Method For Diagnosis Research Articles

The concentration McMaster method for diagnosis of patent Ascaris and Trichuris infections in humans

In tropical and subtropical regions, soil-transmitted helminth (STH) infections such as Ascaris lumbricoides, Trichuris trichiura, and hookworms have a significant impact on public health. Globally, A. lumbricoides infects approximately 0.8 billion people, while T. trichiura infects around 500 million. This study involved a comparison of three diagnostic methods, Kato-Katz and two flotation methods (concentration McMaster, and simple McMaster), for the detection of Ascaris and Trichuris in human faeces. We conducted a comparison of the number of eggs in faeces (or faecal egg counts (FECs)) obtained with these methods using freshly collected samples that were positive for T. trichiura and spiked with a known quantity of Ascaris sp. eggs. Additionally, for the concentration McMaster method we assessed FECs after storing the samples at 5°C for up to 21 days. The concentration McMaster method demonstrated superiority over the simple McMaster method in terms of higher detection levels for both helminths, while the Kato-Katz method yielded FEC values very close to the ’true’ spiking values. Importantly, the concentration McMaster method was considerably easier to read compared with the Kato-Katz method, and it allowed for sample storage at 5°C for up to 14 days without affecting FEC results. Consequently, we conclude that the concentration McMaster method is an effective and user-friendly alternative for diagnosis of Ascaris and Trichuris infections in humans. Furthermore, it offers the advantage of sample storage before analysis, enhancing flexibility in the workflow.

Fault diagnosis technology overview for lithium‐ion battery energy storage station

AbstractWith an increasing number of lithium‐ion battery (LIB) energy storage station being built globally, safety accidents occur frequently. Diagnosing faults accurately and quickly can effectively avoid safe accidents. However, few studies have provided a detailed summary of lithium‐ion battery energy storage station fault diagnosis methods. In this paper, an overview of topologies, protection equipment, data acquisition and data transmission systems is firstly presented, which is related to the safety of the LIB energy storage power station. Then, existing fault diagnosis technologies are reviewed in detail. Finally, the future developing trends of fault diagnosis technology are discussed.

MicroRNA profile of endometrial cancer from Indian patients-identification of potential biomarkers for prognosis

Endometrial cancer is one of the major cancers in women throughout the world. If diagnosed early, these cancers are treatable and the prognosis is usually good. However, one major problem in treating endometrial cancer is accurate diagnosis and staging. Till date, the choice method for diagnosis and staging is histopathology. Although there are few molecular markers identified, they are not always sufficient in making accurate diagnosis and deciding on therapeutic strategy. As a result, very often patients are under treated or over treated. In this study, our group has profiled microRNAs from Indian patients using NGS-based approach. We have identified 212 differentially expressed microRNAs in endometrial cancer. Among these, there are 17 novel miRNAs. Since this data represents only Indian cohort and also lacks survival data, validation across other populations is necessary before being considered as biomarkers. As one approach towards this, these microRNAs have also been compared to data from TCGA, which represent other populations and also correlated to relevance in overall survival. Using in-silico approaches, mRNA targets of the miRNAs have been predicted. After comparing with TCGA, we have identified 16 miRNA-mRNA pairs which could be potential prognostic biomarkers for endometrial cancer. This is the first miRNA profiling report from Indian cohort and one of the very few studies which have identified potential biomarkers of prognosis in endometrial cancer.

A personalized federated meta-learning method for intelligent and privacy-preserving fault diagnosis

Intelligent fault diagnosis methods have made significant progress during the past decade, often following a centralized training paradigm that gathers data from multiple industrial users, which results in high acquisition costs and raise data privacy concerns. Federated Learning (FL) provides a promising solution to intrinsically protect data privacy by jointly training a shared global model without sharing individual data, but the varying data distributions among clients pose challenges for diagnostic performance. To address these problems, a novel FL framework called federated meta-learning based on fine-grained classifier reconstruction (FedFGCR) is presented in this paper. First, an adaptive interpolation strategy based on meta-learning is designed to compute the optimal combination of global and local feature extractors as the initialization of the local training. Additionally, we introduce a new regularization term that allows the local model to leverage global data, enhancing model generalization by incorporating global semantic feature information. Furthermore, a fine-grained classifier reconstruction method is proposed to improve the personalized diagnosis ability for local samples. Comparative experiments with mainstream FL-based fault diagnosis methods have been conducted, demonstrating FedFGCR’s superiority with accuracy gains ranging from 0.96% to 5.41% on the CWRU dataset and from 5.01% to 9.95% on the KAIST dataset.

Novel imbalanced fault diagnosis method based on generative adversarial networks with balancing serial CNN and Transformer (BCTGAN)

In industrial production engineering, machines cannot sustain faults for extended periods, resulting in limited fault data collection. This scarcity of data impedes the advancement of accurate fault diagnosis models, particularly in the context of small-sample, extremely imbalanced fault modeling, which poses a significant and challenging problem. In this paper, we introduce an innovative data augmentation approach for addressing the small-sample imbalanced problem: the balanced conditioning CNN (Convolutional Neural Networks)-Transformer architecture based Generative Adversarial Network (BCTGAN). Firstly, we employ a serial CNN and Transformer architecture as the foundation of the generator. Secondly, we introduce micro-enhancement and SMOTE (Synthetic Minority Oversampling Technique) enhancement of samples to facilitate model training. Micro-enhancement simply augments the sample size, while SMOTE enhancement enhances diversity. Together, these techniques balance the training process by introducing new loss function terms. Finally, outlier samples are mitigated through a designed multi-domain feature screening method. Despite the added time cost and computational complexity, results from three instances demonstrate that the proposed method holds superior diagnostic results with great potential compared to traditional and general GAN-based fault diagnosis methods. Our work provides theoretical and practical insights for modeling the field of small-sample blended disequilibrium fault diagnosis.

A novel fault diagnosis method for bearing based on maximum average kurtosis morphological deconvolution

Abstract Maximum average kurtosis deconvolution (MAKD) effectively enhances periodic impulses in vibration signals. However, under conditions of random impulse interference, MAKD tends to amplify impulses within a single period. To address this problem, this paper proposes a maximum average kurtosis morphological deconvolution (MAKMD) method. First, on the basis of proposing a time-varying structural element more in line with the characteristics of vibration signals and constructing a new morphological gradient squared operator, an enhanced time varying morphological filtering (ETVMF) is proposed. Then, ETVMF is introduced into MAKD to eliminate the effect of random impulse. Finally, the diagonal slice spectrum (DSS) is utilized to detect the coupling frequency of the bearing, which makes the spectrum clearer and more convenient for bearing fault diagnosis. In MAKMD, the effect of random impulse is eliminated and the capability of fault feature extraction is enhanced. To demonstrate the method’s effectiveness and feasibility, experiments are conducted using simulated signals and measured bearing fault data, comparing results with existing deconvolution methods.

Pulmonary Tuberculosis Diagnosis Using an Intelligent Microscopy Scanner and Image Recognition Model for Improved Acid-Fast Bacilli Detection in Smears.

Microscopic examination of acid-fast mycobacterial bacilli (AFB) in sputum smears remains the most economical and readily available method for laboratory diagnosis of pulmonary tuberculosis (TB). However, this conventional approach is low in sensitivity and labor-intensive. An automated microscopy system incorporating artificial intelligence and machine learning for AFB identification was evaluated. The study was conducted at an infectious disease hospital in Jiangsu Province, China, utilizing an intelligent microscope system. A total of 1000 sputum smears were included in the study, with the system capturing digital microscopic images and employing an image recognition model to automatically identify and classify AFBs. Referee technicians served as the gold standard for discrepant results. The automated system demonstrated an overall accuracy of 96.70% (967/1000), sensitivity of 91.94% (194/211), specificity of 97.97% (773/789), and negative predictive value (NPV) of 97.85% (773/790) at a prevalence of 21.1% (211/1000). Incorporating AI and machine learning into an automated microscopy system demonstrated the potential to enhance the sensitivity and efficiency of AFB detection in sputum smears compared to conventional manual microscopy. This approach holds promise for widespread application in TB diagnostics and potentially other fields requiring labor-intensive microscopic examination.

Label-free detection of kidney stones urine combined with SERS and multivariate statistical algorithm

Kidney stones are a common urological disease with an increasing incidence worldwide. Traditional diagnostic methods for kidney stones are relatively complex and time-consuming, thus necessitating the development of a quicker and simpler diagnostic approach. This study investigates the clinical screening of kidney stones using Surface-Enhanced Raman Scattering (SERS) technology combined with multivariate statistical algorithms, comparing the classification performance of three algorithms (PCA-LDA, PCA-LR, PCA-SVM). Urine samples from 32 kidney stone patients, 30 patients with other urinary stones, and 36 healthy individuals were analyzed. SERS spectra data were collected in the range of 450–1800 cm−1 and analyzed. The results showed that the PCA-SVM algorithm had the highest classification accuracy, with 92.9% for distinguishing kidney stone patients from healthy individuals and 92 % for distinguishing kidney stone patients from those with other urinary stones. In comparison, the classification accuracy of PCA-LR and PCA-LDA was slightly lower. The findings indicate that SERS combined with PCA-SVM demonstrates excellent performance in the clinical screening of kidney stones and has potential for practical clinical application. Future research can further optimize SERS technology and algorithms to enhance their stability and accuracy, and expand the sample size to verify their applicability across different populations. Overall, this study provides a new method for the rapid diagnosis of kidney stones, which is expected to play an important role in clinical diagnostics.

A comparative study on breast cancer classification with stratified shuffle split and K-fold cross validation via ensembled machine learning

In breast cancer, early diagnosis and treatment method hold paramount significance for the augmented survival rates. Through a comprehensive dataset including clinical and genomic information, this study assesses the diverse analytical techniques used in breast cancer classification by the employment of four different machine learning algorithms. There were notable differences in classification findings, emphasizing the necessity of using adept analytical tools to improve the accuracy of breast cancer classification. Among individual algorithms, LGBM has the highest F1 score of 99.2% and a remarkable accuracy of 98.9%. Ensembles comprising AdaBoost, GBM, and RGF outperformed individual techniques with an astonishing 99.5% accuracy. The best ensemble algorithms prioritize features like worst texture, worst concave points, mean concave points, and mean texture, crucial for the classification. The examination of the advantages of ensemble learning methods, which combine predictions from many classifiers to improve classification performance, is at the heart of this the study. In particular, it is revealed how the k-fold and stratified shuffle split cross-validation methods differ in the classification results, providing clinicians a thorough understanding of the clinical ramifications to decipher the complex facets of breast cancer classification and identify crucial tumor traits that can distinguish malignant from benign cases.

Primary Thyroid Lymphoma: A Multi-Center Retrospective Review

Primary thyroid lymphoma (PTL) is rare and diagnosis is challenging. We conducted a multicenter retrospective study of patients with PTL from 1990 to 2023 to determine method of diagnosis, treatment, and outcomes. The study cohort included 31 patients with PTL; all had thyroid enlargement; 21 (68​%) had compressive symptoms, 11 (35​%) had hypothyroidism and 3 had (10​%) B symptoms. Diagnosis was established from incisional biopsy in 8 (26​%), needle biopsy in 4 (13​%), excisional lymph node biopsy in 1 (3​%), and thyroidectomy specimens in 18 (58​%). 15 (48​%) patients had Hashimoto thyroiditis. Treatment included chemotherapy in 19 (61​%); surgery alone in 7 (23​%); and radiation alone or with surgery in 5 (16​%) patients. One (3​%) patient recurred, and 4 (13​%) patients died after a median 4.2 years. Diagnosis of PTL was made in only 13​% of patients preoperatively. There may be opportunity for needle biopsy to facilitate earlier diagnosis and treatment.

Hybrid optimal feature selection-based iterative deep convolution learning for COVID-19 classification system

The COVID-19 pandemic has necessitated the development of innovative and efficient methods for early detection and diagnosis. Integrating Internet of Things (IoT) devices and applications in healthcare has facilitated various functions. This work aims to employ practical artificial intelligence (AI) approaches to extract meaningful information from the vast amount of IoT data to perform disease prediction tasks. However, traditional AI methods need help in feature analysis due to the complexity and scale of IoT data. So, this work implements the optimal iterative COVID-19 classification network (OICC-Net) using machine learning optimization and deep learning approaches. Initially, the preprocessing operation normalizes the dataset with uniform values. Here, random forest infused particle swarm-based black widow optimization (RFI-PS-BWO) algorithm was used to get the disease-specific patterns from SARS-CoV-2 (SC2), and other disease classes, where patterns of the SC2 virus are very similar to those of other virus classes. In addition, an iterative deep convolution learning (IDCL) feature selection method is used to distinguish features from the RFI-PS-BWO data. This iterative process enhances the performance of feature selection by providing improved representation and reducing the dimensionality of the input data. Then, a one-dimensional convolutional neural network (1D-CNN) was employed to classify and identify the extracted features from SC2 with no virus classes. The 1D-CNN model is trained using a large dataset of COVID-19 samples, enabling it to learn intricate patterns and make accurate predictions. It was tested and found that the proposed OICC-Net system is more accurate than current methods, with a score of 99.97 % for F1-score, 100 % for sensitivity, 100 % for specificity, 99.98 % for precision, and 99.99 % for recall.

Multi-scenario failure diagnosis for lithium-ion battery based on coupling PSO-SA-DBSCAN algorithm

The battery failure diagnosis methods are the main algorithms for online battery management system, but the conventional methods are mainly developed from calibrated thresholds detection, which will suffer from the degradation for full-lifespan operation and have no consideration of utilization for battery system information. Here we show innovative diagnosis methods for detecting battery failure both from online battery management system and cloud monitoring platform based on a particle swarm optimization-simulated annealing- density based spatial clustering of applications with noise (PSO-SA-DBSCAN) algorithm. By using the battery electrochemical impedance spectroscopy and voltage, the proposed method can solve the problem of battery abnormal degradation diagnosis, thermal runaway diagnosis and sampling failure diagnosis. Compared against conventional methods, the proposed method has less reliance on parameterization with better precision, which is validated from commercial prismatic cells and cloud data from a 117 serial-cell pack, with high accuracy and lower misinformation compared against conventional detecting methods, confirming the actual effectiveness for online battery pack.

Fault Diagnosis Method for Wind Turbine Gearbox Based on Ensemble-Refined Composite Multiscale Fluctuation-Based Reverse Dispersion Entropy.

The diagnosis of faults in wind turbine gearboxes based on signal processing represents a significant area of research within the field of wind power generation. This paper presents an intelligent fault diagnosis method based on ensemble-refined composite multiscale fluctuation-based reverse dispersion entropy (ERCMFRDE) for a wind turbine gearbox vibration signal that is nonstationary and nonlinear and for noise problems. Firstly, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and stationary wavelet transform (SWT) are adopted for signal decomposition, noise reduction, and restructuring of gearbox signals. Secondly, we extend the single coarse-graining processing method of refined composite multiscale fluctuation-based reverse dispersion entropy (RCMFRDE) to the multiorder moment coarse-grained processing method, extracting mixed fault feature sets for denoised signals. Finally, the diagnostic results are obtained based on the least squares support vector machine (LSSVM). The dataset collected during the gearbox fault simulation on the experimental platform is employed as the research object, and the experiments are conducted using the method proposed in this paper. The experimental results demonstrate that the proposed method is an effective and reliable approach for accurately diagnosing gearbox faults, exhibiting high diagnostic accuracy and a robust performance.

Fault signature extraction of rolling bearings under variable speed via time–frequency overlap group sparse representation

The high-fidelity extraction of the nonperiodic fault transients of rolling bearings under variable speed is of significant importance, while still a challenge for early-stage fault diagnosis of major equipment such as aero engines and wind turbines. A method termed continuous symmetric Laplace wavelet transform (CSLWT) enhanced time–frequency overlap group sparse (OGS) representation is proposed in this paper. A symmetric Laplace mother wavelet function is proposed which could satisfy the admissibility condition of wavelet decomposition and reconstruction, upon which the CSLWT is fabricated and adopted as the sparse representation dictionary to match the nonperiodic fault transients. Since the decomposition coefficients of nonperiodic fault impulses via the CSLWT exhibit group sparsity on the time–frequency plane with unknown intervals, the time–frequency OGS model is adopted and then solved for extraction of the nonperiodic fault transients. Moreover, a data driven, multi-objective optimization strategy is presented for determining the hyper-parameters of the sparse model. The correlated kurtosis of angular envelope signal (CK-AE) and harmonic-to-noise energy ratio of the order spectrum (HNR-OS) are introduced as the optimization objectives, and the Pareto front is solved based on the NSGA-II algorithm, upon which the acceptable solutions set is chosen from the Pareto front, and the proper hyper-parameters are further determined via proposed normalized solutions distance density sorting of the acceptable solutions set. The performance of the proposed method is validated via processing both simulated and experimental vibration signals, as well as compared with some tradition fault diagnosis methods.

A knowledge graph based intelligent auxiliary diagnosis and treatment system for primary tinnitus using traditional Chinese medicine

Primary tinnitus is a disabling disease with an unknown pathogenesis and a high incidence rate in China. Its diagnosis and treatment are complex and difficult to control. Although many treatments are available for primary tinnitus, their efficacy is often unsatisfactory. This paper proposes a new diagnosis and treatment method using knowledge graphs, and an intelligent assistant decision system is developed. To support diagnosis, a knowledge graph is created as a decision support tool using traditional Chinese medicine (TCM). Based on the knowledge graph, a model for the syndrome differentiation of tinnitus in TCM is built. At tinnitus treatment, an intelligent recommandation model for pentatonic music using knowledge graph based heterogeneous label propagation is then used to provide patients with personalized treatment plans. According to evaluation results, the proposed method achieves an accuracy of 87.1 % in tinnitus diagnosis. Compared with the control group, the recommended pentatonic music had a more obvious effect, and the efficacy of the five types of tinnitus was increased by 33.34 %, 33.33 %, 20 %, 26.67 %, 33.34 %, respectively. The system developed in this paper will help clinicians improve the diagnosis and treatment of tinnitus while reducing unnecessary medical expenses and offering significant social and economic benefits.

Application of a lyophilized CRISPR/Cas12a and RPA assay for rapid detection of Actinobacillus pleuropneumoniae

Actinobacillus pleuropneumoniae (A. pleuropneumoniae), a major pathogen in swine, causes severe respiratory illnesses, resulting in substantial economic losses for the global pork industry. Traditional detection methods are labor-intensive and time-consuming, highlighting the need for rapid, sensitive, and point-of-care diagnostic approaches. This study presents the development of an optimized CRISPR/Cas12a system integrated with a recombinase polymerase amplification (RPA) method targeting the apxIVA gene of A. pleuropneumoniae. The RPA-CRISPR/Cas12a assay demonstrated high specificity across various bacterial genomic DNA samples, with a detection limit as low as 3.0 copies/µL, validated under both excitation light and non-excitation light conditions. To address the challenge of reagent stability and eliminate cold chain dependency, we successfully stabilized assay reagents through lyophilization, incorporating saccharide-based protective agents. The optimized lyophilization parameters ensured the long-term stability and efficacy of the lyophilized reagents, offering a significant improvement over liquid storage. This method offers a new, efficient, and convenient method for rapid diagnosis of swine diseases.

A Novel Intelligent Fault Diagnosis Method for Bearings with Multi-Source Data and Improved GASA.

In recent years, single-source-data-based deep learning methods have made considerable strides in the field of fault diagnosis. Nevertheless, the extraction of useful information from multi-source data remains a challenge. In this paper, we propose a novel approach called the Genetic Simulated Annealing Optimization (GASA) method with a multi-source data convolutional neural network (MSCNN) for the fault diagnosis of rolling bearing. This method aims to identify bearing faults more accurately and make full use of multi-source data. Initially, the bearing vibration signal is transformed into a time-frequency graph using the continuous wavelet transform (CWT) and the signal is integrated with the motor current signal and fed into the network model. Then, a GASA-MSCNN fault diagnosis method is established to better capture the crucial information within the signal and identify various bearing health conditions. Finally, a rolling bearing dataset under different noisy environments is employed to validate the robustness of the proposed model. The experimental results demonstrate that the proposed method is capable of accurately identifying various types of rolling bearing faults, with an accuracy rate reaching up to 98% or higher even in variable noise environments. The experiments reveal that the new method significantly improves fault detection accuracy.

Intelligent Detection Method of Atrial Fibrillation by CEPNCC-BiLSTM Based on Long-Term Photoplethysmography Data.

Atrial fibrillation (AF) is the most prevalent arrhythmia characterized by intermittent and asymptomatic episodes. However, traditional detection methods often fail to capture the sporadic and intricate nature of AF, resulting in an increased risk of false-positive diagnoses. To address these challenges, this study proposes an intelligent AF detection and diagnosis method that integrates Complementary Ensemble Empirical Mode Decomposition, Power-Normalized Cepstral Coefficients, Bi-directional Long Short-term Memory (CEPNCC-BiLSTM), and photoelectric volumetric pulse wave technology to enhance accuracy in detecting AF. Compared to other approaches, the proposed method demonstrates faster preprocessing efficiency and higher sensitivity in detecting AF while effectively filtering out false alarms from photoplethysmography (PPG) recordings of non-AF patients. Considering the limitations of conventional AF detection evaluation systems that lack a comprehensive assessment of efficiency and accuracy, this study proposes the ET-score evaluation system based on F-measurement, which incorporates both computational speed and accuracy to provide a holistic assessment of overall performance. Evaluated with the ET-score, the CEPNCC-BiLSTM method outperforms EEMD-based improved Power-Normalized Cepstral Coefficients and Bi-directional Long Short-term Memory (EPNCC-BiLSTM), Support Vector Machine (SVM), EPNCC-SVM, and CEPNCC-SVM methods. Notably, this approach achieves an outstanding accuracy rate of up to 99.2% while processing PPG recordings within 5 s, highlighting its potential for long-term AF monitoring.

DERE-Net: A dual-encoder residual enhanced U-Net for muscle fiber segmentation of H&E images

Accurate segmentation of hematoxylin-eosin (H&E) muscle fiber images is crucial for the diagnosis of weightless muscle atrophy. However, uneven contrast, blurred fiber boundaries and adhesions in H&E images are still challenges for accurate segment complete muscle fiber. Although the existing single-encoder based U-shaped networks can extract local information about the target in H&E images, ignores remote dependencies between muscle fibers and low-level detail features, resulting in poor segmentation accuracy when facing H&E images with complex contrast. Therefore, we propose a dual-encoder residual enhanced U-Net segmentation model (DERE-Net) for effective segment muscle fibers in H&E images. DERE-Net uses Transformer and residual CNN to extract global context information and local features of muscle fibers in parallel, and fuses these two features to enable the model to accurately identify the muscle fibers. The multi-scale semantic information fusion (MSIF) module also utilizes the differences between multi-scale features to recover undetected muscle fiber, thus improving the network’s ability to recognize the object regions. In addition, the attention module is added to the skip connection, making the muscle fiber regions information more prominent and improving the robustness of the model. To demonstrate the DERE-Net effect, the comparative experiments are conducted on our own muscle fiber H&E image dataset, GLAS and MoNuSeg. DERE-Net achieved excellent performance on the muscle fiber H&E dataset (DSC 0.919), GLAS dataset (DSC 0.912), and MoNuSeg dataset (DSC 0.821). These results indicate that DERE-Net can accurately predict muscle fiber regions, providing a new method for accurate diagnosis of muscle atrophy in the future.

An approach leveraging radiomics and model checking for the automatic early diagnosis of adhesive capsulitis.

Adhesive Capsulitis of the shoulder is a painful pathology limiting shoulder movements, commonly known as "Frozen Shoulder". Since this pathology limits movement, it is important to make an early diagnosis. Diagnosing capsulitis relies on clinical assessment, although diagnostic imaging, such as Magnetic Resonance Imaging, can provide predictive or supportive information for specific characteristic signs. However, its diagnosis is not so simple nor so immediate, indeed it remains a difficult topic for many general radiologists and expert musculoskeletal radiologists. This study aims to investigate whether it is possible to use disease signs within a medical image to automatically diagnose Adhesive Capsulitis. To this purpose, we propose an automatic Model Checking-based approach to quickly diagnose the Adhesive Capsulitis taking as input the radiomic feature values from the medical images. Furthermore, we compare the performance achieved by our method with diagnostic results obtained by professional radiologists with different levels of experience. To the best of our knowledge, this is the first method for the automatic diagnosis of Adhesive Capsulitis of the Shoulder.