Applications In Medical Diagnosis Research Articles

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.

Construction of distance measure for fermatean fuzzy sets and its application in educational assessment and medical diagnosis

Construction of distance measure for fermatean fuzzy sets and its application in educational assessment and medical diagnosis

The Generalized Similarity Measures Based on Complex Interval-Valued Hesitant Fuzzy Sets and Their Applications in Pattern Recognition and Medical Diagnosis

The Generalized Similarity Measures Based on Complex Interval-Valued Hesitant Fuzzy Sets and Their Applications in Pattern Recognition and Medical Diagnosis

Flexible colloidal quantum dot lasers enabled by self-assembly

Colloidal quantum dot (CQD) lasers show promising applications in flexible optoelectronic devices, due to their tunable emission wavelength, narrow spectrum bandwidth and high power intensity. However, fabricating a flexible CQD laser is challenging because of the difficulties in fabricating optical cavities on flexible substrates using traditional microfabrication technologies. Herein, we propose a one-step self-assembly approach to fabricate flexible CQD supraparticle lasers. The whole assembly approach is processed in a liquid environment without surfactants, and the formed spherical CQD supraparticles are featured with smooth surfaces, serving as high-quality-factor whispering-gallery mode cavities to support laser oscillation. A low lasing threshold of 54 µJ/cm2 is observed while exciting a CQD supraparticle with pulsed femtosecond lasers. The calculated cavity quality factor of 963 for CQD supraparticle lasers is twofold larger than that of CQD lasers assembled with surfactants. Moreover, the CQD supraparticles can serve as free-standing lasers, which allows them to be deposited on flexible substrates such as paper and cloth. Furthermore, our CQD lasers show high stability, after being continuously photoexcited above the threshold for 400 min, their lasing intensity remains at 85.7% of the initial value. As bright, free-standing and long-term stable light sources, the assembled CQD lasers proposed in this work show potential applications in wearable devices and medical diagnosis.

Efficient Utilization of Image Fusion and Interpolation for Medical Image Diagnosis applications

Efficient Utilization of Image Fusion and Interpolation for Medical Image Diagnosis applications

Framework Construction of an Adversarial Federated Transfer Learning Classifier

As the Internet grows in popularity, more and more classification jobs, such as IoT, finance industry and healthcare field, rely on mobile edge computing to advance machine learning. In the medical industry, however, good diagnostic accuracy necessitates the combination of large amounts of labeled data to train the model, which is difficult and expensive to collect and risks jeopardizing patients' privacy. In this paper, we offer a novel medical diagnostic framework that employs a federated learning platform to ensure patient data privacy by transferring classification algorithms acquired in a labeled domain to a domain with sparse or missing labeled data. Rather than using a generative adversarial network, our framework uses a discriminative model to build multiple classification loss functions with the goal of improving diagnostic accuracy. It also avoids the difficulty of collecting large amounts of labeled data or the high cost of generating large amount of sample data. Experiments on real-world image datasets demonstrates that the suggested adversarial federated transfer learning method is promising for real-world medical diagnosis applications that use image classification.

Investigation of the Cross Sections and Effect of Level Density Models for Platinum Element

In cases where experimental studies cannot be carried out and there is no experimental data with it, studies carried out with theoretical models shed light on the researchers' knowledge of different data. The most important of this data is the measurable or calculatable influence cross-section value, which is defined as the probability of a reaction occurrence. Examining the possible effects of different models in the calculation of the effect section is important for the correct calculation of this value. The most important data, the cross section of influence, has taken its place in the radioisotope world as well as in many areas of nuclear physics. With developing technology and advancing science, radioisotopes have widespread and diversified uses. Most commonly, radioisotopes are used in medical diagnosis and treatment applications. Among the many radioisotopes used for this purpose, 191-199Au radioisotopes are also important in terms of both their benefits and characteristics in medical applications. In this respect, the study aimed to investigate the effects of different nuclear level density models in production impact cross-section calculations of 191-199Au radioisotopes with deuteron reference. Theoretical cross sections using the TALYS code have been simulated for all isotopes. The results of the calculations obtained were compared with each other and with the experimental data in the literature and it was aimed to determine the most compatible level density models according to the reaction situations examined.

Analysis of Hamming and Hausdorff 3D distance measures for complex pythagorean fuzzy sets and their applications in pattern recognition and medical diagnosis

AbstractSimilarity measures are very effective and meaningful tool used for evaluating the closeness between any two attributes which are very important and valuable to manage awkward and complex information in real-life problems. Therefore, for better handing of fuzzy information in real life, Ullah et al. (Complex Intell Syst 6(1): 15–27, 2020) recently introduced the concept of complex Pythagorean fuzzy set (CPyFS) and also described valuable and dominant measures, called various types of distance measures (DisMs) based on CPyFSs. The theory of CPyFS is the essential modification of Pythagorean fuzzy set to handle awkward and complicated in real-life problems. Keeping the advantages of the CPyFS, in this paper, we first construct an example to illustrate that a DisM proposed by Ullah et al. does not satisfy the axiomatic definition of complex Pythagorean fuzzy DisM. Then, combining the 3D Hamming distance with the Hausdorff distance, we propose a new DisM for CPyFSs, which is proved to satisfy the axiomatic definition of complex Pythagorean fuzzy DisM. Moreover, similarly to some DisMs for intuitionistic fuzzy sets, we present some other new complex Pythagorean fuzzy DisMs. Finally, we apply our proposed DisMs to a building material recognition problem and a medical diagnosis problem to illustrate the effectiveness of our DisMs. Finally, we aim to compare the proposed work with some existing measures is to enhance the worth of the derived measures.

Visualization of X-rays with an Ultralow Detection Limit via Zero-Dimensional Perovskite Scintillators

X-rays play an extremely significant role in medical diagnosis, safety testing, scientific research, and other practical applications. However, as the main sources of radioactive pollution, the hazard of X-rays to human health and the environment has been a major concern. Herein, the explored perovskite scintillator of Cs2Zr1-xPbxCl6 in this work exhibits an ultrahigh radioluminescence intensity owing to the enhanced X-ray absorption for the introduction of Pb2+ ions. The Cs2Zr1-xPbxCl6 crystals are demonstrated as efficient scintillators with a self-trapped exciton emission and extremely high steady-state light yield (∼101,944 photons meV-1). This fascinating scintillator provides a convenient visual tool for X-ray detection even for an indoor lighting environment, reaching a low detection limit of ∼14.2 nGy·s-1, which is about 1/387 of the typical medical imaging dose (5.5 μGy·s-1). Moreover, X-ray imaging with a high resolution of 16.6 lp·mm-1 is achieved with the as-explored Cs2Zr1-xPbxCl6 scintillator film. Herein, the Cs2Zr1-xPbxCl6 scintillator provides a feasible strategy for X-ray monitoring in the field of biomedicine, high-energy physics, national security, and other applications.

Directional touch sensing for stiffness singularity search in an object using microfinger with tactile sensor.

Palpation is widely used as the initial medical diagnosis. Integration of micro tactile sensors and artificial muscles enables a soft microfinger for active touch sensing using its bending actuation. Active touch sensing by pushing-in motion of microfinger enables to evaluate stiffness distribution on an elastic object. Due to its compactness, the microfinger can enter a narrow space, such as gastrointestinal and abdominal spaces in a body. However, a microfinger can only touch and sense limited points. We aim at efficient method for searching a stiffness singular part in an elastic object by the directional touch sensing of a microfinger. This study presents a microfinger for active touch sensing using bending and push-in actuation and proposes an algorithm utilizing directivity in touch sensing by a microfinger for efficient localization of the stiffness singular part in an object. A gelatin block structure with a small rigid ball was prepared and touch sensed by the microfinger. Consequently, the position of the buried rigid ball could be efficiently identified based on the proposed algorithm. This result implies that the proposed method has potential applications in endoscopic medical diagnosis, particularly in identifying tumor positions.

DuDoUFNet: Dual-Domain Under-to-Fully-Complete Progressive Restoration Network for Simultaneous Metal Artifact Reduction and Low-Dose CT Reconstruction.

To reduce the potential risk of radiation to the patient, low-dose computed tomography (LDCT) has been widely adopted in clinical practice for reconstructing cross-sectional images using sinograms with reduced x-ray flux. The LDCT image quality is often degraded by different levels of noise depending on the low-dose protocols. The image quality will be further degraded when the patient has metallic implants, where the image suffers from additional streak artifacts along with further amplified noise levels, thus affecting the medical diagnosis and other CT-related applications. Previous studies mainly focused either on denoising LDCT without considering metallic implants or full-dose CT metal artifact reduction (MAR). Directly applying previous LDCT or MAR approaches to the issue of simultaneous metal artifact reduction and low-dose CT (MARLD) may yield sub-optimal reconstruction results. In this work, we develop a dual-domain under-to-fully-complete progressive restoration network, called DuDoUFNet, for MARLD. Our DuDoUFNet aims to reconstruct images with substantially reduced noise and artifact by progressive sinogram to image domain restoration with a two-stage progressive restoration network design. Our experimental results demonstrate that our method can provide high-quality reconstruction, superior to previous LDCT and MAR methods under various low-dose and metal settings.

A Federated Learning Framework for Breast Cancer Histopathological Image Classification

Quantities and diversities of datasets are vital to model training in a variety of medical image diagnosis applications. However, there are the following problems in real scenes: the required data may not be available in a single institution due to the number of patients or the type of pathology, and it is often not feasible to share patient data due to medical data privacy regulations. This means keeping private data safe is required and has become an obstacle in fusing data from multi-party to train a medical model. To solve the problems, we propose a federated learning framework, which allows knowledge fusion achieved by sharing the model parameters of each client through federated training rather than sharing data. Based on breast cancer histopathological dataset (BreakHis), our federated learning experiments achieve the expected results which are similar to the performances of the centralized learning and verify the feasibility and efficiency of the proposed framework.

A New Correlation Coefficient Based on T-Spherical Fuzzy Information with Its Applications in Medical Diagnosis and Pattern Recognition

The T-Spherical fuzzy set (TSFS) is the most generalized form among the introduced fuzzy frameworks. It obtains maximum information from real-life phenomena due to its maximum range. Consequently, TSFS is a very useful structure for dealing with information uncertainties, especially when human opinion is involved. The correlation coefficient (CC) is a valuable tool, possessing symmetry, to determine the similarity degree between objects under uncertainties. This research aims to develop a new CC for TSFS to overcome the drawbacks of existing methods. The proposed CCs are generalized, flexible, and can handle uncertain situations where information has more than one aspect. In addition, the proposed CCs provide decision-makers independence in establishing their opinion. Based on some remarks, the usefulness of the new CC is reviewed, and its generalizability is evaluated. Moreover, the developed new CC is applied to pattern recognition for investment decisions and medical diagnosis of real-life problems to observe their effectiveness and applicability. Finally, the validity of the presented CC is tested by comparing it with the results of the previously developed CC.

Multifunctional porphyrin-ionic liquid aggregate for highly sensitive electrochemical detection of protein

Herein, this paper describes a novel label-free and extensibility electrochemical sensing platform based on porphyrin-ionic liquid (Por-IL) aggregate for the detection of antibody. The Por-IL was synthesized with water-soluble porphyrins Zn(II) meso-tetrakis (4-sulfonatophenyl)porphyrin (ZnTPPS)) and the ionic liquid 1-decyl-alkyl-3-methylimidazolium bromide (C10mimBr) under room temperature, which exhibited high singlet oxygen (1O2) quantum yields and electrocatalytic activity toward hydrogen peroxide (H2O2). In the presence of antibody, the antibody could be introduced on the surface of the electrode on the basis of antigen-antibody biorecognition. Under light illumination, the 1O2 formed, then water and the formed 1O2 could be catalyzed by antibody via the antibody catalyzed water oxidation pathway to produce H2O2 which could be detected by the electrode, leading to the electrochemical signal for readout. Using anti-carcinoembryonic antigen (anti-CEA) as a model target, this method displayed a wide linear detection range and a lower detection limit. In particular, Por-IL demonstrated lower detection limit of 1.67 fg/mL. Furthermore, the Por-IL sensors were able to detect antibody in complex samples. The immunosensor platform has the features of label-free, sensitivity, only with air and on/off light switching and could be used to detect of a variety of antibody by using corresponding antigen, showing potential application in medical diagnosis.

Temperature- and pH-Sensitive Nitrogen and Sulfur Codoped Carbon Quantum Dots for Sequential Detection of Fe3+ and H2S

In this study, nitrogen and sulfur codoped carbon quantum dot (N,S-CQD) fluorescent nanoprobes were synthesized through a one-step hydrothermal method using l-cysteine and α-methacrylic acid as raw materials, and an “on–off–on” fluorescence mechanism was designed using N,S-CQDs for sequential determination of Fe3+ and H2S in human serum. The quenching process (on–off) and mechanism of N,S-CQDs by Fe3+ were studied via fluorescence spectroscopy, zeta potential, cyclic voltammetry, and orbital energy level simulation. Meanwhile, an “off–on” strategy for the N,S-CQD/Fe3+ fluorescence sensing platform was constructed for the determination of the H2S-based “on–off” mechanism. This study not only achieved highly selective detection of Fe3+ and H2S over a wide concentration range of 0–250, 250–500, and 2.5–900 μM, respectively, but also improved our understanding on the specific identifications between N,S-CQDs, Fe3+, and H2S. What is more, it was found that N,S-CQDs also possessed pH and temperature sensing performance. Finally, N,S-CQD fluorescent nanoprobes were successfully applied for the determination of Fe3+ and H2S in spiked serum samples, which also proved their potential application in medical diagnosis and biosensors.