Conventional Diagnosis Methods Research Articles

An early fault warning and diagnosis model based on a two-step analysis strategy for wind turbine bearings

Early bearing fault diagnosis plays a crucial role in ensuring the reliability and safety of wind turbines. This process encounters two primary challenges, that is, weak fault characteristics and strong background noise. Consequently, they restrict the effectiveness of conventional diagnosis methods. To address these difficulties, a two-step early fault diagnosis model is proposed based on an anomaly monitoring index [Formula: see text] and improved successive variational mode decomposition-fast spectral correlation (ISVMD-FSC). Initially, [Formula: see text] enables early anomaly detection. Subsequently, the fault type is further identified using ISVMD-FSC. The ISVMD adaptive decomposition of early warning signals can effectively reduce the noise and isolate the fault impact characteristics. Then, the fault characteristic frequency is extracted using spectral coherence analysis to achieve the purpose of fault diagnosis. This model is tested and validated on simulated data, laboratory accelerated bearing life span data, and real high-speed bearing fault data of wind turbines. Notably, the comparative study shows that [Formula: see text] is capable of detecting the fault earlier than the spectral L2/L1 norm, the sum of the weighted normalized square envelope, index 8, and index 9. Additionally, the fault feature extraction effect of ISVMD-FSC is better than many more advanced time-frequency analysis methods. These results emphasize the model’s excellent performance in early bearing fault diagnosis and its good generalization ability.

The application status of sequencing technology in global respiratory infectious disease diagnosis.

Next-generation sequencing (NGS) has revolutionized clinical microbiology, particularly in diagnosing respiratory infectious diseases and conducting epidemiological investigations. This narrative review summarizes conventional methods for routine respiratory infection diagnosis, including culture, smear microscopy, immunological assays, image techniques as well as polymerase chain reaction(PCR). In contrast to conventional methods, there is a new detection technology, sequencing technology, and here we mainly focus on the next-generation sequencing NGS, especially metagenomic NGS(mNGS). NGS offers significant advantages over traditional methods. Firstly, mNGS eliminates assumptions about pathogens, leading to faster and more accurate results, thus reducing diagnostic time. Secondly, it allows unbiased identification of known and novel pathogens, offering broad-spectrum coverage. Thirdly, mNGS not only identifies pathogens but also characterizes microbiomes, analyzes human host responses, and detects resistance genes and virulence factors. It can complement targeted sequencing for bacterial and fungal classification. Unlike traditional methods affected by antibiotics, mNGS is less influenced due to the extended survival of pathogen DNA in plasma, broadening its applicability. However, barriers to full integration into clinical practice persist, primarily due to cost constraints and limitations in sensitivity and turnaround time. Despite these challenges, ongoing advancements aim to improve cost-effectiveness and efficiency, making NGS a cornerstone technology for global respiratory infection diagnosis.

Digital twin-driven attention-guided convolutional networks for intelligent fault diagnosis across different domains

Fault diagnosis of rolling bearings is crucial in industries to ensure the reliability, safety, and economic feasibility of mechanical systems. Conventional data-driven methods for fault diagnosis depend on pre-existing datasets containing various failure modes for training. However, obtaining such datasets can be challenging, especially in critical industrial environments, hindering the applicability of these methods across different scenarios. To address this challenge, this research introduces a novel approach called a digital twin-driven attention-guided convolutional network (DTACN) for bearing fault diagnostics with limited data. This study offers two significant enhancements: (1) Development of an intricate digital twin model for bearings, which includes multi-scale Kinetic simulations of the bearing’s operational parameters. This model utilizes the architectural characteristics of the bearing and the severity of the failure to predict the vibratory system’s response accurately; and (2) Establishment of a domain adaptation-based framework employing deep convolutional models and attention mechanism to transfer knowledge from simulated signals to real signals. This framework facilitates efficient fault recognition even in scenarios with limited prior knowledge. The effectiveness of the proposed approach has been rigorously validated through comprehensive experiments.

The Value of BLUE Protocol in the Diagnosis of Acute Onset of Respiratory Distress in Critically Ill Patient

Abstract To detect the value of BLUE protocol in the diagnosis of acute onset of respiratory distress in critically ill patients This cross-sectional study conducted on 60 patients by performing ultrasonography on consecutive patients admitted to the ICU with acute onset of respiratory distress (dyspnea), comparing lung ultrasonography results on initial presentation with the final diagnosis with conventional method of diagnosis by the ICU team. The BLUE protocol proved its importance, availability, easiness of application, accuracy and its ability to reach a final correct diagnosis in 90%of the patients. The lung U/S showed much better results than the CXR, but still the CT chest was more superior to the lung U/S as the sensitivity of the BLUE protocol in the diagnosis of pneumonia, pulmonary edema, pneumothorax, pleural effusion and COPD was 91.4%, 92.8%, 90%, 88.8% and 78.6 respectively. The lung U/S (BLUE protocol) is a bedside, reliable, dynamic, rapid, and noninvasive technique, these results suggest it could have a significant role in the diagnostic workup of acute onset of respiratory distress in the ICU.

Semi-Supervised Informer for the Compound Fault Diagnosis of Industrial Robots.

The increasing deployment of industrial robots in manufacturing requires accurate fault diagnosis. Online monitoring data typically consist of a large volume of unlabeled data and a small quantity of labeled data. Conventional intelligent diagnosis methods heavily rely on supervised learning with abundant labeled data. To address this issue, this paper presents a semi-supervised Informer algorithm for fault diagnosis modeling, leveraging the Informer model's long- and short-term memory capabilities and the benefits of semi-supervised learning to handle the diagnosis of a small amount of labeled data alongside a substantial amount of unlabeled data. An experimental study is conducted using real-world industrial robot monitoring data to assess the proposed algorithm's effectiveness, demonstrating its ability to deliver accurate fault diagnosis despite limited labeled samples.

Cost analyses for malaria molecular diagnosis for research planners in India and beyond

ABSTRACT Background Malaria elimination mandates early and accurate diagnosis of infection. Although malaria diagnosis is programmatically dependent on microscopy/RDTs, molecular diagnosis has much better diagnostic accuracy. Higher cost of molecular diagnoses is a recognized challenge for use at the point of care. Because funding is always a recognized constraint, we performed financial cost-analyses of available molecular platforms for better utilization of available budget. Methods Two strategies were applied to deduce the cost per sample. Strategy 1 included recurring components (RC) in minimum pack size, and biologist’s time whereas strategy 2 included only RC and non-recurring components and costs are calculated for sample sizes (1–1,000,000) to infer the sample size effect. Results Spin column-based manual DNA extraction (US$ 3.93 per sample) is the lowest-cost method, followed by magnetic bead-based automated, semi-automated, and PCI-based manual method. Further, DNA extraction cost per sample via spin column-based manual method and semi-automated method decreases with an increase in sample size up to 10,000. Real-time PCRs are ~ 2-fold more economical than conventional PCR, regardless of sample size. Conclusions This study is the first for malaria to estimate systematic molecular diagnosis financial costs. Kit-based and automated methods may replace conventional DNA extraction and amplification methods for a frugal high-throughput diagnosis.

Early autism diagnosis based on path signature and Siamese unsupervised feature compressor.

Autism spectrum disorder has been emerging as a growing public health threat. Early diagnosis of autism spectrum disorder is crucial for timely, effective intervention and treatment. However, conventional diagnosis methods based on communications and behavioral patterns are unreliable for children younger than 2 years of age. Given evidences of neurodevelopmental abnormalities in autism spectrum disorder infants, we resort to a novel deep learning-based method to extract key features from the inherently scarce, class-imbalanced, and heterogeneous structural MR images for early autism diagnosis. Specifically, we propose a Siamese verification framework to extend the scarce data, and an unsupervised compressor to alleviate data imbalance by extracting key features. We also proposed weight constraints to cope with sample heterogeneity by giving different samples different voting weights during validation, and used Path Signature to unravel meaningful developmental features from the two-time point data longitudinally. We further extracted machine learning focused brain regions for autism diagnosis. Extensive experiments have shown that our method performed well under practical scenarios, transcending existing machine learning methods and providing anatomical insights for autism early diagnosis.

Overlapping infections of Mycobacterium canariasense and Nocardia farcinica in an immunocompetent patient: A case report

BACKGROUND Infections by non-tuberculous mycobacteria (NTM) have become more common in recent years. Mycobacterium canariasense (M. canariasense ) was first reported as an opportunistic pathogen in 2004, but there have been very few case reports since then. Nocardia is a genus of aerobic and Gram-positive bacilli, and these species are also opportunistic pathogens and in the Mycobacteriales order. Conventional methods for diagnosis of NTM are inefficient. Metagenomic next-generation sequencing (mNGS) can rapidly detect many pathogenic microorganisms, even rare species. Most NTM and Nocardia infections occur in immunocompromised patients with atypical clinical symptoms. There are no previous reports of infection by M. canariasense and Nocardia farcinica (N. farcinica ), especially in immunocompetent patients. This case report describes an immunocompetent 52-year-old woman who had overlapping infections of M. canariasense , N. farcinica , and Candida parapsilosis (C. parapsilosis ) based on mNGS. CASE SUMMARY A 52-year-old woman presented with a productive cough and chest pain for 2 wk, and recurrent episodes of moderate-grade fever for 1 wk. She received antibiotics for 1 wk at a local hospital, and experienced defervescence, but the productive cough and chest pain persisted. We collected samples of a lung lesion and alveolar lavage fluid for mNGS. The lung tissue was positive for M. canariasense , N. farcinica , and C. parapsilosis , and the alveolar lavage fluid was positive for M. canariasense . The diagnosis was pneumonia, and application of appropriate antibiotic therapy cured the patient. CONCLUSION Etiological diagnosis is critical for patients with infectious diseases. mNGS can identify rare and novel pathogens, and does not require a priori knowledge.

An End-to-end Deep Clustering Method with Consistency and Complementarity Attention Mechanism for Multisensor Fault Diagnosis

Deep clustering methods have found successful applications in single-sensor data fault diagnosis. However, most of these methods employ separate optimization strategies that overlook the interaction between feature learning and clustering. Moreover, conventional deep learning methods for fault diagnosis often disregard the consistent and complementary information inherent in the multisensor data, resulting in unsatisfactory multisensor fault diagnosis performance. In this study, we introduce a novel End-to-end Deep Clustering Method with Consistency and Complementarity Attention Mechanism, termed EDCM-CCAM, tailored for multisensor fault diagnosis. Firstly, multiple deep autoencoder networks are utilized to concurrently extract the deep representation features from various sensor inputs. Secondly, we introduce a Consistency and Complementarity Attention Mechanism (CCAM) to facilitate multisensor feature fusion, accompanied by the design of two distinct loss functions to fully exploit the consistent and complementary information within multisensor data. Finally, fault pattern recognition in multisensor data is accomplished through Kullback-Leibler (KL) divergence-based clustering, while a joint optimization strategy is employed to simultaneously optimize all components of the EDCM-CCAM. The efficacy of the proposed method is validated on a gearbox dataset, demonstrating superior performance in multisensor fault diagnosis compared to alternative methods.

Cover Picture: Metal‐Organic Frameworks as a Fluorescent Probe for Detection of Pathogenic Biomarkers in Human Urine (BKCS 4/2024) Seyeon Jeong, Hoi Ri Moon

The cover picture illustrates metal‐organic frameworks (MOFs) being used as fluorescent probes to detect biomarkers in human urine. MOF‐based fluorescent probes facilitate the diagnosis of diseases, including pheochromocytoma, toluene exposure, vitamin deficiencies, gout, hyperuricemia, cancers, various body disorders, and teratogenic effects, with low detection limits. Unlike conventional disease diagnosis methods such as biopsies or blood collection, detecting pathogenic biomarkers in human urine is non‐invasive and carries less risk. More details are available in the article by Seyeon Jeong and Hoi Ri Moon. image

Signal processing collaborated with deep learning: An interpretable FIRNet for industrial intelligent diagnosis

Due to the neglect of prior characteristics and the lack of explicit constraints on fault knowledge, conventional intelligent diagnosis methods suffer from great hardships in exacting fault-sensitive information and making explainable decisions, resulting in poor interpretability and inferior robustness. Motivated by the excellent multiscale analysis performance of signal processing and the powerful feature mining ability of deep learning, this study proposes an interpretable FIRNet for equipment intelligent diagnosis under strong noise environments. It consists of a well-crafted FIRLayer and a deep learning backbone. Inspired by the modulation principle that fault-sensitive components are normally modulated into multiscale mode characteristics, multiple ex-ante interpretable filters with two learnable parameters, including center frequency and bandwidth, are analytically designed to process the sequence signal sets, represented as signal-processing-based FIRLayer where the extracted multiscale feature maps are taken as an interpretable status information expression. Subsequently, multiscale convolutional kernels are established to extract the high-level feature maps and further make the final diagnostic decisions, represented as the deep learning backbone. The simulated and experimental results show that the proposed FIRNets have higher identification precision compared to the other nine deep learning models. Specially, three aspects, including model interpretability, noise robustness capacity and edge intelligent diagnosis, are further analyzed to illustrate the interpretable advantages of FIRNets. Hereinto, feature visualizations of the FIRLayer and mode decision contributions of the deep learning backbone are in-depth analyzed to verify the interpretable feature representation and decision-making principle of FIRNets. The results indicate that FIRNets have superior ex-post interpretability compared to other methods. Focusing on industrial practices, an efficient edge diagnosis system based on a pruned FIRNet is established, and an online diagnosis accuracy of more than 99% has been achieved. It can be foreseen that the proposed FIRNets show great potential and competitiveness to promote the edge computing application of equipment intelligent diagnosis.

Structural Health Monitoring of Solid Rocket Motors: From Destructive Testing to Perspectives of Photonic-Based Sensing

In the realm of space exploration, solid rocket motors (SRMs) play a pivotal role due to their reliability and high thrust-to-weight ratio. Serving as boosters in space launch vehicles and employed in military systems, and other critical & emerging applications, SRMs’ structural integrity monitoring, is of paramount importance. Traditional maintenance approaches often prove inefficient, leading to either unnecessary interventions or unexpected failures. Condition-based maintenance (CBM) emerges as a transformative strategy, incorporating advanced sensing technologies and predictive analytics. By continuously monitoring crucial parameters such as temperature, pressure, and strain, CBM enables real-time analysis, ensuring timely intervention upon detecting anomalies, thereby optimizing SRM lifecycle management. This paper critically evaluates conventional SRM health diagnosis methods and explores emerging sensing technologies. Photonic sensors and fiber-optic sensors, in particular, demonstrate exceptional promise. Their enhanced sensitivity and broad measurement range allow precise monitoring of temperature, strain, pressure, and vibration, capturing subtle changes indicative of degradation or potential failures. These sensors enable comprehensive, non-intrusive monitoring of multiple SRM locations simultaneously. Integrated with data analytics, these sensors empower predictive analysis, facilitating SRM behavior prediction and optimal maintenance planning. Ultimately, CBM, bolstered by advanced photonic sensors, promises enhanced operational availability, reduced costs, improved safety, and efficient resource allocation in SRM applications.

Comparative evaluation of the detection rate, workflow and associated costs of a multiplex PCR panel versus conventional methods in diagnosis of infectious gastroenteritis.

Introduction. Infectious gastroenteritis is a common reason for consulting a physician. Although most cases of gastrointestinal illness are self-limiting, the identification of the etiologic pathogen by stool specimen analysis is important in cases of more severe illness and for epidemiological reasons.Due to the broad range of causative pathogens, the conventional examination of a stool specimen is labour-intensive and usually requires different diagnostic methods. Multiplex PCR tests [e.g. BioFire Gastrointestinal (GI) Panel] allow the rapid detecting of up to 22 pathogens in one test.Hypothesis. Using a multiplex PCR panel to test stool specimens for infectious gastroenteritis pathogens can improve the detection rate, reduce the time-to-result and hands-on time and lower the costs of a microbiology laboratory.Aim. This study was aimed at evaluating the detection rate, the workflow and associated costs of stool specimen management using the BioFire GI Panel versus conventional methods.Methodology. Stool specimens were evaluated prospectively during the routine operation. Pathogen detection rate, hands-on time, time-to-result and material and personnel costs were determined for the BioFire GI Panel and conventional methods-the latter based on physician request and excluding viral testing.Results. Analysing 333 specimens collected between 2019 and 2020, the detection rate of enteropathogens was significantly higher with a positivity rate of 39.9 % using the multiplex PCR panel compared with 15.0 % using the conventional methods. The BioFire GI Panel presented results in a median time of 2.2 h compared with 77.5 h for culture and 22.1 h for antigen testing, noting that no tests were performed at weekends except for toxinogenic Clostridioides difficile. Based on list prices, the BioFire GI Panel was nine times more expensive compared with conventional methods, whereas hands-on-time was significantly lower using the BioFire GI Panel.Conclusion. Multiplex PCR panels are valuable tools for laboratory identification of infectious agents causing diarrhoea. The higher costs of such a multiplex PCR panel might be outweighed by the higher detection rate, ease of handling, rapid results and most likely improved patient management. However, these panels do not provide information on antimicrobial susceptibility testing. Therefore, if this is necessary for targeted therapy or if outbreak monitoring and control is required, specimens must still be cultured.

Metal–organic frameworks as a fluorescent probe for detection of pathogenic biomarkers in human urine

AbstractMetal–organic frameworks (MOFs) are actively explored as sensing materials owing to their high porosity, substantial surface area, and structural diversity, providing diverse chemical and physical properties. Their fluorescent characteristics are pivotal, enabling MOFs to function as probes for detecting specific target molecules. Unlike conventional disease diagnosis methods such as biopsies or blood collection, the detection of pathogenic biomarkers in human urine is conventional and less risky. While mass spectroscopy identifies proteins and metabolites in urine at high costs, MOF test papers offer a convenient solution. These papers confirm the existence of the target molecules through naked‐eye observation upon UV light irradiation at specific wavelengths. MOF‐based fluorescent probes facilitate disease diagnosis, including pheochromocytoma, toluene exposure, vitamin deficiencies, gout, hyperuricemia, cancers, body disorders, and teratogenicity, with low detection limits. This review delves into recent developments in MOF fluorescent probes, covering synthesis, emission spectra, and sensing mechanisms.

Identification of primary metabolites in fungal species of Trichophyton mentagrophyte and Trichophyton rubrum by NMR spectroscopy.

Superficial mycoses are fungal infections limited to the outermost layers of the skin and its appendages. The chief causative agents of these mycoses are dermatophytes and yeasts. The diagnosis of dermatophytosis can be made by direct mycological examination with potassium hydroxide (10%-30%) of biological material obtained from patients with suspected mycosis, providing results more rapid than fungal cultures, which may take days or weeks. This information, together with clinical history and laboratory diagnosis, ensures that the appropriate treatment is initiated promptly. However, false negative results are obtained in 5%-15%, by conventional methods of diagnosis of dermatophytosis. To study the metabolic profiles of the commonly occurring dermatophytes by NMR spectroscopy. We have used 1D and 2D Nuclear Magnetic Resonance (NMR) experiments along with Human Metabolome Database (HMDB) and Chenomx database search for identification of primary metabolites in the methanol extract of two fungal species: Trichophyton mentagrophyte (T. mentagrophyte) and Trichophyton rubrum (T. rubrum). Both standard strains and representative number of clinical isolates of these two species were investigated. Further, metabolic profiles obtained were analysed using multivariate analysis. We have identified 23 metabolites in the T. mentagrophyte and another 23 metabolites in T. rubrum. Many important metabolites like trehalose, proline, mannitol, acetate, GABA and several other amino acids were detected, which provide the necessary components for fungal growth and metabolism. Altered metabolites were defined between Trichophyton mentagrophyte and T. rubrum strains. We have detected many metabolites in the two fungal species T. mentagrophyte and T. rubrum by using NMR spectroscopy. NMR spectroscopy provides a holistic snapshot of the metabolome of an organism. Key metabolic differences were identified between the two fungal strains. We need to perform more studies on metabolite profiling of the samples from these species for their rapid diagnosis and prompt treatment.

Research on motor rotation anomaly detection based on improved VMD algorithm

PurposeThe electromechanical brake system is leading the latest development trend in railway braking technology. The tolerance stack-up generated during the assembly and production process catalyzes the slight geometric dimensioning and tolerancing between the motor stator and rotor inside the electromechanical cylinder. The tolerance leads to imprecise brake control, so it is necessary to diagnose the fault of the motor in the fully assembled electromechanical brake system. This paper aims to present improved variational mode decomposition (VMD) algorithm, which endeavors to elucidate and push the boundaries of mechanical synchronicity problems within the realm of the electromechanical brake system.Design/methodology/approachThe VMD algorithm plays a pivotal role in the preliminary phase, employing mode decomposition techniques to decompose the motor speed signals. Afterward, the error energy algorithm precision is utilized to extract abnormal features, leveraging the practical intrinsic mode functions, eliminating extraneous noise and enhancing the signal’s fidelity. This refined signal then becomes the basis for fault analysis. In the analytical step, the cepstrum is employed to calculate the formant and envelope of the reconstructed signal. By scrutinizing the formant and envelope, the fault point within the electromechanical brake system is precisely identified, contributing to a sophisticated and accurate fault diagnosis.FindingsThis paper innovatively uses the VMD algorithm for the modal decomposition of electromechanical brake (EMB) motor speed signals and combines it with the error energy algorithm to achieve abnormal feature extraction. The signal is reconstructed according to the effective intrinsic mode functions (IMFS) component of removing noise, and the formant and envelope are calculated by cepstrum to locate the fault point. Experiments show that the empirical mode decomposition (EMD) algorithm can effectively decompose the original speed signal. After feature extraction, signal enhancement and fault identification, the motor mechanical fault point can be accurately located. This fault diagnosis method is an effective fault diagnosis algorithm suitable for EMB systems.Originality/valueBy using this improved VMD algorithm, the electromechanical brake system can precisely identify the rotational anomaly of the motor. This method can offer an online diagnosis analysis function during operation and contribute to an automated factory inspection strategy while parts are assembled. Compared with the conventional motor diagnosis method, this improved VMD algorithm can eliminate the need for additional acceleration sensors and save hardware costs. Moreover, the accumulation of online detection functions helps improve the reliability of train electromechanical braking systems.

Modern Diagnostic Technologies in the Study of the Field Material Collected from the Natural Plague Foci of the Kyrgyz Republic in 2023

The aim of the work was to analyze the results of investigating the field material obtained from natural plague foci of the Kyrgyz Republic (KR) in 2023, using modern diagnostic technologies.Materials and methods. 1435 biological samples from the Tien Shan, Alai and Talas high-mountain foci of the Kyrgyz Republic were studied using conventional methods of laboratory diagnosis of plague: microbiological, immunological, biological; as well as modern molecular-genetic methods. Testing of the obtained samples for the presence of plague pathogen DNA was carried out using RT-PCR; and the presence of antibodies to the plague microbe was detected by enzyme-linked immunosorbent assay (ELISA). Molecular identification of Yersinia pestis strains according to their appurtenance to subspecies, biovars, and phylogenetic lineages was performed by RT-PCR using the method of identifying single-nucleotide substitutions based on the analysis of melting curves of products.Results and discussion. An approach to the molecular identification of Y. pestis strains from plague foci of the KR has been developed and validated through identifying singlenucleotide substitutions using the analysis of product melting curves (HMR-analysis) with a set of designed primers. It has been established that Y. pestis strains isolated in the Sarydzhaz autonomous focus of the Tien Shan high-mountain focus belong to the biovar antiqua of the main subspecies, phylogenetic branch 0.ANT5. The phylogenetic relation of Y. pestis strains isolated in 2023 was studied based on genome-wide SNP analysis. Areas of epizootic activity in Eastern Alai have been identified. The data obtained indicate the sustained activation of plague foci in the KR. Areas of the territory of the KR that are promising for paleogenomic research are also discussed.

Explainable Artificial Intelligence for Fault Diagnosis of Industrial Processes

Process monitoring is important for ensuring operational reliability and preventing occupational accidents. In recent years, data-driven methods such as machine learning and deep learning have been preferred for fault detection and diagnosis. In particular, unsupervised learning algorithms, such as auto-encoders, exhibit good detection performance, even for unlabeled data from complex processes. However, decisions generated from deep-neural-network-based models are difficult to interpret and cannot provide explanatory insight to users. We address this issue by proposing a new fault diagnosis method using explainable artificial intelligence to break the traditional trade-off between the accuracy and interpretability of deep learning model. First, an adversarial auto-encoder model for fault detection is built and then interpreted through the integration of Shapley additive explanations (SHAP) with a combined monitoring index. Using SHAP values, a diagnosis is conducted by allocating credit for detected faults, deviations from a normal state, among its input variables. The proposed diagnosis method can consider not only reconstruction space but also latent space unlike conventional method, which evaluate only reconstruction error. The proposed method was applied to two chemical process systems and compared with conventional diagnosis methods. The results highlight that the proposed method achieves the exact fault diagnosis for single and multiple faults and, also, distinguishes the global pattern of various fault types.

Application of Artificial Intelligence to Diagnosis of Laryngeal Lesions Using Laryngoscopy

Laryngeal diseases have a significant impact on quality of life and often require timely and accurate diagnosis for effective management. Conventional methods of diagnosis, such as manual inspection of laryngoscopic images, have limitations in terms of accuracy and efficiency. The integration of artificial intelligence (AI) and machine learning techniques in laryngoscopic image analysis has emerged as a promising approach to enhance diagnostic accuracy, streamline workflow, and improve patient outcomes. This review paper provides an in-depth analysis of the recent advancements in AI-driven laryngoscopic image analysis for the diagnosis of laryngeal diseases, also covering methodologies, challenges, and future prospects.

Diagnostic Accuracy of Cd1a Immuno Histochemical Evaluation in Comparison with H & E and Giemsa for Cutaneous Leishmaniasis

Background: Cutaneous leishmaniasis remains a global health challenge, with diagnosis largely dependent on the visualization of Leishmania parasites in infected tissue. Traditional staining methods, such as Hematoxylin and Eosin (H&E) and Giemsa, have been the cornerstone of histopathological diagnosis, but they come with limitations in sensitivity and specificity. Objective: This study aimed to evaluate the diagnostic accuracy of CD1a immunohistochemical staining in the detection of leishmania amastigotes in skin biopsies and compare its efficacy with H&E and Giemsa staining methods. Methods: A total of 110 skin biopsy samples with a strong clinical suspicion of cutaneous leishmaniasis were examined using H&E, Giemsa, and CD1a immunohistochemical staining techniques. The sensitivity and specificity of these methods were analyzed and compared to ascertain the most effective diagnostic approach. Results: The study identified leishmania amastigotes in 78% of the cases using H&E staining, while Giemsa staining resulted in amastigote detection in 82% of cases. Notably, CD1a immunohistochemical staining showed a remarkable sensitivity, identifying amastigotes in 95% of the samples. The increase in detection rate with CD1a staining was statistically significant when compared to H&E (p<0.05), with CD1a revealing amastigotes in an additional 23% of cases where H&E staining was negative. Giemsa staining was able to detect amastigotes in three cases that were not visible with H&E, suggesting its potential role as a complementary technique in certain diagnostic scenarios. Conclusion: The study concludes that CD1a immunohistochemical staining significantly enhances the detection of leishmania amastigotes and should be considered as an adjunct to conventional staining methods in the histopathological diagnosis of cutaneous leishmaniasis. Future diagnostic protocols could benefit from the incorporation of CD1a staining to improve accuracy.