Multimodal deep learning has emerged as a key paradigm in contemporary medical diagnostics,advancing precision medicine by enabling integration and learning from diverse data sources.The exponential growth of high-dim...Multimodal deep learning has emerged as a key paradigm in contemporary medical diagnostics,advancing precision medicine by enabling integration and learning from diverse data sources.The exponential growth of high-dimensional healthcare data,encompassing genomic,transcriptomic,and other omics profiles,as well as radiological imaging and histopathological slides,makes this approach increasingly important because,when examined separately,these data sources only offer a fragmented picture of intricate disease processes.Multimodal deep learning leverages the complementary properties of multiple data modalities to enable more accurate prognostic modeling,more robust disease characterization,and improved treatment decision-making.This review provides a comprehensive overview of the current state of multimodal deep learning approaches in medical diagnosis.We classify and examine important application domains,such as(1)radiology,where automated report generation and lesion detection are facilitated by image-text integration;(2)histopathology,where fusion models improve tumor classification and grading;and(3)multi-omics,where molecular subtypes and latent biomarkers are revealed through cross-modal learning.We provide an overview of representative research,methodological advancements,and clinical consequences for each domain.Additionally,we critically analyzed the fundamental issues preventing wider adoption,including computational complexity(particularly in training scalable,multi-branch networks),data heterogeneity(resulting from modality-specific noise,resolution variations,and inconsistent annotations),and the challenge of maintaining significant cross-modal correlations during fusion.These problems impede interpretability,which is crucial for clinical trust and use,in addition to performance and generalizability.Lastly,we outline important areas for future research,including the development of standardized protocols for harmonizing data,the creation of lightweight and interpretable fusion architectures,the integration of real-time clinical decision support systems,and the promotion of cooperation for federated multimodal learning.Our goal is to provide researchers and clinicians with a concise overview of the field’s present state,enduring constraints,and exciting directions for further research through this review.展开更多
In a recent case report in the World Journal of Clinical Cases,emphasized the crucial role of rapidly and accurately identifying pathogens to optimize patient treatment outcomes.Laboratory-on-a-chip(LOC)technology has...In a recent case report in the World Journal of Clinical Cases,emphasized the crucial role of rapidly and accurately identifying pathogens to optimize patient treatment outcomes.Laboratory-on-a-chip(LOC)technology has emerged as a transformative tool in health care,offering rapid,sensitive,and specific identification of microorganisms.This editorial provides a comprehensive overview of LOC technology,highlighting its principles,advantages,applications,challenges,and future directions.Success studies from the field have demonstrated the practical benefits of LOC devices in clinical diagnostics,epidemiology,and food safety.Comparative studies have underscored the superiority of LOC technology over traditional methods,showcasing improvements in speed,accuracy,and portability.The future integration of LOC with biosensors,artificial intelligence,and data analytics promises further innovation and expansion.This call to action emphasizes the importance of continued research,investment,and adoption to realize the full potential of LOC technology in improving healthcare outcomes worldwide.展开更多
This article provides a short review on the importance of the detailed analysis of a Langmuir probe I-V trace in a multi-Maxwellian plasma,and discuss proper procedures analyzing Langmuir probe I-V traces in bi-Maxwel...This article provides a short review on the importance of the detailed analysis of a Langmuir probe I-V trace in a multi-Maxwellian plasma,and discuss proper procedures analyzing Langmuir probe I-V traces in bi-Maxwellian and triple-Maxwellian Electron Energy Distribution Function(EEDF)plasmas.Discus⁃sion and demonstration of procedures include the treatment of the ion saturation current,electron saturation cur⁃rent,space-charge effects on the I-V trace,and most importantly how to properly isolate and fit for each electron group present in an I-V trace reflecting a mult-Maxwellian EEDF,as well as how having a multi-Maxwellian EEDF affects the procedures of treating the ion and electron saturation currents.Shortcomings of common improp⁃er procedures are discussed and demonstrated with simulated I-V traces to show how these procedures gives false measurements.展开更多
Artificial Intelligence(AI)is fundamentally transforming medical diagnostics,driving advancements that enhance accuracy,efficiency,and personalized patient care.This narrative review explores AI integration across var...Artificial Intelligence(AI)is fundamentally transforming medical diagnostics,driving advancements that enhance accuracy,efficiency,and personalized patient care.This narrative review explores AI integration across various diagnostic domains,emphasizing its role in improving clinical decision-making.The evolution of medical diagnostics from traditional observational methods to sophisticated imaging,laboratory tests,and molecular diagnostics lays the foundation for understanding AI’s impact.Modern diagnostics are inherently complex,influenced by multifactorial disease presentations,patient variability,cognitive biases,and systemic factors like data overload and interdisciplinary collaboration.AI-enhanced clinical decision support systems utilize both knowledge-based and non-knowledge-based approaches,employing machine learning and deep learning algorithms to analyze vast datasets,identify patterns,and generate accurate differential diagnoses.AI’s potential in diagnostics is demonstrated through applications in genomics,predictive analytics,and early disease detection,with successful case studies in oncology,radiology,pathology,ophthalmology,dermatology,gastroenterology,and psychiatry.These applications demonstrate AI’s ability to process complex medical data,facilitate early intervention,and extend specialized care to underserved populations.However,integrating AI into diagnostics faces significant limitations,including technical challenges related to data quality and system integration,regulatory hurdles,ethical concerns about transparency and bias,and risks of misinformation and overreliance.Addressing these challenges requires robust regulatory frameworks,ethical guidelines,and continuous advancements in AI technology.The future of AI in diagnostics promises further innovations in multimodal AI,genomic data integration,and expanding access to high-quality diagnostic services globally.Responsible and ethical implementation of AI will be crucial to fully realize its potential,ensuring AI serves as a powerful ally in achieving diagnostic excellence and improving global health care outcomes.This narrative review emphasizes AI’s pivotal role in shaping the future of medical diagnostics,advocating for sustained investment and collaborative efforts to harness its benefits effectively.展开更多
Electrochemical impedance spectroscopy(EIS)offers valuable insights into the dynamic behaviors of lithium-ion batteries,making it a powerful and non-invasive tool for evaluating battery health.However,EIS falls short ...Electrochemical impedance spectroscopy(EIS)offers valuable insights into the dynamic behaviors of lithium-ion batteries,making it a powerful and non-invasive tool for evaluating battery health.However,EIS falls short in quantitatively determining the degree of specific degradation modes,which are essential for improving battery lifespan.This study introduces a novel approach employing deep neural networks enhanced by an attention mechanism to identify the degree of degradation modes.The proposed method can automatically determine the most relevant frequency ranges for each degradation mode,which can link impedance characteristics to battery degradation.To overcome the limitation of scarce labeled experimental data,simulation results derived from mechanistic models are incorporated into the model.Validation results demonstrate that the proposed method could achieve root mean square errors below 3%for estimating loss of lithium inventory and loss of active material of the positive electrode,and below 4%for estimating loss of active material of the negative electrode while requiring only 25%of early-stage experimental degradation data.By integrating simulation results,the proposed method achieves a reduction in maximum estimation error ranging from 42.92%to 66.30%across different temperatures and operating conditions compared to the baseline model trained solely on experimental data.展开更多
In Unani medicine,Bawl(urine)is recognized as a key diagnostic tool,with humoural imbalances assessed via parameters like color,consistency,sediment,clarity,froth,odor,and volume.This conceptual review explores how th...In Unani medicine,Bawl(urine)is recognized as a key diagnostic tool,with humoural imbalances assessed via parameters like color,consistency,sediment,clarity,froth,odor,and volume.This conceptual review explores how these classical diagnostic indicators may be contextualized alongside modern urinalysis markers(e.g.,bilirubin,protein,ketones,and sedimentation)and examined through emerging artificial intelligence(AI)frameworks.Potential applications include ResNet-18 for color classification,You Only Look Once version 8(YOLOv8)for sediment detection,long short-term memory(LSTM)for viscosity estimation,and EfficientDet for froth analysis,with standardized urine images/videos forming the basis of future datasets.Additionally,a comparative ontology is proposed to align Unani perspectives with diagnostic approaches in traditional Chinese medicine,encouraging cross-system integration.By synthesizing classical epistemology with computational intelligence,this review highlights pathways for developing AI-based decision support systems to promote personalized,accessible,and telemedicine-enabled healthcare.展开更多
As a critical technology for industrial system reliability and safety,machine monitoring and fault diagnostics have advanced transformatively with large language models(LLMs).This paper reviews LLM-based monitoring an...As a critical technology for industrial system reliability and safety,machine monitoring and fault diagnostics have advanced transformatively with large language models(LLMs).This paper reviews LLM-based monitoring and diagnostics methodologies,categorizing them into in-context learning,fine-tuning,retrievalaugmented generation,multimodal learning,and time series approaches,analyzing advances in diagnostics and decision support.It identifies bottlenecks like limited industrial data and edge deployment issues,proposing a three-stage roadmap to highlight LLMs’potential in shaping adaptive,interpretable PHM frameworks.展开更多
Background:Head and neck cancers(HNC)account for a significant global health burden,with increasing incidence rates and complex treatment requirements.Traditional diagnostic and therapeutic approaches,while effective,...Background:Head and neck cancers(HNC)account for a significant global health burden,with increasing incidence rates and complex treatment requirements.Traditional diagnostic and therapeutic approaches,while effective,often result in substantial morbidity and limitations in personalized care.This review provides a comprehensive overview of the latest innovations in diagnostics and therapeutic strategies for HNC from 2015 to 2024.Methods:A review of literature focused on pe-reviewed journals,clinical trial databases,and oncology conference proceedings.Key areas include molecular diagnostics,imaging technologies,minimally invasive surgeries,and innovative therapeutic strategies.Results:Technologies like liquid biopsy next-generation sequencing(NGS)have greatly improved diagnostic accuracy and personalization in HNC care.These advancements have improved survival rates and enhanced patients’quality of life.Personalized therapeutic approaches,including immune checkpoint inhibitors,precision radiation therapy,and surgery,have led to enhanced treatment efficacy while reducing side effects.The integration of AI and machine learning into diagnostics and treatment planning shows promise in optimizing clinical decision-making and predicting treatment outcomes.Conclusion:The current innovations in diagnostics and therapeutics are reshaping the management of head and neck cancer,offering more tailored and effective approaches to care.Overall,the continuous integration of these innovations in clinical practice is reshaping HNC treatment and improving patient outcomes and survival rates.Future research should focus on further refining these technologies,addressing challenges related to accessibility,and exploring their long-term clinical benefits in diverse patient populations.展开更多
With the rapid development of science and technology,the application of artificial intelligence(AI)technology in medical education has become increasingly widespread in the digital age,bringing new opportunities and c...With the rapid development of science and technology,the application of artificial intelligence(AI)technology in medical education has become increasingly widespread in the digital age,bringing new opportunities and challenges to China’s higher education of traditional Chinese medicine(TCM).In the context of digital education,it is of great significance to construct a teaching model that integrates AI technology with the characteristics of the diagnostics of traditional Chinese medicine,in order to improve the quality of curriculum teaching in the future.This article aims to introduce how to organically integrate AI technology with diagnostics of traditional Chinese medicine teaching based on the characteristics of the discipline,to achieve teaching mode reform,therefore to improve the teaching quality of traditional Chinese medicine education,and cultivate high-quality TCM talents that meet the needs of the new era.展开更多
A duty in development of an on-line fault detection algorithm is to make it associate with estimation of engine s health degradation. For this purpose,an on-line diagnostic algorithm is put forward. Using a tracking f...A duty in development of an on-line fault detection algorithm is to make it associate with estimation of engine s health degradation. For this purpose,an on-line diagnostic algorithm is put forward. Using a tracking filter to estimate the engine s health condition over its lifetime,can be reconstructed an onboard model,which is then made to match a real aircraft gas turbine engine. Finally,a bank of Kalman filters is applied in fault detection and isola-tion (FDI) of sensors for the engine. Through the bank...展开更多
Radiation doses to patients in diagnostics and interventional radiology need to be optimized to comply with the principles of radiation protection in medical practice. This involves using specific detectors with respe...Radiation doses to patients in diagnostics and interventional radiology need to be optimized to comply with the principles of radiation protection in medical practice. This involves using specific detectors with respective diagnostic beams to carry out quality control/quality assurance tests needed to optimize patient doses in the hospital. Semiconductor detectors are used in dosimetry to verify the equipment performance and dose to patients. This work aims to assess the performance, energy dependence, and response of five commercially available semiconductor detectors in RQR, RQR-M, RQA, and RQT at Secondary Standard Dosimetry for clinical applications. The diagnostic beams were generated using Exradin A4 reference ion chamber and PTW electrometer. The ambient temperature and pressure were noted for KTP correction. The detectors designed for RQR showed good performance in RQT beams and vice versa. The detectors designed for RQR-M displayed high energy dependency in other diagnostic beams. The type of diagnostic beam quality determines the response of semiconductor detectors. Therefore, a detector should be calibrated according to the beam qualities to be measured.展开更多
Interpreting experimental diagnostics data in tokamaks,while considering non-ideal effects,is challenging due to the complexity of plasmas.To address this challenge,a general synthetic diagnostics(GSD)platform has bee...Interpreting experimental diagnostics data in tokamaks,while considering non-ideal effects,is challenging due to the complexity of plasmas.To address this challenge,a general synthetic diagnostics(GSD)platform has been established that facilitates microwave imaging reflectometry and electron cyclotron emission imaging.This platform utilizes plasma profiles as input and incorporates the finite-difference time domain,ray tracing and the radiative transfer equation to calculate the propagation of plasma spontaneous radiation and the external electromagnetic field in plasmas.Benchmark tests for classical cases have been conducted to verify the accuracy of every core module in the GSD platform.Finally,2D imaging of a typical electron temperature distribution is reproduced by this platform and the results are consistent with the given real experimental data.This platform also has the potential to be extended to 3D electromagnetic field simulations and other microwave diagnostics such as cross-polarization scattering.展开更多
Helicobacter pylori(H.pylori)infection is highly prevalent in the human population and may lead to severe gastrointestinal pathology including gastric and duodenal ulcers,mucosa associated tissue lymphoma and gastric ...Helicobacter pylori(H.pylori)infection is highly prevalent in the human population and may lead to severe gastrointestinal pathology including gastric and duodenal ulcers,mucosa associated tissue lymphoma and gastric adenocarcinoma.In recent years,an alarming increase in antimicrobial resistance and subsequently failing empiric H.pylori eradication therapies have been noted worldwide,also in many European countries.Therefore,rapid and accurate determination of H.pylori’s antibiotic susceptibility prior to the administration of eradication regimens becomes ever more important.Traditionally,detection of H.pylori and its antimicrobial resistance is done by culture and phenotypic drug susceptibility testing that are cumbersome with a long turn-around-time.Recent advances in diagnostics provide new tools,like real-time polymerase chain reaction(PCR)and line probe assays,to diagnose H.pylori infection and antimicrobial resistance to certain antibiotics,directly from clinical specimens.Moreover,high-throughput whole genome sequencing technologies allow the rapid analysis of the pathogen’s genome,thereby allowing identification of resistance mutations and associated antibiotic resistance.In the first part of this review,we will give an overview on currently available diagnostic methods for detection of H.pylori and its drug resistance and their implementation in H.pylori management.The second part of the review focusses on the use of next generation sequencing technology in H.pylori research.To this end,we conducted a literature search for original research articles in English using the terms“Helicobacter”,“transcriptomic”,“transcriptome”,“next generation sequencing”and“whole genome sequencing”.This review is aimed to bridge the gap between current diagnostic practice(histology,rapid urease test,H.pylori culture,PCR and line probe assays)and new sequencing technologies and their potential implementation in diagnostic laboratory settings in order to complement the currently recommended H.pylori management guidelines and subsequently improve public health.展开更多
The pathogenesis of inflammatory bowel disease (IBD) is complex and largely unknown. Until recently, research has focused on the study of protein regulators in inflammation to reveal the cellular and molecular network...The pathogenesis of inflammatory bowel disease (IBD) is complex and largely unknown. Until recently, research has focused on the study of protein regulators in inflammation to reveal the cellular and molecular networks in the pathogenesis of IBD. However, in the last few years, new and promising insights have been generated from studies describing an association between an altered expression of a specific class of non-coding RNAs, called microRNAs (miRs or miRNAs) and IBD. The short (approximately 22 nucleotides), endogenous, single-stranded RNAs are evolutionary conserved inanimals and plants, and regulate specific target mRNAs at the post-transcriptional level. MiRNAs are involved in several biological processes, including development, cell differentiation, proliferation and apoptosis. Furthermore, it is estimated that miRNAs may be responsible for regulating the expression of nearly one-third of the genes in the human genome. Thus, miRNA deregulation often results in an impaired cellular function, and a disturbance of downstream gene regulation and signaling cascades, suggesting their implication in disease etiology. Despite the identification of more than 1900 mature human miRNAs, very little is known about their biological functions and functional targets. Recent studies have identified dysregulated miRNAs in tissue samples of IBD patients and have demonstrated similar differences in circulating miRNAs in the serum of IBD patients. Thus, there is great promise that miRNAs will aid in the early diagnosis of IBD, and in the development of personalized therapies. Here, we provide a short review of the current state-of-the-art of miRNAs in IBD pathogenesis, diagnostics and therapeutics.展开更多
In this paper,we propose an approach for diagnostics and prognostics of damaged aircraft structures,by combing high-performance fatigue mechanics with filtering theories.Fast&accurate deterministic analyses of fat...In this paper,we propose an approach for diagnostics and prognostics of damaged aircraft structures,by combing high-performance fatigue mechanics with filtering theories.Fast&accurate deterministic analyses of fatigue crack propagations are carried out,by using the Finite Element Alternating Method(FEAM)for computing SIFs,and by using the newly developed Moving Least Squares(MLS)law for computing fatigue crack growth rates.Such algorithms for simulating fatigue crack propagations are embedded in the computer program Safe-Flaw,which is called upon as a subroutine within the probabilistic framework of filter theories.Both the extended Kalman as well as particle filters are applied in this study,to obtain the statistically optimal and semi-optimal estimates of crack lengths,from a series of noisy measurements of crack-lengths over time.For the specific problem,a simple modification to the particle filter,which can drastically reduce the computational burden,is also proposed.Based on the results of such diagnostic analyses,the prognostics of aerospace structures are thereafter achieved,to estimate the probabilistic distribution of the remaining useful life.By using a simple example of a single-crack near a fastener hole,we demonstrate the concept and effectiveness of the proposed framework.This paper thus forms the scientific foundation for the recently proposed concepts of VRAMS(Virtual Risk-Informed Agile Maneuver Sustainment)and Digital Twins of aerospace vehicles.展开更多
Colorectal cancer(CRC)is one of the most prevalent cancers in developed countries.On the other hand,CRC is also one of the most curable cancers if it is detected in early stages through regular colonoscopy or sigmoido...Colorectal cancer(CRC)is one of the most prevalent cancers in developed countries.On the other hand,CRC is also one of the most curable cancers if it is detected in early stages through regular colonoscopy or sigmoidoscopy.Since CRC develops slowly from precancerous lesions,early detection can reduce both the incidence and mortality of the disease.Fecal occult blood test is a widely used non-invasive screening tool for CRC.Although fecal occult blood test is simple and cost-effective in screening CRC,there is room for improvement in terms of the accuracy of the test.Genetic dysregulations have been found to play an important role in CRC development.With better understanding of the molecular basis of CRC,there is a growing expectation on the development of diagnostic tests based on more sensitive and specific molecular markers and those tests may provide a breakthrough to the limitations of current screening tests for CRC.In this review,the molecular basis of CRC development,the characteristics and applications of different non-invasive molecular biomarkers,as well as the technologies available for the detection were discussed.This review intended to provide a summary on the current and future molecular diagnostics in CRC and its pre-malignant state,colorectal adenoma.展开更多
The increasing morbidity of internal diseases poses serious threats to human health and quality of life.Exhaled breath analysis is a noninvasive and convenient diagnostic method to improve the cure rate of patients. I...The increasing morbidity of internal diseases poses serious threats to human health and quality of life.Exhaled breath analysis is a noninvasive and convenient diagnostic method to improve the cure rate of patients. In this study, a self-powered breath analyzer based on polyaniline/polyvinylidene fluoride(PANI/PVDF) piezogas-sensing arrays has been developed for potential detection of several internal diseases. The device works by converting exhaled breath energy into piezoelectric gassensing signals without any external power sources. The five sensing units in the device have different sensitivities to various gas markers with concentrations ranging from 0 to 600 ppm. The working principle can be attributed to the coupling of the in-pipe gas-flow-induced piezoelectric effect of PVDF and gas-sensing properties of PANI electrodes. In addition, the device demonstrates its use as an ethanol analyzer to roughly mimic fatty liver diagnosis.This new approach can be applied to fabricating new exhaled breath analyzers and promoting the development of self-powered systems.展开更多
Delayed diagnosis of cancer using conventional diagnostic modalities needs to be addressed to reduce the mortality rate of cancer.Recently,2 D nanomaterial-enabled advanced biosensors have shown potential towards the ...Delayed diagnosis of cancer using conventional diagnostic modalities needs to be addressed to reduce the mortality rate of cancer.Recently,2 D nanomaterial-enabled advanced biosensors have shown potential towards the early diagnosis of cancer.The high surface area,surface functional groups availability,and excellent electrical conductivity of MXene make it the 2 D material of choice for the fabrication of advanced electrochemical biosensors for disease diagnostics.MXene-enabled electrochemical aptasensors have shown great promise for the detection of cancer biomarkers with a femtomolar limit of detection.Additionally,the stability,ease of synthesis,good reproducibility,and high specificity offered by MXene-enabled aptasensors hold promise to be the mainstream diagnostic approach.In this review,the design and fabrication of MXene-based electrochemical aptasensors for the detection of cancer biomarkers have been discussed.Besides,various synthetic processes and useful properties of MXenes which can be tuned and optimized easily and efficiently to fabricate sensitive biosensors have been elucidated.Further,futuristic sensing applications along with challenges will be deliberated herein.展开更多
Identifying biomarkers that can be used as diagnostics or predictors of treatment response(theranostics) in people with schizophrenia(Sz) will be an important step towards being able to provide personalized treatment....Identifying biomarkers that can be used as diagnostics or predictors of treatment response(theranostics) in people with schizophrenia(Sz) will be an important step towards being able to provide personalized treatment. Findings from the studies in brain tissue have not yet been translated into biomarkers that are practical in clinical use because brain biopsies are not acceptable and neuroimaging techniques are expensive and the results are inconclusive. Thus, in recent years, there has been search for blood-based biomarkers for Sz as a valid alternative. Although there are some encouraging preliminary data to support the notion of peripheral biomarkers for Sz, it must be acknowledged that Sz is a complex and heterogeneous disorder which needs to be further dissected into subtype using biological based and clinical markers. The scope of this review is to critically examine published blood-based biomarker of Sz, focusing on possible uses for diagnosis, treatment response, or their relationship with schizophreniaassociated phenotype. We sorted the studies into six categories which include:(1) brain-derived neurotrophic factor;(2) inflammation and immune function;(3) neurochemistry;(4) oxidative stress response and metabolism;(5) epigenetics and micro RNA; and(6) transcriptome and proteome studies. This review also summarized the molecules which have been conclusively reported as potential blood-based biomarkers for Sz in different blood cell types. Finally, we further discusses the pitfall of current blood-based studies and suggest that a prediction model-based, Sz specific, bloodoriented study design as well as standardize blood collection conditions would be useful for Sz biomarker development.展开更多
Gastric cancer is the fourth most common type ofcancer and represents a major cause of cancer-related deaths worldwide. With recent biomedical advances in our understanding of the molecular characteristics of gastric ...Gastric cancer is the fourth most common type ofcancer and represents a major cause of cancer-related deaths worldwide. With recent biomedical advances in our understanding of the molecular characteristics of gastric cancer, many genetic alterations have been identified as potential targets for its treatment. Multiple novel agents are currently under development as the demand for active agents that improve the survival of gastric cancer patients constantly increases. Based on lessons from previous trials of targeted agents, it is now widely accepted that the establishment of an optimal diagnostic test to select molecularly defined patients is of equal importance to the development of active agents against targetable genetic alterations. Herein, we highlight the current status and future perspectives of companion diagnostics in the treatment of gastric cancer.展开更多
文摘Multimodal deep learning has emerged as a key paradigm in contemporary medical diagnostics,advancing precision medicine by enabling integration and learning from diverse data sources.The exponential growth of high-dimensional healthcare data,encompassing genomic,transcriptomic,and other omics profiles,as well as radiological imaging and histopathological slides,makes this approach increasingly important because,when examined separately,these data sources only offer a fragmented picture of intricate disease processes.Multimodal deep learning leverages the complementary properties of multiple data modalities to enable more accurate prognostic modeling,more robust disease characterization,and improved treatment decision-making.This review provides a comprehensive overview of the current state of multimodal deep learning approaches in medical diagnosis.We classify and examine important application domains,such as(1)radiology,where automated report generation and lesion detection are facilitated by image-text integration;(2)histopathology,where fusion models improve tumor classification and grading;and(3)multi-omics,where molecular subtypes and latent biomarkers are revealed through cross-modal learning.We provide an overview of representative research,methodological advancements,and clinical consequences for each domain.Additionally,we critically analyzed the fundamental issues preventing wider adoption,including computational complexity(particularly in training scalable,multi-branch networks),data heterogeneity(resulting from modality-specific noise,resolution variations,and inconsistent annotations),and the challenge of maintaining significant cross-modal correlations during fusion.These problems impede interpretability,which is crucial for clinical trust and use,in addition to performance and generalizability.Lastly,we outline important areas for future research,including the development of standardized protocols for harmonizing data,the creation of lightweight and interpretable fusion architectures,the integration of real-time clinical decision support systems,and the promotion of cooperation for federated multimodal learning.Our goal is to provide researchers and clinicians with a concise overview of the field’s present state,enduring constraints,and exciting directions for further research through this review.
文摘In a recent case report in the World Journal of Clinical Cases,emphasized the crucial role of rapidly and accurately identifying pathogens to optimize patient treatment outcomes.Laboratory-on-a-chip(LOC)technology has emerged as a transformative tool in health care,offering rapid,sensitive,and specific identification of microorganisms.This editorial provides a comprehensive overview of LOC technology,highlighting its principles,advantages,applications,challenges,and future directions.Success studies from the field have demonstrated the practical benefits of LOC devices in clinical diagnostics,epidemiology,and food safety.Comparative studies have underscored the superiority of LOC technology over traditional methods,showcasing improvements in speed,accuracy,and portability.The future integration of LOC with biosensors,artificial intelligence,and data analytics promises further innovation and expansion.This call to action emphasizes the importance of continued research,investment,and adoption to realize the full potential of LOC technology in improving healthcare outcomes worldwide.
文摘This article provides a short review on the importance of the detailed analysis of a Langmuir probe I-V trace in a multi-Maxwellian plasma,and discuss proper procedures analyzing Langmuir probe I-V traces in bi-Maxwellian and triple-Maxwellian Electron Energy Distribution Function(EEDF)plasmas.Discus⁃sion and demonstration of procedures include the treatment of the ion saturation current,electron saturation cur⁃rent,space-charge effects on the I-V trace,and most importantly how to properly isolate and fit for each electron group present in an I-V trace reflecting a mult-Maxwellian EEDF,as well as how having a multi-Maxwellian EEDF affects the procedures of treating the ion and electron saturation currents.Shortcomings of common improp⁃er procedures are discussed and demonstrated with simulated I-V traces to show how these procedures gives false measurements.
文摘Artificial Intelligence(AI)is fundamentally transforming medical diagnostics,driving advancements that enhance accuracy,efficiency,and personalized patient care.This narrative review explores AI integration across various diagnostic domains,emphasizing its role in improving clinical decision-making.The evolution of medical diagnostics from traditional observational methods to sophisticated imaging,laboratory tests,and molecular diagnostics lays the foundation for understanding AI’s impact.Modern diagnostics are inherently complex,influenced by multifactorial disease presentations,patient variability,cognitive biases,and systemic factors like data overload and interdisciplinary collaboration.AI-enhanced clinical decision support systems utilize both knowledge-based and non-knowledge-based approaches,employing machine learning and deep learning algorithms to analyze vast datasets,identify patterns,and generate accurate differential diagnoses.AI’s potential in diagnostics is demonstrated through applications in genomics,predictive analytics,and early disease detection,with successful case studies in oncology,radiology,pathology,ophthalmology,dermatology,gastroenterology,and psychiatry.These applications demonstrate AI’s ability to process complex medical data,facilitate early intervention,and extend specialized care to underserved populations.However,integrating AI into diagnostics faces significant limitations,including technical challenges related to data quality and system integration,regulatory hurdles,ethical concerns about transparency and bias,and risks of misinformation and overreliance.Addressing these challenges requires robust regulatory frameworks,ethical guidelines,and continuous advancements in AI technology.The future of AI in diagnostics promises further innovations in multimodal AI,genomic data integration,and expanding access to high-quality diagnostic services globally.Responsible and ethical implementation of AI will be crucial to fully realize its potential,ensuring AI serves as a powerful ally in achieving diagnostic excellence and improving global health care outcomes.This narrative review emphasizes AI’s pivotal role in shaping the future of medical diagnostics,advocating for sustained investment and collaborative efforts to harness its benefits effectively.
基金supported by the National Key R&D Program of China(2024YFB2505003).
文摘Electrochemical impedance spectroscopy(EIS)offers valuable insights into the dynamic behaviors of lithium-ion batteries,making it a powerful and non-invasive tool for evaluating battery health.However,EIS falls short in quantitatively determining the degree of specific degradation modes,which are essential for improving battery lifespan.This study introduces a novel approach employing deep neural networks enhanced by an attention mechanism to identify the degree of degradation modes.The proposed method can automatically determine the most relevant frequency ranges for each degradation mode,which can link impedance characteristics to battery degradation.To overcome the limitation of scarce labeled experimental data,simulation results derived from mechanistic models are incorporated into the model.Validation results demonstrate that the proposed method could achieve root mean square errors below 3%for estimating loss of lithium inventory and loss of active material of the positive electrode,and below 4%for estimating loss of active material of the negative electrode while requiring only 25%of early-stage experimental degradation data.By integrating simulation results,the proposed method achieves a reduction in maximum estimation error ranging from 42.92%to 66.30%across different temperatures and operating conditions compared to the baseline model trained solely on experimental data.
文摘In Unani medicine,Bawl(urine)is recognized as a key diagnostic tool,with humoural imbalances assessed via parameters like color,consistency,sediment,clarity,froth,odor,and volume.This conceptual review explores how these classical diagnostic indicators may be contextualized alongside modern urinalysis markers(e.g.,bilirubin,protein,ketones,and sedimentation)and examined through emerging artificial intelligence(AI)frameworks.Potential applications include ResNet-18 for color classification,You Only Look Once version 8(YOLOv8)for sediment detection,long short-term memory(LSTM)for viscosity estimation,and EfficientDet for froth analysis,with standardized urine images/videos forming the basis of future datasets.Additionally,a comparative ontology is proposed to align Unani perspectives with diagnostic approaches in traditional Chinese medicine,encouraging cross-system integration.By synthesizing classical epistemology with computational intelligence,this review highlights pathways for developing AI-based decision support systems to promote personalized,accessible,and telemedicine-enabled healthcare.
文摘As a critical technology for industrial system reliability and safety,machine monitoring and fault diagnostics have advanced transformatively with large language models(LLMs).This paper reviews LLM-based monitoring and diagnostics methodologies,categorizing them into in-context learning,fine-tuning,retrievalaugmented generation,multimodal learning,and time series approaches,analyzing advances in diagnostics and decision support.It identifies bottlenecks like limited industrial data and edge deployment issues,proposing a three-stage roadmap to highlight LLMs’potential in shaping adaptive,interpretable PHM frameworks.
文摘Background:Head and neck cancers(HNC)account for a significant global health burden,with increasing incidence rates and complex treatment requirements.Traditional diagnostic and therapeutic approaches,while effective,often result in substantial morbidity and limitations in personalized care.This review provides a comprehensive overview of the latest innovations in diagnostics and therapeutic strategies for HNC from 2015 to 2024.Methods:A review of literature focused on pe-reviewed journals,clinical trial databases,and oncology conference proceedings.Key areas include molecular diagnostics,imaging technologies,minimally invasive surgeries,and innovative therapeutic strategies.Results:Technologies like liquid biopsy next-generation sequencing(NGS)have greatly improved diagnostic accuracy and personalization in HNC care.These advancements have improved survival rates and enhanced patients’quality of life.Personalized therapeutic approaches,including immune checkpoint inhibitors,precision radiation therapy,and surgery,have led to enhanced treatment efficacy while reducing side effects.The integration of AI and machine learning into diagnostics and treatment planning shows promise in optimizing clinical decision-making and predicting treatment outcomes.Conclusion:The current innovations in diagnostics and therapeutics are reshaping the management of head and neck cancer,offering more tailored and effective approaches to care.Overall,the continuous integration of these innovations in clinical practice is reshaping HNC treatment and improving patient outcomes and survival rates.Future research should focus on further refining these technologies,addressing challenges related to accessibility,and exploring their long-term clinical benefits in diverse patient populations.
文摘With the rapid development of science and technology,the application of artificial intelligence(AI)technology in medical education has become increasingly widespread in the digital age,bringing new opportunities and challenges to China’s higher education of traditional Chinese medicine(TCM).In the context of digital education,it is of great significance to construct a teaching model that integrates AI technology with the characteristics of the diagnostics of traditional Chinese medicine,in order to improve the quality of curriculum teaching in the future.This article aims to introduce how to organically integrate AI technology with diagnostics of traditional Chinese medicine teaching based on the characteristics of the discipline,to achieve teaching mode reform,therefore to improve the teaching quality of traditional Chinese medicine education,and cultivate high-quality TCM talents that meet the needs of the new era.
文摘A duty in development of an on-line fault detection algorithm is to make it associate with estimation of engine s health degradation. For this purpose,an on-line diagnostic algorithm is put forward. Using a tracking filter to estimate the engine s health condition over its lifetime,can be reconstructed an onboard model,which is then made to match a real aircraft gas turbine engine. Finally,a bank of Kalman filters is applied in fault detection and isola-tion (FDI) of sensors for the engine. Through the bank...
文摘Radiation doses to patients in diagnostics and interventional radiology need to be optimized to comply with the principles of radiation protection in medical practice. This involves using specific detectors with respective diagnostic beams to carry out quality control/quality assurance tests needed to optimize patient doses in the hospital. Semiconductor detectors are used in dosimetry to verify the equipment performance and dose to patients. This work aims to assess the performance, energy dependence, and response of five commercially available semiconductor detectors in RQR, RQR-M, RQA, and RQT at Secondary Standard Dosimetry for clinical applications. The diagnostic beams were generated using Exradin A4 reference ion chamber and PTW electrometer. The ambient temperature and pressure were noted for KTP correction. The detectors designed for RQR showed good performance in RQT beams and vice versa. The detectors designed for RQR-M displayed high energy dependency in other diagnostic beams. The type of diagnostic beam quality determines the response of semiconductor detectors. Therefore, a detector should be calibrated according to the beam qualities to be measured.
基金supported by the National Magnetic Confinement Fusion Energy Program of China(No.2019YFE03020001)the Collaborative Innovation Program of Hefei Science Center,CAS(No.2021HSC-CIP010)the Fundamental Research Funds for the Central Universities。
文摘Interpreting experimental diagnostics data in tokamaks,while considering non-ideal effects,is challenging due to the complexity of plasmas.To address this challenge,a general synthetic diagnostics(GSD)platform has been established that facilitates microwave imaging reflectometry and electron cyclotron emission imaging.This platform utilizes plasma profiles as input and incorporates the finite-difference time domain,ray tracing and the radiative transfer equation to calculate the propagation of plasma spontaneous radiation and the external electromagnetic field in plasmas.Benchmark tests for classical cases have been conducted to verify the accuracy of every core module in the GSD platform.Finally,2D imaging of a typical electron temperature distribution is reproduced by this platform and the results are consistent with the given real experimental data.This platform also has the potential to be extended to 3D electromagnetic field simulations and other microwave diagnostics such as cross-polarization scattering.
文摘Helicobacter pylori(H.pylori)infection is highly prevalent in the human population and may lead to severe gastrointestinal pathology including gastric and duodenal ulcers,mucosa associated tissue lymphoma and gastric adenocarcinoma.In recent years,an alarming increase in antimicrobial resistance and subsequently failing empiric H.pylori eradication therapies have been noted worldwide,also in many European countries.Therefore,rapid and accurate determination of H.pylori’s antibiotic susceptibility prior to the administration of eradication regimens becomes ever more important.Traditionally,detection of H.pylori and its antimicrobial resistance is done by culture and phenotypic drug susceptibility testing that are cumbersome with a long turn-around-time.Recent advances in diagnostics provide new tools,like real-time polymerase chain reaction(PCR)and line probe assays,to diagnose H.pylori infection and antimicrobial resistance to certain antibiotics,directly from clinical specimens.Moreover,high-throughput whole genome sequencing technologies allow the rapid analysis of the pathogen’s genome,thereby allowing identification of resistance mutations and associated antibiotic resistance.In the first part of this review,we will give an overview on currently available diagnostic methods for detection of H.pylori and its drug resistance and their implementation in H.pylori management.The second part of the review focusses on the use of next generation sequencing technology in H.pylori research.To this end,we conducted a literature search for original research articles in English using the terms“Helicobacter”,“transcriptomic”,“transcriptome”,“next generation sequencing”and“whole genome sequencing”.This review is aimed to bridge the gap between current diagnostic practice(histology,rapid urease test,H.pylori culture,PCR and line probe assays)and new sequencing technologies and their potential implementation in diagnostic laboratory settings in order to complement the currently recommended H.pylori management guidelines and subsequently improve public health.
基金Supported by Grants from Fonden til Lgevidenskabens Fremme(the AP Mller Foundation)the Family Erichsen Memorial Foundation+2 种基金the Lundbeck Foundationthe Axel Muusfeldts Foundationthe Foundation of Aase and Ejnar Danielsen
文摘The pathogenesis of inflammatory bowel disease (IBD) is complex and largely unknown. Until recently, research has focused on the study of protein regulators in inflammation to reveal the cellular and molecular networks in the pathogenesis of IBD. However, in the last few years, new and promising insights have been generated from studies describing an association between an altered expression of a specific class of non-coding RNAs, called microRNAs (miRs or miRNAs) and IBD. The short (approximately 22 nucleotides), endogenous, single-stranded RNAs are evolutionary conserved inanimals and plants, and regulate specific target mRNAs at the post-transcriptional level. MiRNAs are involved in several biological processes, including development, cell differentiation, proliferation and apoptosis. Furthermore, it is estimated that miRNAs may be responsible for regulating the expression of nearly one-third of the genes in the human genome. Thus, miRNA deregulation often results in an impaired cellular function, and a disturbance of downstream gene regulation and signaling cascades, suggesting their implication in disease etiology. Despite the identification of more than 1900 mature human miRNAs, very little is known about their biological functions and functional targets. Recent studies have identified dysregulated miRNAs in tissue samples of IBD patients and have demonstrated similar differences in circulating miRNAs in the serum of IBD patients. Thus, there is great promise that miRNAs will aid in the early diagnosis of IBD, and in the development of personalized therapies. Here, we provide a short review of the current state-of-the-art of miRNAs in IBD pathogenesis, diagnostics and therapeutics.
文摘In this paper,we propose an approach for diagnostics and prognostics of damaged aircraft structures,by combing high-performance fatigue mechanics with filtering theories.Fast&accurate deterministic analyses of fatigue crack propagations are carried out,by using the Finite Element Alternating Method(FEAM)for computing SIFs,and by using the newly developed Moving Least Squares(MLS)law for computing fatigue crack growth rates.Such algorithms for simulating fatigue crack propagations are embedded in the computer program Safe-Flaw,which is called upon as a subroutine within the probabilistic framework of filter theories.Both the extended Kalman as well as particle filters are applied in this study,to obtain the statistically optimal and semi-optimal estimates of crack lengths,from a series of noisy measurements of crack-lengths over time.For the specific problem,a simple modification to the particle filter,which can drastically reduce the computational burden,is also proposed.Based on the results of such diagnostic analyses,the prognostics of aerospace structures are thereafter achieved,to estimate the probabilistic distribution of the remaining useful life.By using a simple example of a single-crack near a fastener hole,we demonstrate the concept and effectiveness of the proposed framework.This paper thus forms the scientific foundation for the recently proposed concepts of VRAMS(Virtual Risk-Informed Agile Maneuver Sustainment)and Digital Twins of aerospace vehicles.
文摘Colorectal cancer(CRC)is one of the most prevalent cancers in developed countries.On the other hand,CRC is also one of the most curable cancers if it is detected in early stages through regular colonoscopy or sigmoidoscopy.Since CRC develops slowly from precancerous lesions,early detection can reduce both the incidence and mortality of the disease.Fecal occult blood test is a widely used non-invasive screening tool for CRC.Although fecal occult blood test is simple and cost-effective in screening CRC,there is room for improvement in terms of the accuracy of the test.Genetic dysregulations have been found to play an important role in CRC development.With better understanding of the molecular basis of CRC,there is a growing expectation on the development of diagnostic tests based on more sensitive and specific molecular markers and those tests may provide a breakthrough to the limitations of current screening tests for CRC.In this review,the molecular basis of CRC development,the characteristics and applications of different non-invasive molecular biomarkers,as well as the technologies available for the detection were discussed.This review intended to provide a summary on the current and future molecular diagnostics in CRC and its pre-malignant state,colorectal adenoma.
基金supported by the National Natural Science Foundation of China (11674048)the Fundamental Research Funds for the Central Universities (N170505001 and N160502002)Program for Shenyang Youth Science and Technology Innovation Talents (RC170269)
文摘The increasing morbidity of internal diseases poses serious threats to human health and quality of life.Exhaled breath analysis is a noninvasive and convenient diagnostic method to improve the cure rate of patients. In this study, a self-powered breath analyzer based on polyaniline/polyvinylidene fluoride(PANI/PVDF) piezogas-sensing arrays has been developed for potential detection of several internal diseases. The device works by converting exhaled breath energy into piezoelectric gassensing signals without any external power sources. The five sensing units in the device have different sensitivities to various gas markers with concentrations ranging from 0 to 600 ppm. The working principle can be attributed to the coupling of the in-pipe gas-flow-induced piezoelectric effect of PVDF and gas-sensing properties of PANI electrodes. In addition, the device demonstrates its use as an ethanol analyzer to roughly mimic fatty liver diagnosis.This new approach can be applied to fabricating new exhaled breath analyzers and promoting the development of self-powered systems.
基金Open access funding provided by Shanghai Jiao Tong University.
文摘Delayed diagnosis of cancer using conventional diagnostic modalities needs to be addressed to reduce the mortality rate of cancer.Recently,2 D nanomaterial-enabled advanced biosensors have shown potential towards the early diagnosis of cancer.The high surface area,surface functional groups availability,and excellent electrical conductivity of MXene make it the 2 D material of choice for the fabrication of advanced electrochemical biosensors for disease diagnostics.MXene-enabled electrochemical aptasensors have shown great promise for the detection of cancer biomarkers with a femtomolar limit of detection.Additionally,the stability,ease of synthesis,good reproducibility,and high specificity offered by MXene-enabled aptasensors hold promise to be the mainstream diagnostic approach.In this review,the design and fabrication of MXene-based electrochemical aptasensors for the detection of cancer biomarkers have been discussed.Besides,various synthetic processes and useful properties of MXenes which can be tuned and optimized easily and efficiently to fabricate sensitive biosensors have been elucidated.Further,futuristic sensing applications along with challenges will be deliberated herein.
基金Supported by The National Science Council of Taiwan,Nos.102-2917-I-002-002 and 103-2811-B-002-107the Australian Research Council,No.FT100100689the National Health and Medical Research Council,No.APP1002240
文摘Identifying biomarkers that can be used as diagnostics or predictors of treatment response(theranostics) in people with schizophrenia(Sz) will be an important step towards being able to provide personalized treatment. Findings from the studies in brain tissue have not yet been translated into biomarkers that are practical in clinical use because brain biopsies are not acceptable and neuroimaging techniques are expensive and the results are inconclusive. Thus, in recent years, there has been search for blood-based biomarkers for Sz as a valid alternative. Although there are some encouraging preliminary data to support the notion of peripheral biomarkers for Sz, it must be acknowledged that Sz is a complex and heterogeneous disorder which needs to be further dissected into subtype using biological based and clinical markers. The scope of this review is to critically examine published blood-based biomarker of Sz, focusing on possible uses for diagnosis, treatment response, or their relationship with schizophreniaassociated phenotype. We sorted the studies into six categories which include:(1) brain-derived neurotrophic factor;(2) inflammation and immune function;(3) neurochemistry;(4) oxidative stress response and metabolism;(5) epigenetics and micro RNA; and(6) transcriptome and proteome studies. This review also summarized the molecules which have been conclusively reported as potential blood-based biomarkers for Sz in different blood cell types. Finally, we further discusses the pitfall of current blood-based studies and suggest that a prediction model-based, Sz specific, bloodoriented study design as well as standardize blood collection conditions would be useful for Sz biomarker development.
文摘Gastric cancer is the fourth most common type ofcancer and represents a major cause of cancer-related deaths worldwide. With recent biomedical advances in our understanding of the molecular characteristics of gastric cancer, many genetic alterations have been identified as potential targets for its treatment. Multiple novel agents are currently under development as the demand for active agents that improve the survival of gastric cancer patients constantly increases. Based on lessons from previous trials of targeted agents, it is now widely accepted that the establishment of an optimal diagnostic test to select molecularly defined patients is of equal importance to the development of active agents against targetable genetic alterations. Herein, we highlight the current status and future perspectives of companion diagnostics in the treatment of gastric cancer.
