channel attention and spatial attention, thus extracting global feature information and preserving diverse lesion features. Secondly, we propose a multi-scale feature fusion module to learn more feature information for small lesion regions, which combines complementary relationships between different convolutional layers to capture more detailed feature information. Furthermore, we introduce a Cross-layer Consistency Constraint Loss to overcome semantic differences between multi-scale features. Main results. The proposed MMNet obtains a high accuracy of 86.4% and a high kappa score of 88.4% for multi-class DR grading tasks on the EyePACS dataset, while 98.6% AUC, 95.3% accuracy, 92.7% recall, 95.0% precision, and 93.3% F1-score for referral and non-referral classification on the Messidor-1 dataset. Extensive experiments on two challenging benchmarks demonstrate that our MMNet achieves significant improvements and outperforms other state-of-the-art DR grading methods. Significance. MMNet has improved the diagnostic efficiency and accuracy of diabetes retinopathy and promoted the application of computer-aided medical diagnosis in DR screening.

An efficient feature selection and explainable classification method for EEG-based epileptic seizure detection

Epilepsy is a prevalent neurological disorder that poses life-threatening emergencies. Early electroencephalogram (EEG) seizure detection can mitigate the risks and aid in the treatment of patients with epilepsy. EEG based epileptic seizure (ES) detection has significant applications in epilepsy treatment and medical diagnosis. Therefore, this paper presents an innovative framework for efficient ES detection, providing coefficient and distance correlation feature selection algorithms, a Bagged Tree-based classifer (BTBC), and Explainable Artificial Intelligence (XAI). Initially, the Butterworth filter is employed to eliminate various artifacts, and the discrete wavelet transform (DWT) is used to decompose the EEG signals and extract various eigenvalue features of the statistical time domain (STD) as linear and Fractal dimension-based non-linear (FD-NL). The optimal features are then identified through correlation coefficients with P-value and distance correlation analysis.These features are subsequently utilized by the Bagged Tree-based classifer (BTBC). The proposed model provides best performance in mitigating overfitting issues and improves the average accuracy by 2% using (CD, E), (AB, CD, E), and (A, B) experimental types as compared to other machine learning (ML) models using well-known Bonn and UCI-EEG benchmark datasets. Finally, SHapley additive exPlanation (SHAP) was used to interpret and explain the decision-making process of the proposed model. The results highlight the framework’s capability to accurately classify ES, thereby improving the diagnosis process in patients with brain dysfunctions.