Field Of Medical Diagnosis Research Articles

A novel rhodamine B fluorescence probe for rapid identification of different amino acids by high efficiency fluorescence spectrum-mass spectrometry.

Rapid detection of amino acids plays an important role in the field of medical diagnosis. By combining Rhodamine B with triphenylamine, a novel double-response fluorescence probe (E)-4-((4-(((3',6'-bis(diethylamino)-3-oxospiro[isoindoline-1,9'-xanthen]-2-yl)imino)methyl)phenyl)(phenyl)amino)benzaldehyde (RBTPA) was prepared for rapid identification of different amino acids. Under daylight and 365 nm irradiation, it was found that the color change was most bright at pH = 3, and changed to dim at pH = 4. When pH = 3 and pH = 4, the photophysical properties of the two strong acids are very different. The maximum redshift of UV absorption light is 110nm, and the maximum fluorescence emission intensity is 4 times different. In order to further observe their binding structure analysis with different amino acids, qualitative analysis of each response structure was determined by mass spectrometry according to different molecular weights. The fluorescence probe RBTPA has two different isomers for recognition response in aldehyde group and imine group, respectively.

Diagnose Breast Cancer Using Two Machine Learning Methods andComparing and Analyzing Them

The breast cancer is currently the number one cancer in the world and has a high early treatment rate. Therefore, early screening and diagnosis of breast cancer is very important. Machine learning is very strong in analyzing and processing massive data. In this paper, we train and optimize decision tree and SVM models to identify malignant breast cancer and compare and analyze the performance of the two models. The accuracy of the earliest optimized models reaches about 98%, and it is found that the two models focus on different classification effects for the two samples, which can be further optimized by stacking and other ways. The dataset used to train the models in this paper is from the Wisconsin breast cancer database (WBCD), a benchmark dataset commonly used to compare different algorithms, which is of some significance for exploring the use of machine learning in the field of medical diagnosis.

In Situ Measurement of Urea Concentration With an In-Fiber SPR-MZI Sensor.

A fiber-optic urea sensor based on surface plasmon resonance (SPR) and Mach-Zehnder interference (MZI) combined principle was designed and implemented. By plating gold film on the single-mode-no-core-thin-core-single-mode fiber structure, we successfully excited both SPR and MZI, and constructed two parallel detection channels for simultaneously measurement of urea concentration and temperature. Urease was immobilized on the gold film by metal-organic zeolite skeleton (ZIF-8), which can not only fix a large number of urease to improve measurement sensitivity of urea, but also protect urease activity to ensure the sensor stability. Experimental results indicate that the designed urea sensor with temperature compensation function can detect urea solution with concentration of 1-9 mM, and the sensitivity is 1.4 nm/mM. The proposed measurement method provides a new choice for monitoring urea concentration in the field of medical diagnosis and human health monitoring.

Artificial intelligence model for tumoral clinical decision support systems

Background and ObjectiveComparative diagnostic in brain tumor evaluation makes possible to use the available information of a medical center to compare similar cases when a new patient is evaluated. By leveraging Artificial Intelligence models, the proposed system is able of retrieving the most similar cases of brain tumors for a given query. The primary objective is to enhance the diagnostic process by generating more accurate representations of medical images, with a particular focus on patient-specific normal features and pathologies. A key distinction from previous models lies in its ability to produce enriched image descriptors solely from binary information, eliminating the need for costly and difficult to obtain tumor segmentation. MethodsThe proposed model uses Artificial Intelligence to detect patient features to recommend the most similar cases from a database. The system not only suggests similar cases but also balances the representation of healthy and abnormal features in its design. This not only encourages the generalization of its use but also aids clinicians in their decision-making processes. This generalization makes possible for future research in different medical diagnosis areas with almost not any change in the system. ResultsWe conducted a comparative analysis of our approach in relation to similar studies. The proposed architecture obtains a Dice coefficient of 0.474 in both tumoral and healthy regions of the patients, which outperforms previous literature. Our proposed model excels at extracting and combining anatomical and pathological features from brain Magnetic Resonances (MRs), achieving state-of-the-art results while relying on less expensive label information. This substantially reduces the overall cost of the training process. Our findings highlight the significant potential for improving the efficiency and accuracy of comparative diagnostics and the treatment of tumoral pathologies. ConclusionsThis paper provides substantial grounds for further exploration of the broader applicability and optimization of the proposed architecture to enhance clinical decision-making. The novel approach presented in this work marks a significant advancement in the field of medical diagnosis, particularly in the context of Artificial Intelligence-assisted image retrieval, and promises to reduce costs and improve the quality of patient care using Artificial Intelligence as a support tool instead of a black box system.

APPLICATION OF MATHEMATICAL MODELS IN THE DIAGNOSIS OF DISEASES OF INTERNAL ORGANS

The application of diagnostic expert systems in medical technology signifies a notable progression, as they provide a computerized framework for decision-support, assisting healthcare practitioners in the process of disease diagnosis. These systems facilitate the integration of patient data, encompassing symptoms and medical history, with a knowledge base in order to produce a comprehensive compilation of potential diagnoses. Through the utilization of knowledge-based methodologies, they enhance these potentialities in order to ascertain the most probable diagnosis. The present study examines expert systems, investigating their historical development, architectural structure, and the approaches utilized for knowledge representation. There is a significant emphasis placed on the advancement and implementation of these systems within the medical industry of Kazakhstan. This paper provides a comprehensive analysis of the benefits and drawbacks associated with diagnostic expert systems, emphasizing their potential to bring about significant advancements in medical fields. The study places significant emphasis on the necessity of developing and conducting thorough testing of these systems in order to improve the precision and effectiveness of medical diagnostics. The statement recognizes the importance of continuous research in order to enhance the design and implementation of these systems in various healthcare settings. This research makes a notable addition by examining optimization theory in the field of medical diagnosis. This study presents novel approaches for effectively addressing the intricacies and uncertainties associated with the diagnosis of complicated disorders. The work presents methodology for navigating the complex field of medical diagnostics by utilizing mathematical modeling and optimization approaches, specifically the gradient projection method. The utilization of diverse ways to tackle qualitative ambiguities in this approach signifies a significant progression inside the domain of diagnostic expert systems.

A Self-Powered Lactate Sensor Based on the Piezoelectric Effect for Assessing Tumor Development.

The build-up of lactate in solid tumors stands as a crucial and early occurrence in malignancy development, and the concentration of lactate in the tumor microenvironment may be a more sensitive indicator for analyzing primary tumors. In this study, we designed a self-powered lactate sensor for the rapid analysis of tumor samples, utilizing the coupling between the piezoelectric effect and enzymatic reaction. This lactate sensor is fabricated using a ZnO nanowire array modified with lactate oxidase (LOx). The sensing process does not require an external power source or batteries. The device can directly output electric signals containing lactate concentration information when subjected to external forces. The lactate concentration detection upper limit of the sensor is at least 27 mM, with a limit of detection (LOD) of approximately 1.3 mM and a response time of around 10 s. This study innovatively applied self-powered technology to the in situ detection of the tumor microenvironment and used the results to estimate the growth period of the primary tumor. The availability of this application has been confirmed through biological experiments. Furthermore, the sensor data generated by the device offer valuable insights for evaluating the likelihood of remote tumor metastasis. This study may expand the research scope of self-powered technology in the field of medical diagnosis and offer a novel perspective on cancer diagnosis.

Image reconstruction method for incomplete CT projection based on self-guided image filtering.

In some fields of medical diagnosis or industrial nondestructive testing, it is difficult to obtain complete computed tomography (CT) data due to the limitation of radiation dose or other factors. Therefore, image reconstruction of incomplete projection data is the focus of this paper. In this paper, a new image reconstruction model based on self-guided image filtering (SGIF) term is proposed for few-view and segmental limited-angle (SLA) CT reconstruction. Then the alternating direction method (ADM) is used to solve this model. For simplicity, we call it ADM-SGIF method. The key idea of ADM-SGIF method is to use the reconstructed image itself as a reference and utilize its structural features to guide CT reconstruction. This method can effectively preserve image structures and remove shading artifacts. To validate the effectiveness of the proposed reconstruction method, we conduct digital phantom and real CT data experiments. The results indicate that ADM-SGIF method outperforms competing methods, including total variation (TV), relative total variation (RTV), and L0-norm minimization solved by ADM (ADM-L0) methods, in both subjective and objective evaluations.

Analyzing entropy features in time-series data for pattern recognition in neurological conditions

In the field of medical diagnosis and patient monitoring, effective pattern recognition in neurological time-series data is essential. Traditional methods predominantly based on statistical or probabilistic learning and inference often struggle with multivariate, multi-source, state-varying, and noisy data while also posing privacy risks due to excessive information collection and modeling. Furthermore, these methods often overlook critical statistical information, such as the distribution of data points and inherent uncertainties. To address these challenges, we introduce an information theory-based pipeline that leverages specialized features to identify patterns in neurological time-series data while minimizing privacy risks. We incorporate various entropy methods based on the characteristics of different scenarios and entropy. For stochastic state transition applications, we incorporate Shannon’s entropy, entropy rates, entropy production, and the von Neumann entropy of Markov chains. When state modeling is impractical, we select and employ approximate entropy, increment entropy, dispersion entropy, phase entropy, and slope entropy. The pipeline’s effectiveness and scalability are demonstrated through pattern analysis in a dementia care dataset and also an epileptic and a myocardial infarction dataset. The results indicate that our information theory-based pipeline can achieve average performance improvements across various models on the recall rate, F1 score, and accuracy by up to 13.08 percentage points, while enhancing inference efficiency by reducing the number of model parameters by an average of 3.10 times. Thus, our approach opens a promising avenue for improved, efficient, and critical statistical information-considered pattern recognition in medical time-series data.

Harnessing the Power of Multimodal Data: Medical Fusion and Classification

In the field of medical diagnosis, combining different types of information like text, images, and audio is a big step forward in making patient assessments more accurate. This research introduces an innovative method to bring together and categorize these different types of data. This method fills an important gap in current research [50, 54]. Proposed approach focuses on turning each type of data—text, images, and audio—into useful numbers. Text data is processed to extract meaning and context, while images are analysed using advanced computer techniques to capture important visual details. We also carefully examine audio data to extract important sound features, which is often overlooked but can be a valuable source of diagnostic information [48]. What makes our method special is how we combine these different types of data. We designed a strategy to blend these diverse sets of numbers into a single, enriched representation. This approach keeps the unique characteristics of each data type intact while harnessing their combined power for diagnosis [22, 29]. After combining the data, we use a well-chosen classification model that's known for its ability to make sense of complex data, especially in medical diagnosis scenarios [67, 71]. Proposed approach is rigorously assessing our method using a set of strong metrics that measure not only how accurate it is but also how reliable and valid it is for diagnosis [90, 94]. The results of this study mark a significant step forward in the field of combining different types of data, showing how it can greatly improve medical diagnosis. This method has the potential to revolutionize healthcare, enabling more precise and comprehensive data-driven decisions [143, 156].

SRT: Swin-residual transformer for benign and malignant nodules classification in thyroid ultrasound images

With the advancement of deep learning technology, computer-aided diagnosis (CAD) is playing an increasing role in the field of medical diagnosis. In particular, the emergence of Transformer-based models has led to a wider application of computer vision technology in the field of medical image processing. In the diagnosis of thyroid diseases, the diagnosis of benign and malignant thyroid nodules based on the TI-RADS classification is greatly influenced by the subjective judgment of ultrasonographers, and at the same time, it also brings an extremely heavy workload to ultrasonographers. To address this, we propose Swin-Residual Transformer (SRT) in this paper, which incorporates residual blocks and triplet loss into Swin Transformer (SwinT). It improves the sensitivity to global and localized features of thyroid nodules and better distinguishes small feature differences. In our exploratory experiments, SRT model achieves an accuracy of 0.8832 with an AUC of 0.8660, outperforming state-of-the-art convolutional neural network (CNN) and Transformer models. Also, ablation experiments have demonstrated the improved performance in the thyroid nodule classification task after introducing residual blocks and triple loss. These results validate the potential of the proposed SRT model to improve the diagnosis of thyroid nodules' ultrasound images. It also provides a feasible guarantee to avoid excessive puncture sampling of thyroid nodules in future clinical diagnosis.

人工智能在医疗诊断系统中的应用

With the progress of scientific level, artificial intelligence has been applied more and more in the medical field. A large number of medical data are mainly characterized by unstructured information. Through the analysis of these information, a large number of medical case data can be generated, thus generating related value-added information. With the rapid development of artificial intelligence represented by deep learning, artificial intelligence has broad application prospects in clinical practice. The integration of artificial intelligence technology and medical field under the environment of big data is an important trend in the development of medical field. Artificial intelligence technology can effectively analyze and process massive medical data, assist doctors to improve the accuracy and efficiency of diagnosis and treatment, and bring more personalized treatment measures to patients. With the development of medical technology, the development of artificial intelligence medical technology provides a new idea for clinical medicine. The application of artificial intelligence technology in the field of medical diagnosis is studied in this paper.

Artificial Intelligence Techniques in Medical Imaging: A Systematic Review

This scientific review presents a comprehensive overview of medical imaging modalities and their diverse applications in artificial intelligence (AI)-based disease classification and segmentation. The paper begins by explaining the fundamental concepts of AI, machine learning (ML), and deep learning (DL). It provides a summary of their different types to establish a solid foundation for the subsequent analysis. The prmary focus of this study is to conduct a systematic review of research articles that examine disease classification and segmentation in different anatomical regions using AI methodologies. The analysis includes a thorough examination of the results reported in each article, extracting important insights and identifying emerging trends. Moreover, the paper critically discusses the challenges encountered during these studies, including issues related to data availability and quality, model generalization, and interpretability. The aim is to provide guidance for optimizing technique selection. The analysis highlights the prominence of hybrid approaches, which seamlessly integrate ML and DL techniques, in achieving effective and relevant results across various disease types. The promising potential of these hybrid models opens up new opportunities for future research in the field of medical diagnosis. Additionally, addressing the challenges posed by the limited availability of annotated medical images through the incorporation of medical image synthesis and transfer learning techniques is identified as a crucial focus for future research efforts.

Liquid Biopsy: An Evolving Paradigm for Non-invasive Disease Diagnosis and Monitoring in Medicine.

Liquid biopsy stands as an innovative instrument in the realm of precision medicine, enabling non-invasive disease diagnosis and the early detection of cancer. Liquid biopsy helps in the extraction of circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and cell-free DNA (cfDNA) from blood samples and other body fluids, thereby facilitating disease diagnosis and prediction of high-risk patients.Various techniques such as advanced sequencing methods and biomarker-based cell capture have led to the isolation and study of the different biomarkers such as ctDNA, cfDNA, and CTCs. These biopsies also have immense potential in the early detection and diagnosis of various diseases across all medical specialties, prediction and screening of high-risk cases, and detection of different immune response patterns in response to infectious diseases, and also help in predicting treatment outcomes. Although liquid biopsy has the potential to disrupt the field of medical diagnosis, it is met by various challenges such as limited tumor-derived components, less specificity, and inadequate advancement in methods to isolate biomarkers. Despite all these challenges, liquid biopsies provide the potential to become a minimally invasive method of diagnosisthat would facilitate real-time monitoring of patients, which differentiates them from traditional tissue biopsies. This article aims to provide a complete overview of the current technologies, different biomarkers, and body fluids that can be used in liquid biopsy and its clinical applications and the potential impact that liquid biopsy holds in the field of precision medicine, facilitating early diagnosis and prompt management of various diseases and cancers.

A novel dimensionality reduction algorithm for Cholangiocarcinoma hyperspectral images

Medical hyperspectral imagery (HSI) has become a promising auxiliary diagnostic tool in the field of medical diagnosis and being offered noninvasive disease diagnosis in many cases. However, the huge number of spectral bands may lead to the curse of dimensionality and increase computational complexity. Thus, dimensionality reduction (DR) is an essential step for hyperspectral preprocessing. To maintain the cubic nature of HSI and extract more discriminative and representative information from original data, we proposed a novel DR method termed tensor-based weight-modified multi-manifold discriminant analysis (TWMDA). In this paper, two weight modified intra-class and inter-class adjacency affinity matrices are constructed to make full use of the class information and strengthen the power of capturing discriminant information from high-dimensional data. After that, balancing the effect of within-class adjacency compactness and between-class adjacency separation to obtain better classification performance in tensor space instead of vector space. Experimental results on Cholangiocarcinoma (CCA) microscope hyperspectral data sets prove the efficiency and superiority of the proposed method. Hence, this novel DR approach would be more beneficial for cancer diagnosis based on HSI.

Optical properties reconstruction in nonhomogeneous participating medium based on an improved sequential quadratic programming

The reconstruction of optical parameters achieves functional imaging by providing characteristics closely related to the physiological state of biological tissues. With considerable research significance, this imaging technology has received increasing attention in the field of medical diagnosis and treatment. However, owing to the limited number of known measurement signals far less than the number of target parameters to be estimated, reconstruction requires a long ill-conditioned process, which is easily polluted by background noise so as to difficulties for achieving high imaging accuracy and efficiency. To conquer these deficiencies, an improved sequential quadratic programming (ISQP) is proposed in this work. In the ISQP, dual constraints of equality and inequality are added in the process of minimizing the objective functional, which avoids the time-consuming forward problem. Moreover, duality method, reduced Hessian matrix, and second-order correction are introduced in the subproblem. In addition, prior information of diagonal contiguous points is introduced to overcome the ill-posedness. The reconstruction performance of ISQP in different modes is discussed in depth. A time-frequency coupling model based on ISQP is constructed subsequently, of which the practical significance is demonstrated in the biological tissue-like medium model. The results show that in the established frequency domain model, ISQP significantly shortens the calculation time of the traditional method and improves the reconstruction quality of absorption parameters. The developed time-frequency coupling method effectively eliminates the influence of the background disturbance, and in the meantime improves the accuracy and efficiency when simultaneously estimating multi-parameters related to optics, with good robustness. The effectiveness of the ISQP combined with hybrid time-frequency model provides new referable ideas and application potential for the development and progress of medical, biology, photonics and other fields.

An intelligent computer-aided diagnosis method for paroxysmal atrial fibrillation patients with nondiagnostic ECG signals

Classification of ECG signals plays an important role in the field of medical diagnosis. Despite that much progress has been made in ECG classification in recent years, most of them focus on single-lead cardiac feature extraction. In this paper, we propose a new classification method based on twelve-lead surface ECG signal by fusing improved hand-crafted ECG features and deep learning classifiers for paroxysmal atrial fibrillation (PAF) detection. Three-dimensional dominated components are extracted without ECG signal segmentation operations, and continuous wavelet transform (CWT) is adopted for time–frequency representation to reflect essential characteristics of multi-resolution frequency domain at different scales. This kind of time–frequency representation has high discriminative power to detect PAF even before pathologic changes in surface twelve-lead ECG signals. Five pre-trained deep neural networks models are used to perform transfer learning and feature fusion. ECG signals are finally classified by using a feature fusion method based on multiple deep networks.

Comparison evaluation of unet-based models with noise augmentation for breast cancer segmentation on ultrasound image

This paper investigates the application of convolutional neural networks (CNNs), particularly the UNet model architecture, to improve the accuracy of breast cancer tumor segmentation in ultrasound images. Accurate identification of breast cancer is essential for effective patient treatment. However, ultrasound images, often contain noise and artifacts, which can complicate the task of tumor segmentation. Therefore, to highlight the most robust architecture, modifications were made to the original set, including the addition of noise and fuzziness. In this study, a comparative study of five different variants of UNet models (UNet, Attention UNet, UNet++, Dense Inception UNet and Residual UNet) was conducted on a diverse set of ultrasound images with different breast tumors. Using consistent training methods and techniques of augmentation and adding noise to the data, an improvement in segmentation accuracy was highlighted when using the Dense Inception UNet architecture. The results have potential practical applications in the field of medical diagnosis and can assist medical professionals in automatic tumor segmentation in breast cancer ultrasound images. This study highlights the improvement of segmentation accuracy by introducing dense induction into the UNet architecture. Importantly, the Dice coefficient, a key segmentation metric, improved markedly, increasing from 0.973 to 0.976 after data augmentation. The results of the study offer promise to the medical community by offering a more accurate and reliable approach to segmenting breast cancer lesions on ultrasound images. The findings can be implemented in clinical practice to assist radiologists in early cancer diagnosis.

The flexible and distributed pressure sensor with 64 units for on-line gait recognition analysis

With the rapid development of flexible electronic technology, human gait recognition device is also developing in the direction of flexibility, refinement, and intelligence. In this paper, a gait recognition system with the advantages of being non-invasive and flexible is proposed and prepared to collect complex pressure information of human plantar and identify different gaits accurately. A flexible and distributed pressure sensor with a structure of eight rows and eight columns is designed and fabricated by the screen printing process and micro-nano manufacturing technology. The insole shape can be placed directly into a sneaker for easy wearing, while 64 pressure sensing units allow for more accurate detection of complex plantar pressure information. In addition, based on the “voltage mirror” principle and the feature threshold analysis method, a distributed pressure acquisition circuit and an online gait recognition software are developed respectively to realize time-sharing decoupling of complex output signals from multiple pressure sensing units and online real-time display of different gaits. Finally, Different human gait recognition experiments have also been carried out. The results show that the test error of the pressure sensing unit is better than 8.28%. The gait recognition system can realize the reliable and accurate online real-time identification of slow walking, fast walking, and different leaning directions, which shows great potential and value in the fields of medical diagnosis, health monitoring, gait correction and artificial intelligence.

A novel multi-parameter similarity measure of interval neutrosophic sets for medical diagnosis

The use of similarity measures in interval-valued neutrosophic sets (IVNSs) theory is essential for comparing and assessing the degree of IVNSs difference. However, existing similarity measures for IVNSs suffer from several issues such as lacking precise axiomatic definitions, counterintuitive results, division by zero errors, inability to distinguish between positive and negative differences, and failure to satisfy the ranking definition. To address these limitations, we propose a novel multi-parameter similarity measure for IVNSs based on the tangent function. We demonstrate that our measure satisfies the axiomatic definition and apply it to medical diagnosis, achieving accurate diagnostic results. Additionally, we consider the interactions between symptoms, adjust the proposed similarity measure using Choquet integrals, and provide analytical comparisons to demonstrate the advantages of our improved similarity measure, highlighting its stability and high confidence in the field of medical diagnosis.This study contributes to the advancement of similarity measures in IVNSs theory and provides valuable insights for the field of medical diagnosis.

A Comprehensive Review on the Diagnosis of Knee Injury by Deep Learning-Based Magnetic Resonance Imaging.

The continual improvement in the field of medical diagnosis has led to the monopoly of using deep learning (DL)-based magnetic resonance imaging (MRI) for the diagnosis of knee injury related to meniscal injury,ligament injury including the cruciate ligaments, collateral ligaments and medial patella-femoral ligament, and cartilage injury. The present systematic review was done by PubMed and Directory of Open Access Journals (DOAJ), wherein we finalised 24 studies conducted on the accuracy of DL MRI studies for knee injury identification. The studies showed an accuracy of 72.5% to 100% indicating that DL MRI holds an equivalent performance as humans in decision-making and management of knee injuries. This further opens up future exploration for improving MRI-based diagnosis keeping in mind the limitations of verification bias and data imbalance in ground truth subjectivity.