BACKGROUND Early detection of acute appendicitis(AA)in pediatric cases,critical to avoiding life-threatening complications such as perforation or abscess,remains challenging.AIM To evaluate the utility of abdominal ul...BACKGROUND Early detection of acute appendicitis(AA)in pediatric cases,critical to avoiding life-threatening complications such as perforation or abscess,remains challenging.AIM To evaluate the utility of abdominal ultrasonography(AUS)in diagnosing pediatric AA.METHODS Overall,102 pediatric patients(aged 3-12 years)suspected of having AA were enrolled and divided into the AA(n=78)and non-AA(n=24)groups.All children underwent AUS and computed tomography(CT).Comparative analyses regarding general patient characteristics and appendix-specific parameters were conducted.The diagnostic performance of AUS and CT in pediatric AA was evaluated.RESULTS All appendix-related parameters were greater in the AA group than in the non-AA group.The areas under the receiver-operating characteristic curves for pediatric AA diagnosis using AUS,CT,and AUS+CT were 0.870,0.824,and 0.931(all P<0.001),respectively(AUS:94.87%sensitivity,79.17%specificity;CT:89.74%sensitivity,75.00%specificity;combined:98.72%sensitivity,87.50%specificity).The positive predictive value(PPV),negative predictive value(NPV),accuracy rate,positive detection rate,and misdiagnosis rate of AUS were 93.67%,82.61%,91.18%,72.55%,and 20.83%,respectively.CT had a slightly lower PPV(92.11%)and NPV(69.23%),along with accuracy,positive detection,and misdiagnosis rates of 86.27%,68.63%,and 25%,respectively.Their combination improved performance,yielding 96.25%PPV,95.45%NPV,96.08%accuracy,75.49%positive detection rate,and 12.50%misdiagnosis rate.CONCLUSION AUS demonstrates certain diagnostic potential in AA diagnosis in pediatric patients,and its combination with CT further improves diagnostic efficacy.展开更多
Acute appendicitis remains one of the most common causes of emergency abdominal surgery globally.Imaging plays a pivotal role in confirming or excluding the diagnosis and identifying complications that influence manag...Acute appendicitis remains one of the most common causes of emergency abdominal surgery globally.Imaging plays a pivotal role in confirming or excluding the diagnosis and identifying complications that influence management pathways.This narrative review synthesizes contemporary evidence and consensusbased imaging protocols for appendicitis,with a focus on computed tomography,magnetic resonance imaging,and ultrasound.The article explores advanced diagnostic criteria,interpretation challenges,imaging algorithms derived from professional society guidelines,and special considerations including pregnancy and pediatric populations.Clinical practice recommendations by the World Society of Emergency Surgery,European Association of Endoscopic Surgery,American College of Radiology,and Infectious Diseases Society of America are incorporated to frame best practices.展开更多
Battery energy storage systems bolster power grids’absorption capacity,however,battery safety issues remain a formidable challenge.Timely and pre-cise fault diagnosis,coupled with early-stage fault warn-ings,is cruci...Battery energy storage systems bolster power grids’absorption capacity,however,battery safety issues remain a formidable challenge.Timely and pre-cise fault diagnosis,coupled with early-stage fault warn-ings,is crucial.This study introduces an eigen decompo-sition-based multi-fault diagnosis approach for lithi-umion battery packs,enabling online diagnosis of short circuits,electrical connection faults,and voltage sensor malfunctions.By incorporating an interleaved measurement topology,precise fault type differentiation is achieved.Eigenvector matching analysis is employed to increase sensitivity to fault characteristics and enhance robustness.The interleaved topology can be seamlessly integrated using common voltage measurement solutions,eliminating the need for additional design complexities,while sensor number redundancy enhances fault tolerance of battery management systems(BMS).A cloud-side collaboration method is proposed,where the BMS functions as an edge device for specific data computations,while the parameters are fine-tuned by the server through big data analytics.This approach circumvents cumbersome server calculations,thereby curbing server cost escalation.The edge computing process is divided into two steps,with partial calculations often sufficient to evaluate battery safety,thus reducing the computational load on edge devices.Several battery tests are conducted,and the results confirm the method’s capability,feasibility,and validity in early-stage fault diagnosis.展开更多
The critical components of gas turbines suffer from prolonged exposure to factors such as thermal oxidation,mechanical wear,and airflow disturbances during prolonged operation.These conditions can lead to a series of ...The critical components of gas turbines suffer from prolonged exposure to factors such as thermal oxidation,mechanical wear,and airflow disturbances during prolonged operation.These conditions can lead to a series of issues,including mechanical faults,air path malfunctions,and combustion irregularities.Traditional modelbased approaches face inherent limitations due to their inability to handle nonlinear problems,natural factors,measurement uncertainties,fault coupling,and implementation challenges.The development of artificial intelligence algorithms has provided an effective solution to these issues,sparking extensive research into data-driven fault diagnosis methodologies.The review mechanism involved searching IEEE Xplore,ScienceDirect,and Web of Science for peerreviewed articles published between 2019 and 2025,focusing on multi-fault diagnosis techniques.A total of 220 papers were identified,with 123 meeting the inclusion criteria.This paper provides a comprehensive review of diagnostic methodologies,detailing their operational principles and distinctive features.It analyzes current research hotspots and challenges while forecasting future trends.The study systematically evaluates the strengths and limitations of various fault diagnosis techniques,revealing their practical applicability and constraints through comparative analysis.Furthermore,this paper looks forward to the future development direction of this field and provides a valuable reference for the optimization and development of gas turbine fault diagnosis technology in the future.展开更多
Keratitis is a common ophthalmic disease associated with a high risk of blindness.Although deep learning(DL) based on slit-lamp images has shown great promise for automatic keratitis diagnosis,data heterogeneity and p...Keratitis is a common ophthalmic disease associated with a high risk of blindness.Although deep learning(DL) based on slit-lamp images has shown great promise for automatic keratitis diagnosis,data heterogeneity and privacy constraints hinder data sharing,limiting model generalization across multiple medical centers.To address these challenges,we propose a similarity-guided dynamic adjustment federated learning algorithm for automated keratitis diagnosis(SDAFL_AKD).SDAFL_AKD introduces a similarity-based regularization term during local model updates to alleviate catastrophic forgetting and employs a performance-driven dynamic aggregation mechanism on the server-side to adaptively weight client contributions,thereby enhancing global model robustness under non-independent and identically distributed(Non-IID) conditions.The framework is evaluated on slit-lamp images collected from four independent data sources encompassing keratitis,normal cornea,and other cornea abnormalities,and compared with Fed Avg,model-contrastive federated learning(MOON),stochastic controlled averaging for federated learning(SCAFFOLD) and single-center baseline models.Experimental results demonstrate that SDAFL_AKD consistently outperforms conventional methods,achieving average accuracies of 97.95% on a balanced dataset and 86.05% on an imbalanced smart phone-acquired dataset.Ablation studies further confirm the synergistic benefits of the similarity(SIM) and dynamic aggregation(DA) modules in improving multi-category recognition and generalization.These findings indicate the effectiveness of SDAFL_AKD for keratitis diagnosis under data heterogeneous and privacy-constrained conditions,providing a scalable solution for collaborative ophthalmic image analysis across institutions.展开更多
To ensure the safe and stable operation of rotating machinery,intelligent fault diagnosis methods hold significant research value.However,existing diagnostic approaches largely rely on manual feature extraction and ex...To ensure the safe and stable operation of rotating machinery,intelligent fault diagnosis methods hold significant research value.However,existing diagnostic approaches largely rely on manual feature extraction and expert experience,which limits their adaptability under variable operating conditions and strong noise environments,severely affecting the generalization capability of diagnostic models.To address this issue,this study proposes a multimodal fusion fault diagnosis framework based on Mel-spectrograms and automated machine learning(AutoML).The framework first extracts fault-sensitive Mel time–frequency features from acoustic signals and fuses them with statistical features of vibration signals to construct complementary fault representations.On this basis,automated machine learning techniques are introduced to enable end-to-end diagnostic workflow construction and optimal model configuration acquisition.Finally,diagnostic decisions are achieved by automatically integrating the predictions of multiple high-performance base models.Experimental results on a centrifugal pump vibration and acoustic dataset demonstrate that the proposed framework achieves high diagnostic accuracy under noise-free conditions and maintains strong robustness under noisy interference,validating its efficiency,scalability,and practical value for rotating machinery fault diagnosis.展开更多
Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction.However,several challenges remain.First,signal noise collected during blade operati...Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction.However,several challenges remain.First,signal noise collected during blade operation masks fault features,severely impairing the fault diagnosis performance of deep learning models.Second,current blade fault diagnosis often relies on single-sensor data,resulting in limited monitoring dimensions and ability to comprehensively capture complex fault states.To address these issues,a multi-sensor fusion-based wind turbine blade fault diagnosis method is proposed.Specifically,a CNN-Transformer Coupled Feature Learning Architecture is constructed to enhance the ability to learn complex features under noisy conditions,while a Weight-Aligned Data Fusion Module is designed to comprehensively and effectively utilize multi-sensor fault information.Experimental results of wind turbine blade fault diagnosis under different noise interferences show that higher accuracy is achieved by the proposed method compared to models with single-source data input,enabling comprehensive and effective fault diagnosis.展开更多
Deep learning-based methods have shown great potential in intelligent bearing fault diagnosis.However,most existing approaches suffer from the scarcity of labeled data,which often results in insufficient robustness un...Deep learning-based methods have shown great potential in intelligent bearing fault diagnosis.However,most existing approaches suffer from the scarcity of labeled data,which often results in insufficient robustness under complex working conditions and a general lack of interpretability.To address these challenges,we propose a physics-informed multimodal fault diagnosis framework based on few-shot learning,which integrates a 2D timefrequency image encoder and a 1Dvibration signal encoder.Specifically,we embed prior knowledge ofmulti-resolution analysis from signal processing into the model by designing a Laplace Wavelet Convolution(LWC)module,which enhances interpretability since wavelet coefficients naturally correspond to specific frequency and temporal structures.To further balance the guidance of physical priors with the flexibility of learnable representations,we introduce a parametric multi-kernel wavelet that employs channel-wise dynamic attention to adaptively select relevant wavelet bases,thereby improving the feature expressiveness.Moreover,we develop a Mahalanobis-Prototype Joint Metric,which constructs more accurate and distribution-consistent decision boundaries under few-shot conditions.Comprehensive experiments on the Case Western Reserve University(CWRU)and Paderborn University(PU)bearing datasets demonstrate the superior effectiveness,robustness,and interpretability of the proposed approach compared with state-of-the-art baselines.展开更多
Recently,Internet ofThings(IoT)has been increasingly integrated into the automotive sector,enabling the development of diverse applications such as the Internet of Vehicles(IoV)and intelligent connected vehicles.Lever...Recently,Internet ofThings(IoT)has been increasingly integrated into the automotive sector,enabling the development of diverse applications such as the Internet of Vehicles(IoV)and intelligent connected vehicles.Leveraging IoVtechnologies,operational data fromcore vehicle components can be collected and analyzed to construct fault diagnosis models,thereby enhancing vehicle safety.However,automakers often struggle to acquire sufficient fault data to support effective model training.To address this challenge,a robust and efficient federated learning method(REFL)is constructed for machinery fault diagnosis in collaborative IoV,which can organize multiple companies to collaboratively develop a comprehensive fault diagnosis model while keeping their data locally.In the REFL,the gradient-based adversary algorithm is first introduced to the fault diagnosis field to enhance the deep learning model robustness.Moreover,the adaptive gradient processing process is designed to improve the model training speed and ensure the model accuracy under unbalance data scenarios.The proposed REFL is evaluated on non-independent and identically distributed(non-IID)real-world machinery fault dataset.Experiment results demonstrate that the REFL can achieve better performance than traditional learning methods and are promising for real industrial fault diagnosis.展开更多
Tooth developmental anomalies are a group of disorders caused by unfavorable factors affecting the tooth development process,resulting in abnormalities in tooth number,structure,and morphology.These anomalies typicall...Tooth developmental anomalies are a group of disorders caused by unfavorable factors affecting the tooth development process,resulting in abnormalities in tooth number,structure,and morphology.These anomalies typically manifest during childhood,impairing dental function,maxillofacial development,and facial aesthetics,while also potentially impacting overall physical and mental health.The complex etiology and diverse clinical phenotypes of these anomalies pose significant challenges for prevention,early diagnosis,and treatment.As they usually emerge early in life,long-term management and multidisciplinary collaboration in dental care are essential.However,there is currently a lack of systematic clinical guidelines for the diagnosis and treatment of these conditions,adding to the difficulties in clinical practice.In response to this need,this expert consensus summarizes the classifications,etiology,typical clinical manifestations,and diagnostic criteria of tooth developmental anomalies based on current clinical evidence.It also provides prevention strategies and stage-specific clinical management recommendations to guide clinicians in diagnosis and treatment,promoting early intervention and standardized care for these anomalies.展开更多
Tuberculosis(TB)remains one of the most persistent and formidable public health challenges globally.Despite the ambitious targets set by the World Health Organization End TB Strategy,the path to elimination is fraught...Tuberculosis(TB)remains one of the most persistent and formidable public health challenges globally.Despite the ambitious targets set by the World Health Organization End TB Strategy,the path to elimination is fraught with obstacles.According to the Global Tuberculosis Report 2025,while global incidence has been stabilization,the burden of multidrug-resistant tuberculosis(MDR-TB)and the long-term sequelae facing survivors continue to hinder progress[1].展开更多
To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervis...To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.展开更多
Sarcopenia in the elderly is a syndrome characterized by age-related progressive loss of muscle mass, decline in muscle strength, and deterioration of muscle function. Its high incidence significantly increases the ri...Sarcopenia in the elderly is a syndrome characterized by age-related progressive loss of muscle mass, decline in muscle strength, and deterioration of muscle function. Its high incidence significantly increases the risk of falls, fractures, disability, and mortality among the elderly, posing a global public health challenge for geriatric health. Insulin-like growth factor-1 (IGF-1), a key cytokine regulating muscle growth, repair, and metabolism, exhibits a progressive decline in serum levels with aging and is closely associated with the onset and progression of sarcopenia in the elderly. This study reviews the research progress of IGF-1 in the diagnosis and efficacy prediction of sarcopenia in the elderly, providing theoretical references for precise diagnosis, treatment, and prognosis assessment of sarcopenia in the elderly.展开更多
[Objective]Leaf diseases significantly affect both the yield and quality of tea throughout the year.To address the issue of inadequate segmentation finesse in the current tea spot segmentation models,a novel diagnosis...[Objective]Leaf diseases significantly affect both the yield and quality of tea throughout the year.To address the issue of inadequate segmentation finesse in the current tea spot segmentation models,a novel diagnosis of the severity of tea spots was proposed in this research,designated as MDC-U-Net3+,to enhance segmentation accuracy on the base framework of U-Net3+.[Methods]Multi-scale feature fusion module(MSFFM)was incorporated into the backbone network of U-Net3+to obtain feature information across multiple receptive fields of diseased spots,thereby reducing the loss of features within the encoder.Dual multi-scale attention(DMSA)was incorporated into the skip connection process to mitigate the segmentation boundary ambiguity issue.This integration facilitates the comprehensive fusion of fine-grained and coarse-grained semantic information at full scale.Furthermore,the segmented mask image was subjected to conditional random fields(CRF)to enhance the optimization of the segmentation results[Results and Discussions]The improved model MDC-U-Net3+achieved a mean pixel accuracy(mPA)of 94.92%,accompanied by a mean Intersection over Union(mIoU)ratio of 90.9%.When compared to the mPA and mIoU of U-Net3+,MDC-U-Net3+model showed improvements of 1.85 and 2.12 percentage points,respectively.These results illustrated a more effective segmentation performance than that achieved by other classical semantic segmentation models.[Conclusions]The methodology presented herein could provide data support for automated disease detection and precise medication,consequently reducing the losses associated with tea diseases.展开更多
The misfolding,aggregation,and deposition of alpha-synuclein into Lewy bodies are pivotal events that trigger pathological changes in Parkinson's disease.Extracellular vesicles are nanosized lipidbilayer vesicles ...The misfolding,aggregation,and deposition of alpha-synuclein into Lewy bodies are pivotal events that trigger pathological changes in Parkinson's disease.Extracellular vesicles are nanosized lipidbilayer vesicles secreted by cells that play a crucial role in intercellular communication due to their diverse cargo.Among these,brain-derived extracellular vesicles,which are secreted by various brain cells such as neurons,glial cells,and Schwann cells,have garnered increasing attention.They serve as a promising tool for elucidating Parkinson's disease pathogenesis and for advancing diagnostic and therapeutic strategies.This review highlights the recent advancements in our understanding of brain-derived extracellular vesicles released into the blood and their role in the pathogenesis of Parkinson's disease,with specific emphasis on their involvement in the aggregation and spread of alpha-synuclein.Brain-derived extracellular vesicles contribute to disease progression through multiple mechanisms,including autophagy-lysosome dysfunction,neuroinflammation,and oxidative stress,collectively driving neurodegeneration in Parkinson's disease.Their application in Parkinson's disease diagnosis is a primary focus of this review.Recent studies have demonstrated that brainderived extracellular vesicles can be isolated from peripheral blood samples,as they carryα-synuclein and other key biomarkers such as DJ-1 and various micro RNAs.These findings highlight the potential of brain-derived extracellular vesicles,not only for the early diagnosis of Parkinson's disease but also for disease progression monitoring and differential diagnosis.Additionally,an overview of explorations into the potential therapeutic applications of brain-derived extracellular vesicles for Parkinson's disease is provided.Therapeutic strategies targeting brain-derived extracellular vesicles involve modulating the release and uptake of pathological alpha-synuclein-containing brain-derived extracellular vesicles to inhibit the spread of the protein.Moreover,brain-derived extracellular vesicles show immense promise as therapeutic delivery vehicles capable of transporting drugs into the central nervous system.Importantly,brain-derived extracellular vesicles also play a crucial role in neural regeneration by promoting neuronal protection,supporting axonal regeneration,and facilitating myelin repair,further enhancing their therapeutic potential in Parkinson's disease and other neurological disorders.Further clarification is needed of the methods for identifying and extracting brain-derived extracellular vesicles,and large-scale cohort studies are necessary to validate the accuracy and specificity of these biomarkers.Future research should focus on systematically elucidating the unique mechanistic roles of brain-derived extracellular vesicles,as well as their distinct advantages in the clinical translation of methods for early detection and therapeutic development.展开更多
Retinal diseases are a serious threat to human visual health and their early diagnosis is crucial.Currently,most of the retinal disease diagnostic algorithms are based on a single imaging modality of fundus color phot...Retinal diseases are a serious threat to human visual health and their early diagnosis is crucial.Currently,most of the retinal disease diagnostic algorithms are based on a single imaging modality of fundus color photography(FCP)or optical coherence tomography(OCT).These methods can only reflect retinal diseases to a certain extent,ignoring the speci ficity of modalities between different imaging modalities.In this research,a newmulti-scale feature fusion network(MSFF-Net)model for multi-modal retinal image diagnosis is proposed.The MSFF-Net model employs a dualbranch architecture design,enabling efficient learning and extraction of multi-modal feature information related to retinal diseases from CFP and OCT images.MSFF-Net improves disease diagnosis by combining multi-scale features of CFP and OCT images.When evaluated on challenging datasets,the model achieved an accuracy of 95.00%and an F1-score of 95.24%for retinal disease diagnosis.Even under low-quality dataset conditions,it maintained robust performance,with diagnostic accuracy and F1-scores of 71.50%and 71.73%,respectively.In addition,the MSFFNet model outperformed eight state-of-the-art single and multi-modal models in the comparison experiments.The proposed MSFF-Net model provides ophthalmologists with a more accurate and efficient diagnostic pathway that helps them detect and treat retinal diseases earlier.展开更多
Whole Slide Imaging (WSI) technology, as a revolutionary digital technology in the field of pathology, is gradually changing the traditional clinical pathological diagnosis model. By converting traditional glass patho...Whole Slide Imaging (WSI) technology, as a revolutionary digital technology in the field of pathology, is gradually changing the traditional clinical pathological diagnosis model. By converting traditional glass pathological sections into complete digital images through high-resolution scanning, it provides a new method for pathological diagnosis. Based on this, this paper studies the application of WSI technology in clinical pathological diagnosis, elaborates on its application value, analyzes the current application status, and proposes corresponding application countermeasures, aiming to provide reference for the standardized and popularized development of this technology in clinical pathological diagnosis.展开更多
To address the issue of abnormal energy consumption fluctuations in the converter steelmaking process,an integrated diagnostic method combining the gray wolf optimization(GWO)algorithm,support vector machine(SVM),and ...To address the issue of abnormal energy consumption fluctuations in the converter steelmaking process,an integrated diagnostic method combining the gray wolf optimization(GWO)algorithm,support vector machine(SVM),and K-means clustering was proposed.Eight input parameters—derived from molten iron conditions and external factors—were selected as feature variables.A GWO-SVM model was developed to accurately predict the energy consumption of individual heats.Based on the prediction results,the mean absolute percentage error and maximum relative error of the test set were employed as criteria to identify heats with abnormal energy usage.For these heats,the K-means clustering algorithm was used to determine benchmark values of influencing factors from similar steel grades,enabling root-cause diagnosis of excessive energy consumption.The proposed method was applied to real production data from a converter in a steel plant.The analysis reveals that heat sample No.44 exhibits abnormal energy consumption,due to gas recovery being 1430.28 kg of standard coal below the benchmark level.A secondary contributing factor is a steam recovery shortfall of 237.99 kg of standard coal.This integrated approach offers a scientifically grounded tool for energy management in converter operations and provides valuable guidance for optimizing process parameters and enhancing energy efficiency.展开更多
Owing to the harsh conditions,wind turbine bearings are prone to faults,and the resulting fault information is easily submerged by strong noise disturbance,making conventional diagnosis challenging.Therefore,this stud...Owing to the harsh conditions,wind turbine bearings are prone to faults,and the resulting fault information is easily submerged by strong noise disturbance,making conventional diagnosis challenging.Therefore,this study presents an innovative bearing fault diagnosis approach predicated on Parameter⁃Optimized Symplectic Geometry Mode Decomposition(POSGMD)and Improved Convolutional Neural Network(ICNN).Firstly,assisted by the relative entropy⁃based adaptive selection of embedding dimension,a POSGMD is presented to adaptively decompose the collected bearing vibration signals into various Symplectic Geometry Components(SGC),which can solve the problem of manual selection of the embedding dimension in the raw Symplectic Geometry Mode Decomposition(SGMD).Meanwhile,the signal reconstruction on the decomposed SGC is conducted based on kurtosis⁃weighted principle to obtain the reconstructed signals.Subsequently,the Continuous Wavelet Transform(CWT)of the reconstructed signals is calculated to generate the corresponding time⁃frequency images as sample set.Finally,an ICNN is introduced for model training and automatic recognition of bearing faults.Two case studies are used to validate the presented methods efficacy.Comparing the presented method with traditional fault diagnosis methods,experimental results show that it can achieve greater identification accuracy and superior anti⁃noise resilience.This work provides a practical and effective solution for fault diagnosis in wind turbine bearings,contributing to the timely detection of faults and the reliable operation of wind turbines or other rotational machinery in industrial applications.展开更多
Objective To systematically characterize the developmental trajectory and interdisciplinary integration of intelligent diagnosis in traditional Chinese medicine(TCM)through quantitative topic evolution analysis,we add...Objective To systematically characterize the developmental trajectory and interdisciplinary integration of intelligent diagnosis in traditional Chinese medicine(TCM)through quantitative topic evolution analysis,we addressed the fragmentation of existing research and clarified the long-term research structure and evolutionary patterns of the field.Methods A topic evolution analysis was performed on Chinese-language literature pertaining to intelligent diagnosis in TCM.Publications were retrieved from the China National Knowledge Infrastructure(CNKI),Wanfang Data,and China Science and Technology Journal Database(VIP),covering the period from database inception to July 3,2025.A hybrid segmentation approach,based on cumulative publication growth trends and inflection point detection,was applied to divide the research timeline into distinct stages.Subsequently,the latent Dirichlet allocation(LDA)model was used to extract research topics,followed by alignment and evolutionary analysis of topics across different stages.Results A total of 3919 publications published between 2003 and 2025 were included,and the research trajectory was divided into five stages based on data-driven breakpoint detection.The field exhibited a clear evolutionary shift from early rule-based systems and tonguepulse image and signal analysis(2006–2010),to machine-learning-based syndrome and prescription modeling(2011–2015),followed by deep-learning-driven pattern recognition and formula association(2016–2020).Since 2021,research has increasingly emphasized knowledge-graph construction,multimodal integration,and intelligent clinical decision-support systems,with recent studies(2024–2025)showing the emergence of large language models and agent-based diagnostic frameworks.Topic evolution analysis further revealed sustained cross-stage continuity in syndrome modeling and prescription association analysis,alongside the progressive consolidation of integrated intelligent diagnostic platforms.Conclusion By identifying key technological transitions and persistent core research themes,our findings offer a structured reference framework for the design of intelligent diagnostic systems,the construction of knowledge-driven clinical decision-support tools,and the alignment of AI models with TCM diagnostic logic.Importantly,the stage-based evolutionary insights derived from this analysis can inform future methodological choices,improve model interpretability and clinical applicability,and support the translation of intelligent TCM diagnosis from experimental research to real-world clinical practice.展开更多
文摘BACKGROUND Early detection of acute appendicitis(AA)in pediatric cases,critical to avoiding life-threatening complications such as perforation or abscess,remains challenging.AIM To evaluate the utility of abdominal ultrasonography(AUS)in diagnosing pediatric AA.METHODS Overall,102 pediatric patients(aged 3-12 years)suspected of having AA were enrolled and divided into the AA(n=78)and non-AA(n=24)groups.All children underwent AUS and computed tomography(CT).Comparative analyses regarding general patient characteristics and appendix-specific parameters were conducted.The diagnostic performance of AUS and CT in pediatric AA was evaluated.RESULTS All appendix-related parameters were greater in the AA group than in the non-AA group.The areas under the receiver-operating characteristic curves for pediatric AA diagnosis using AUS,CT,and AUS+CT were 0.870,0.824,and 0.931(all P<0.001),respectively(AUS:94.87%sensitivity,79.17%specificity;CT:89.74%sensitivity,75.00%specificity;combined:98.72%sensitivity,87.50%specificity).The positive predictive value(PPV),negative predictive value(NPV),accuracy rate,positive detection rate,and misdiagnosis rate of AUS were 93.67%,82.61%,91.18%,72.55%,and 20.83%,respectively.CT had a slightly lower PPV(92.11%)and NPV(69.23%),along with accuracy,positive detection,and misdiagnosis rates of 86.27%,68.63%,and 25%,respectively.Their combination improved performance,yielding 96.25%PPV,95.45%NPV,96.08%accuracy,75.49%positive detection rate,and 12.50%misdiagnosis rate.CONCLUSION AUS demonstrates certain diagnostic potential in AA diagnosis in pediatric patients,and its combination with CT further improves diagnostic efficacy.
文摘Acute appendicitis remains one of the most common causes of emergency abdominal surgery globally.Imaging plays a pivotal role in confirming or excluding the diagnosis and identifying complications that influence management pathways.This narrative review synthesizes contemporary evidence and consensusbased imaging protocols for appendicitis,with a focus on computed tomography,magnetic resonance imaging,and ultrasound.The article explores advanced diagnostic criteria,interpretation challenges,imaging algorithms derived from professional society guidelines,and special considerations including pregnancy and pediatric populations.Clinical practice recommendations by the World Society of Emergency Surgery,European Association of Endoscopic Surgery,American College of Radiology,and Infectious Diseases Society of America are incorporated to frame best practices.
基金supported in part by the National Natural Science Foundation of China(No.62133007)Shandong Provincial Key Research and Development Program(No.2024CXPT052).
文摘Battery energy storage systems bolster power grids’absorption capacity,however,battery safety issues remain a formidable challenge.Timely and pre-cise fault diagnosis,coupled with early-stage fault warn-ings,is crucial.This study introduces an eigen decompo-sition-based multi-fault diagnosis approach for lithi-umion battery packs,enabling online diagnosis of short circuits,electrical connection faults,and voltage sensor malfunctions.By incorporating an interleaved measurement topology,precise fault type differentiation is achieved.Eigenvector matching analysis is employed to increase sensitivity to fault characteristics and enhance robustness.The interleaved topology can be seamlessly integrated using common voltage measurement solutions,eliminating the need for additional design complexities,while sensor number redundancy enhances fault tolerance of battery management systems(BMS).A cloud-side collaboration method is proposed,where the BMS functions as an edge device for specific data computations,while the parameters are fine-tuned by the server through big data analytics.This approach circumvents cumbersome server calculations,thereby curbing server cost escalation.The edge computing process is divided into two steps,with partial calculations often sufficient to evaluate battery safety,thus reducing the computational load on edge devices.Several battery tests are conducted,and the results confirm the method’s capability,feasibility,and validity in early-stage fault diagnosis.
基金funded by the Science and Technology Vice President Project in Changping District,Beijing(Project Name:Research on multi-scale optimization and intelligent control technology of integrated energy systemProject number:202302007013).
文摘The critical components of gas turbines suffer from prolonged exposure to factors such as thermal oxidation,mechanical wear,and airflow disturbances during prolonged operation.These conditions can lead to a series of issues,including mechanical faults,air path malfunctions,and combustion irregularities.Traditional modelbased approaches face inherent limitations due to their inability to handle nonlinear problems,natural factors,measurement uncertainties,fault coupling,and implementation challenges.The development of artificial intelligence algorithms has provided an effective solution to these issues,sparking extensive research into data-driven fault diagnosis methodologies.The review mechanism involved searching IEEE Xplore,ScienceDirect,and Web of Science for peerreviewed articles published between 2019 and 2025,focusing on multi-fault diagnosis techniques.A total of 220 papers were identified,with 123 meeting the inclusion criteria.This paper provides a comprehensive review of diagnostic methodologies,detailing their operational principles and distinctive features.It analyzes current research hotspots and challenges while forecasting future trends.The study systematically evaluates the strengths and limitations of various fault diagnosis techniques,revealing their practical applicability and constraints through comparative analysis.Furthermore,this paper looks forward to the future development direction of this field and provides a valuable reference for the optimization and development of gas turbine fault diagnosis technology in the future.
基金Supported by the National Natural Science Foundation of China (No.62276210,82201148,62376215)the Key Research and Development Project of Shaanxi Province (No.2025CY-YBXM-044,2024GX-YBXM-137)+5 种基金the Ningbo Top Medical and Health Research Program (No.2023030716)the Natural Science Foundation of Ningbo (No.2023J390)Interdisciplinary Research Program of the School of Electronic Engineering,Xi’an University of Posts and Telecommunications (No.XKJC2501)the Open Fund of National Engineering Laboratory for Big Data System Computing Technology (No.SZU-BDSC-OF2024-16)the Key Research and Development Project of Xianyang (No.L2024-ZDYF-ZDYF-SF-0067)General Special Scientific Research Program of Shaanxi Provincial Department of Education (No.24JK0651)。
文摘Keratitis is a common ophthalmic disease associated with a high risk of blindness.Although deep learning(DL) based on slit-lamp images has shown great promise for automatic keratitis diagnosis,data heterogeneity and privacy constraints hinder data sharing,limiting model generalization across multiple medical centers.To address these challenges,we propose a similarity-guided dynamic adjustment federated learning algorithm for automated keratitis diagnosis(SDAFL_AKD).SDAFL_AKD introduces a similarity-based regularization term during local model updates to alleviate catastrophic forgetting and employs a performance-driven dynamic aggregation mechanism on the server-side to adaptively weight client contributions,thereby enhancing global model robustness under non-independent and identically distributed(Non-IID) conditions.The framework is evaluated on slit-lamp images collected from four independent data sources encompassing keratitis,normal cornea,and other cornea abnormalities,and compared with Fed Avg,model-contrastive federated learning(MOON),stochastic controlled averaging for federated learning(SCAFFOLD) and single-center baseline models.Experimental results demonstrate that SDAFL_AKD consistently outperforms conventional methods,achieving average accuracies of 97.95% on a balanced dataset and 86.05% on an imbalanced smart phone-acquired dataset.Ablation studies further confirm the synergistic benefits of the similarity(SIM) and dynamic aggregation(DA) modules in improving multi-category recognition and generalization.These findings indicate the effectiveness of SDAFL_AKD for keratitis diagnosis under data heterogeneous and privacy-constrained conditions,providing a scalable solution for collaborative ophthalmic image analysis across institutions.
基金supported in part by the National Natural Science Foundation of China under Grants 52475102 and 52205101in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2023A1515240021+1 种基金in part by the Young Talent Support Project of Guangzhou Association for Science and Technology(QT-2024-28)in part by the Youth Development Initiative of Guangdong Association for Science and Technology(SKXRC2025254).
文摘To ensure the safe and stable operation of rotating machinery,intelligent fault diagnosis methods hold significant research value.However,existing diagnostic approaches largely rely on manual feature extraction and expert experience,which limits their adaptability under variable operating conditions and strong noise environments,severely affecting the generalization capability of diagnostic models.To address this issue,this study proposes a multimodal fusion fault diagnosis framework based on Mel-spectrograms and automated machine learning(AutoML).The framework first extracts fault-sensitive Mel time–frequency features from acoustic signals and fuses them with statistical features of vibration signals to construct complementary fault representations.On this basis,automated machine learning techniques are introduced to enable end-to-end diagnostic workflow construction and optimal model configuration acquisition.Finally,diagnostic decisions are achieved by automatically integrating the predictions of multiple high-performance base models.Experimental results on a centrifugal pump vibration and acoustic dataset demonstrate that the proposed framework achieves high diagnostic accuracy under noise-free conditions and maintains strong robustness under noisy interference,validating its efficiency,scalability,and practical value for rotating machinery fault diagnosis.
基金supported by the China Three Gorges Corporation(No.NBZZ202300860)the National Natural Science Foundation of China(No.52275104)the Science and Technology Innovation Program of Hunan Province(No.2023RC3097).
文摘Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction.However,several challenges remain.First,signal noise collected during blade operation masks fault features,severely impairing the fault diagnosis performance of deep learning models.Second,current blade fault diagnosis often relies on single-sensor data,resulting in limited monitoring dimensions and ability to comprehensively capture complex fault states.To address these issues,a multi-sensor fusion-based wind turbine blade fault diagnosis method is proposed.Specifically,a CNN-Transformer Coupled Feature Learning Architecture is constructed to enhance the ability to learn complex features under noisy conditions,while a Weight-Aligned Data Fusion Module is designed to comprehensively and effectively utilize multi-sensor fault information.Experimental results of wind turbine blade fault diagnosis under different noise interferences show that higher accuracy is achieved by the proposed method compared to models with single-source data input,enabling comprehensive and effective fault diagnosis.
文摘Deep learning-based methods have shown great potential in intelligent bearing fault diagnosis.However,most existing approaches suffer from the scarcity of labeled data,which often results in insufficient robustness under complex working conditions and a general lack of interpretability.To address these challenges,we propose a physics-informed multimodal fault diagnosis framework based on few-shot learning,which integrates a 2D timefrequency image encoder and a 1Dvibration signal encoder.Specifically,we embed prior knowledge ofmulti-resolution analysis from signal processing into the model by designing a Laplace Wavelet Convolution(LWC)module,which enhances interpretability since wavelet coefficients naturally correspond to specific frequency and temporal structures.To further balance the guidance of physical priors with the flexibility of learnable representations,we introduce a parametric multi-kernel wavelet that employs channel-wise dynamic attention to adaptively select relevant wavelet bases,thereby improving the feature expressiveness.Moreover,we develop a Mahalanobis-Prototype Joint Metric,which constructs more accurate and distribution-consistent decision boundaries under few-shot conditions.Comprehensive experiments on the Case Western Reserve University(CWRU)and Paderborn University(PU)bearing datasets demonstrate the superior effectiveness,robustness,and interpretability of the proposed approach compared with state-of-the-art baselines.
基金supported in part by National key R&D projects(2024YFB4207203)National Natural Science Foundation of China(52401376)+3 种基金the Zhejiang Provincial Natural Science Foundation of China under Grant(No.LTGG24F030004)Hangzhou Key Scientific Research Plan Project(2024SZD1A24)“Pioneer”and“Leading Goose”R&DProgramof Zhejiang(2024C03254,2023C03154)Jiangxi Provincial Gan-Po Elite Support Program(Major Academic and Technical Leaders Cultivation Project,20243BCE51180).
文摘Recently,Internet ofThings(IoT)has been increasingly integrated into the automotive sector,enabling the development of diverse applications such as the Internet of Vehicles(IoV)and intelligent connected vehicles.Leveraging IoVtechnologies,operational data fromcore vehicle components can be collected and analyzed to construct fault diagnosis models,thereby enhancing vehicle safety.However,automakers often struggle to acquire sufficient fault data to support effective model training.To address this challenge,a robust and efficient federated learning method(REFL)is constructed for machinery fault diagnosis in collaborative IoV,which can organize multiple companies to collaboratively develop a comprehensive fault diagnosis model while keeping their data locally.In the REFL,the gradient-based adversary algorithm is first introduced to the fault diagnosis field to enhance the deep learning model robustness.Moreover,the adaptive gradient processing process is designed to improve the model training speed and ensure the model accuracy under unbalance data scenarios.The proposed REFL is evaluated on non-independent and identically distributed(non-IID)real-world machinery fault dataset.Experiment results demonstrate that the REFL can achieve better performance than traditional learning methods and are promising for real industrial fault diagnosis.
基金supported by the grants No.82370912 from the National Natural Science Foundation of ChinaNo.2022020801010499 from the Bureau of Science and Technology of Wuhan,ChinaNo.2042023kf0231 from the Fundamental Research Funds for the Central Universities,China。
文摘Tooth developmental anomalies are a group of disorders caused by unfavorable factors affecting the tooth development process,resulting in abnormalities in tooth number,structure,and morphology.These anomalies typically manifest during childhood,impairing dental function,maxillofacial development,and facial aesthetics,while also potentially impacting overall physical and mental health.The complex etiology and diverse clinical phenotypes of these anomalies pose significant challenges for prevention,early diagnosis,and treatment.As they usually emerge early in life,long-term management and multidisciplinary collaboration in dental care are essential.However,there is currently a lack of systematic clinical guidelines for the diagnosis and treatment of these conditions,adding to the difficulties in clinical practice.In response to this need,this expert consensus summarizes the classifications,etiology,typical clinical manifestations,and diagnostic criteria of tooth developmental anomalies based on current clinical evidence.It also provides prevention strategies and stage-specific clinical management recommendations to guide clinicians in diagnosis and treatment,promoting early intervention and standardized care for these anomalies.
文摘Tuberculosis(TB)remains one of the most persistent and formidable public health challenges globally.Despite the ambitious targets set by the World Health Organization End TB Strategy,the path to elimination is fraught with obstacles.According to the Global Tuberculosis Report 2025,while global incidence has been stabilization,the burden of multidrug-resistant tuberculosis(MDR-TB)and the long-term sequelae facing survivors continue to hinder progress[1].
基金supported by the National Natural Science Foundation of China Funded Project(Project Name:Research on Robust Adaptive Allocation Mechanism of Human Machine Co-Driving System Based on NMS Features,Project Approval Number:52172381).
文摘To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.
文摘Sarcopenia in the elderly is a syndrome characterized by age-related progressive loss of muscle mass, decline in muscle strength, and deterioration of muscle function. Its high incidence significantly increases the risk of falls, fractures, disability, and mortality among the elderly, posing a global public health challenge for geriatric health. Insulin-like growth factor-1 (IGF-1), a key cytokine regulating muscle growth, repair, and metabolism, exhibits a progressive decline in serum levels with aging and is closely associated with the onset and progression of sarcopenia in the elderly. This study reviews the research progress of IGF-1 in the diagnosis and efficacy prediction of sarcopenia in the elderly, providing theoretical references for precise diagnosis, treatment, and prognosis assessment of sarcopenia in the elderly.
文摘[Objective]Leaf diseases significantly affect both the yield and quality of tea throughout the year.To address the issue of inadequate segmentation finesse in the current tea spot segmentation models,a novel diagnosis of the severity of tea spots was proposed in this research,designated as MDC-U-Net3+,to enhance segmentation accuracy on the base framework of U-Net3+.[Methods]Multi-scale feature fusion module(MSFFM)was incorporated into the backbone network of U-Net3+to obtain feature information across multiple receptive fields of diseased spots,thereby reducing the loss of features within the encoder.Dual multi-scale attention(DMSA)was incorporated into the skip connection process to mitigate the segmentation boundary ambiguity issue.This integration facilitates the comprehensive fusion of fine-grained and coarse-grained semantic information at full scale.Furthermore,the segmented mask image was subjected to conditional random fields(CRF)to enhance the optimization of the segmentation results[Results and Discussions]The improved model MDC-U-Net3+achieved a mean pixel accuracy(mPA)of 94.92%,accompanied by a mean Intersection over Union(mIoU)ratio of 90.9%.When compared to the mPA and mIoU of U-Net3+,MDC-U-Net3+model showed improvements of 1.85 and 2.12 percentage points,respectively.These results illustrated a more effective segmentation performance than that achieved by other classical semantic segmentation models.[Conclusions]The methodology presented herein could provide data support for automated disease detection and precise medication,consequently reducing the losses associated with tea diseases.
基金supported by the National Natural Science Foundation of China,No.822712782019 Wuhan Huanghe Talents Program+3 种基金2020 Wuhan Medical Research Project,No.20200206010123032021 Hubei Youth Top-notch Talent Training Program2022 Outstanding Youth Project of Natural Science Foundation of Hubei Province,No.2022CFA106Medical Research Program of Huatongguokang,No.2023HT036(all to NX)。
文摘The misfolding,aggregation,and deposition of alpha-synuclein into Lewy bodies are pivotal events that trigger pathological changes in Parkinson's disease.Extracellular vesicles are nanosized lipidbilayer vesicles secreted by cells that play a crucial role in intercellular communication due to their diverse cargo.Among these,brain-derived extracellular vesicles,which are secreted by various brain cells such as neurons,glial cells,and Schwann cells,have garnered increasing attention.They serve as a promising tool for elucidating Parkinson's disease pathogenesis and for advancing diagnostic and therapeutic strategies.This review highlights the recent advancements in our understanding of brain-derived extracellular vesicles released into the blood and their role in the pathogenesis of Parkinson's disease,with specific emphasis on their involvement in the aggregation and spread of alpha-synuclein.Brain-derived extracellular vesicles contribute to disease progression through multiple mechanisms,including autophagy-lysosome dysfunction,neuroinflammation,and oxidative stress,collectively driving neurodegeneration in Parkinson's disease.Their application in Parkinson's disease diagnosis is a primary focus of this review.Recent studies have demonstrated that brainderived extracellular vesicles can be isolated from peripheral blood samples,as they carryα-synuclein and other key biomarkers such as DJ-1 and various micro RNAs.These findings highlight the potential of brain-derived extracellular vesicles,not only for the early diagnosis of Parkinson's disease but also for disease progression monitoring and differential diagnosis.Additionally,an overview of explorations into the potential therapeutic applications of brain-derived extracellular vesicles for Parkinson's disease is provided.Therapeutic strategies targeting brain-derived extracellular vesicles involve modulating the release and uptake of pathological alpha-synuclein-containing brain-derived extracellular vesicles to inhibit the spread of the protein.Moreover,brain-derived extracellular vesicles show immense promise as therapeutic delivery vehicles capable of transporting drugs into the central nervous system.Importantly,brain-derived extracellular vesicles also play a crucial role in neural regeneration by promoting neuronal protection,supporting axonal regeneration,and facilitating myelin repair,further enhancing their therapeutic potential in Parkinson's disease and other neurological disorders.Further clarification is needed of the methods for identifying and extracting brain-derived extracellular vesicles,and large-scale cohort studies are necessary to validate the accuracy and specificity of these biomarkers.Future research should focus on systematically elucidating the unique mechanistic roles of brain-derived extracellular vesicles,as well as their distinct advantages in the clinical translation of methods for early detection and therapeutic development.
基金supported by the National Natural Science Foundation of China(Nos.82472104 and U24B2053)the Natural Science Basic Research Program of Shaanxi(No.2025JC-JCQN-023)+2 种基金the Key Core Technology Research and Development of Shaanxi(No.2024QY2-GJHX-03)the Innovation Capability Support Program of Shaanxi(Program No.2023-CX-TD-54)the Xidian University Specially Funded Project for Interdisciplinary Exploration(No.TZJHF202510).
文摘Retinal diseases are a serious threat to human visual health and their early diagnosis is crucial.Currently,most of the retinal disease diagnostic algorithms are based on a single imaging modality of fundus color photography(FCP)or optical coherence tomography(OCT).These methods can only reflect retinal diseases to a certain extent,ignoring the speci ficity of modalities between different imaging modalities.In this research,a newmulti-scale feature fusion network(MSFF-Net)model for multi-modal retinal image diagnosis is proposed.The MSFF-Net model employs a dualbranch architecture design,enabling efficient learning and extraction of multi-modal feature information related to retinal diseases from CFP and OCT images.MSFF-Net improves disease diagnosis by combining multi-scale features of CFP and OCT images.When evaluated on challenging datasets,the model achieved an accuracy of 95.00%and an F1-score of 95.24%for retinal disease diagnosis.Even under low-quality dataset conditions,it maintained robust performance,with diagnostic accuracy and F1-scores of 71.50%and 71.73%,respectively.In addition,the MSFFNet model outperformed eight state-of-the-art single and multi-modal models in the comparison experiments.The proposed MSFF-Net model provides ophthalmologists with a more accurate and efficient diagnostic pathway that helps them detect and treat retinal diseases earlier.
文摘Whole Slide Imaging (WSI) technology, as a revolutionary digital technology in the field of pathology, is gradually changing the traditional clinical pathological diagnosis model. By converting traditional glass pathological sections into complete digital images through high-resolution scanning, it provides a new method for pathological diagnosis. Based on this, this paper studies the application of WSI technology in clinical pathological diagnosis, elaborates on its application value, analyzes the current application status, and proposes corresponding application countermeasures, aiming to provide reference for the standardized and popularized development of this technology in clinical pathological diagnosis.
基金support from the National Key R&D Program of China(Grant No.2020YFB1711100).
文摘To address the issue of abnormal energy consumption fluctuations in the converter steelmaking process,an integrated diagnostic method combining the gray wolf optimization(GWO)algorithm,support vector machine(SVM),and K-means clustering was proposed.Eight input parameters—derived from molten iron conditions and external factors—were selected as feature variables.A GWO-SVM model was developed to accurately predict the energy consumption of individual heats.Based on the prediction results,the mean absolute percentage error and maximum relative error of the test set were employed as criteria to identify heats with abnormal energy usage.For these heats,the K-means clustering algorithm was used to determine benchmark values of influencing factors from similar steel grades,enabling root-cause diagnosis of excessive energy consumption.The proposed method was applied to real production data from a converter in a steel plant.The analysis reveals that heat sample No.44 exhibits abnormal energy consumption,due to gas recovery being 1430.28 kg of standard coal below the benchmark level.A secondary contributing factor is a steam recovery shortfall of 237.99 kg of standard coal.This integrated approach offers a scientifically grounded tool for energy management in converter operations and provides valuable guidance for optimizing process parameters and enhancing energy efficiency.
基金Jiangsu Association for Science and Technology Youth Talent Support Project(Grant No.JSTJ-2024-031)National Natural Science Foundation of China(Grant No.52005265)Natural Science Fund for Colleges and Universities in Jiangsu Province(Grant No.20KJB460002)。
文摘Owing to the harsh conditions,wind turbine bearings are prone to faults,and the resulting fault information is easily submerged by strong noise disturbance,making conventional diagnosis challenging.Therefore,this study presents an innovative bearing fault diagnosis approach predicated on Parameter⁃Optimized Symplectic Geometry Mode Decomposition(POSGMD)and Improved Convolutional Neural Network(ICNN).Firstly,assisted by the relative entropy⁃based adaptive selection of embedding dimension,a POSGMD is presented to adaptively decompose the collected bearing vibration signals into various Symplectic Geometry Components(SGC),which can solve the problem of manual selection of the embedding dimension in the raw Symplectic Geometry Mode Decomposition(SGMD).Meanwhile,the signal reconstruction on the decomposed SGC is conducted based on kurtosis⁃weighted principle to obtain the reconstructed signals.Subsequently,the Continuous Wavelet Transform(CWT)of the reconstructed signals is calculated to generate the corresponding time⁃frequency images as sample set.Finally,an ICNN is introduced for model training and automatic recognition of bearing faults.Two case studies are used to validate the presented methods efficacy.Comparing the presented method with traditional fault diagnosis methods,experimental results show that it can achieve greater identification accuracy and superior anti⁃noise resilience.This work provides a practical and effective solution for fault diagnosis in wind turbine bearings,contributing to the timely detection of faults and the reliable operation of wind turbines or other rotational machinery in industrial applications.
基金Grants of National Natural Science Foundation of China(82274685).
文摘Objective To systematically characterize the developmental trajectory and interdisciplinary integration of intelligent diagnosis in traditional Chinese medicine(TCM)through quantitative topic evolution analysis,we addressed the fragmentation of existing research and clarified the long-term research structure and evolutionary patterns of the field.Methods A topic evolution analysis was performed on Chinese-language literature pertaining to intelligent diagnosis in TCM.Publications were retrieved from the China National Knowledge Infrastructure(CNKI),Wanfang Data,and China Science and Technology Journal Database(VIP),covering the period from database inception to July 3,2025.A hybrid segmentation approach,based on cumulative publication growth trends and inflection point detection,was applied to divide the research timeline into distinct stages.Subsequently,the latent Dirichlet allocation(LDA)model was used to extract research topics,followed by alignment and evolutionary analysis of topics across different stages.Results A total of 3919 publications published between 2003 and 2025 were included,and the research trajectory was divided into five stages based on data-driven breakpoint detection.The field exhibited a clear evolutionary shift from early rule-based systems and tonguepulse image and signal analysis(2006–2010),to machine-learning-based syndrome and prescription modeling(2011–2015),followed by deep-learning-driven pattern recognition and formula association(2016–2020).Since 2021,research has increasingly emphasized knowledge-graph construction,multimodal integration,and intelligent clinical decision-support systems,with recent studies(2024–2025)showing the emergence of large language models and agent-based diagnostic frameworks.Topic evolution analysis further revealed sustained cross-stage continuity in syndrome modeling and prescription association analysis,alongside the progressive consolidation of integrated intelligent diagnostic platforms.Conclusion By identifying key technological transitions and persistent core research themes,our findings offer a structured reference framework for the design of intelligent diagnostic systems,the construction of knowledge-driven clinical decision-support tools,and the alignment of AI models with TCM diagnostic logic.Importantly,the stage-based evolutionary insights derived from this analysis can inform future methodological choices,improve model interpretability and clinical applicability,and support the translation of intelligent TCM diagnosis from experimental research to real-world clinical practice.
