This narrative review examines recent advances in salivary biomarkers for oral squamous cell carcinoma(OSCC),a major subtype of oral cancer with persistently low five-year survival rates due to delayed diagnosis.Saliv...This narrative review examines recent advances in salivary biomarkers for oral squamous cell carcinoma(OSCC),a major subtype of oral cancer with persistently low five-year survival rates due to delayed diagnosis.Saliva has emerged as a noninvasive diagnostic medium capable of reflecting both local tumor activity and systemic physiological changes.Various salivary biomarkers,including microRNAs,cytokines,proteins,metabolites,and exosomes,have been linked to oncogenic signaling pathways involved in tumor progression,immune modulation,and therapeutic resistance.Advances in quantitative polymerase chain reaction,mass spectrometry,and next-generation sequencing have enabled comprehensive biomarker profiling,while point-of-care detection systems and saliva-based omics platforms are accelerating clinical translation.Remaining challenges include variability in salivary composition,lack of standardized collection protocols,and insufficient validation across large patient cohorts.This review highlights the mechanistic relevance,diagnostic potential,and translational challenges of salivary biomarkers in OSCC.展开更多
This study investigates the variability in cancer diagnosis across different tissues and organs, with a focus on the role of diagnostic methods such as Hematoxylin and Eosin (H&E) staining and immunohistochemistry...This study investigates the variability in cancer diagnosis across different tissues and organs, with a focus on the role of diagnostic methods such as Hematoxylin and Eosin (H&E) staining and immunohistochemistry (IHC). The predominance of female breast cancer (30%) aligns with global trends, underscoring the need for robust diagnostic protocols, particularly in developing regions. Other prevalent cancers, including skin, stomach, and cervix uteri, reflect a mix of environmental, genetic, and infectious factors. The underrepresentation of gallbladder and thyroid cancers (<1%) suggests potential underdiagnosis or lower prevalence. Age distribution data indicate peak cancer incidence in individuals aged 31 - 45 years, with gender-specific cancers like breast and cervical cancer predominantly affecting females (63.4%). The analysis also highlights significant diagnostic gaps, as 61.2% of cases did not undergo IHC testing due to resource constraints, leading to potential biases in cancer prevalence and diagnostic accuracy. The study emphasizes the complementary role of IHC in confirming ambiguous H&E findings, with strong alignment observed when both methods were used. However, the absence of IHC in many cases limits the robustness of conclusions, suggesting the need for increased access to IHC testing. The findings advocate for integrating IHC into routine diagnostics, expanding diagnostic capabilities, and improving sample sizes to ensure more reliable and comprehensive cancer data.展开更多
OBJECTIVE:To reach consensus on the diagnostic criteria of syndrome of dampness obstruction in idiopathic membranous nephropathy(IMN)patients by literature research and expert investigation(interviews and a Delphi met...OBJECTIVE:To reach consensus on the diagnostic criteria of syndrome of dampness obstruction in idiopathic membranous nephropathy(IMN)patients by literature research and expert investigation(interviews and a Delphi method).METHODS:Our study was consistent with T/CACM 1336-2020.We searched the monographs and references published in the past 40 years(1983-2022),and established the diagnostic criteria pool of waterdampness syndrome and dampness-turbidity syndrome in Traditional Chinese Medicine(TCM)based on literature by using frequency statistics and correlation analysis.Expert investigation(interview method and two rounds of Delphi method)was used to form the diagnostic criteria of water-dampness syndrome and dampnessturbidity syndrome of idiopathic membranous nephropathy.Clinical diagnostic test research was carried out,and compared with“Diagnostic Criteria for dampness syndrome”(T/CACM 1454-2023)to evaluate the authenticity,reliability and clinical application value of the standard.RESULTS:A total of 122 relevant guides,standards,monographs and documents were included through searching books and Chinese databases.Four experts were interviewed and two rounds of delphi method(75 experts nationwide)were carried out.The experts'opinions are relatively concentrated and the differences are small.Based on the weight of each index,the diagnostic criteria indexes of water-dampness syndrome and dampness-turbidity syndrome were selected.After discussion by the core group members,the diagnostic model of"necessary symptoms and optional symptoms"was established,and the final diagnostic criteria of waterdampness syndrome and dampness-turbidity syndrome were established.One hundred and ninety-one inpatients and outpatients of Guangdong Provincial Hospital of Chinese Medicine from January 2021 to February 2023 were included in Diagnostic test study.There was no statistical difference in gender,age and course of disease(P>0.05).The sensitivity and specificity of the trial standard were 90.34%and 73.33%respectively,while the sensitivity and specificity of T/CACM 1454-2023 were 99.43%and 6.67%,respectively.CONCLUSIONS:The consensus-based diagnostic criteria for IMN can be widely incorporated in TCM.A further clinical study will be conducted to analyze the diagnosis value and cut-off score of our IMN criteria.展开更多
BACKGROUND Studies investigating diagnostic delays and their effects on patients with alcoholic cirrhosis.AIM To investigate the current status and associated factors influencing diagnostic delays in 401 patients with...BACKGROUND Studies investigating diagnostic delays and their effects on patients with alcoholic cirrhosis.AIM To investigate the current status and associated factors influencing diagnostic delays in 401 patients with alcoholic cirrhosis.METHODS A cross-sectional analysis was conducted at a tertiary hospital in China from June 2020 to December 2023.Data were collected through telephone follow-ups and questionnaires.The Wilcoxon and Kruskal-Wallis H tests were used to compare diagnostic delays across various characteristics.Multivariate linear regression was employed to identify factors associated with diagnostic delays.RESULTS The median diagnostic delay was 5 months,with an interquartile range of 2-11 months.The proportions of patients with alcoholic cirrhosis who initially visited tertiary,secondary,and primary hospitals were 38.9%,37.91%,and 23.19%,respectively.Furthermore,the rates of patients undergoing liver computed tomography(CT)during their first visit at tertiary,secondary,and primary hospitals were 92.95%,13.82%,and 1.08%,respectively(P<0.001).Significant differences were observed in diagnostic delay-related characteristics,including residence,resident type,initial diagnosis,medical insurance,liver CT,and liver ultrasound during the first visit,age,years of education,family size,marital status,annual family income,years of drinking,daily alcohol consumption,and type of alcohol consumed(P<0.01).Furthermore,diagnostic delays were variably associated with daily alcohol consumption and other characteristics(i.e.residence,years of drinking,medical insurance,years of education,annual family income,liver CT and ultrasound during the first visit).Significant predictors of diagnostic delay identified on multivariate linear regression analysis included years of education,daily alcohol consumption,annual family income and blood ammonia levels(P<0.01).Patients with alcoholic cirrhosis experience varying degrees of diagnostic delays,necessitating interventions targeting potential contributing factors.CONCLUSION Our study indicates that patients with alcoholic cirrhosis may experience varying degrees of diagnostic delay.Interventions targeting potential factors contributing to diagnostic delay are necessary.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
Pancreatic cancer is recognized as one of the leading causes of cancer mortality,representing the second most common source of cancer-related deaths within the gastrointestinal domain.Surgical resection is currently t...Pancreatic cancer is recognized as one of the leading causes of cancer mortality,representing the second most common source of cancer-related deaths within the gastrointestinal domain.Surgical resection is currently the only definitive treatment;however,the subtle emergence of symptoms often leads to a diagnosis at an advanced stage,with merely 10%-15%of patients being eligible for surgical intervention.The primary obstacle to achieving a potential radical resection is the presence of distant metastatic disease or invasion of adjacent major vascular structures.This review aims to highlight the critical role of endoscopic ultrasound in the diagnosis and staging of pancreatic tumors.We systematically searched PubMed,MEDLINE and Web of Science by using‘pancreatic cancer’and‘endoscopic ultrasonography’as keywords.Relevant studies were reviewed and analyzed.Endoscopic ultrasonography(EUS)is efficient in the diagnosis and staging of pancreatic cancer,past studies reported the accuracy of EUS is 63%to 94%for T-staging and 44%to 82%for N-staging but there are still limitations that need to be comprehensively applied with other diagnostic methods to evaluation of distant metastasis for surgical resectability.Our review aims to reveal the value for the staging of pancreatic cancer.展开更多
Several promising plasma biomarker proteins,such as amyloid-β(Aβ),tau,neurofilament light chain,and glial fibrillary acidic protein,are widely used for the diagnosis of neurodegenerative diseases.However,little is k...Several promising plasma biomarker proteins,such as amyloid-β(Aβ),tau,neurofilament light chain,and glial fibrillary acidic protein,are widely used for the diagnosis of neurodegenerative diseases.However,little is known about the long-term stability of these biomarker proteins in plasma samples stored at-80°C.We aimed to explore how storage time would affect the diagnostic accuracy of these biomarkers using a large cohort.Plasma samples from 229 cognitively unimpaired individuals,encompassing healthy controls and those experiencing subjective cognitive decline,as well as 99 patients with cognitive impairment,comprising those with mild cognitive impairment and dementia,were acquired from the Sino Longitudinal Study on Cognitive Decline project.These samples were stored at-80°C for up to 6 years before being used in this study.Our results showed that plasma levels of Aβ42,Aβ40,neurofilament light chain,and glial fibrillary acidic protein were not significantly correlated with sample storage time.However,the level of total tau showed a negative correlation with sample storage time.Notably,in individuals without cognitive impairment,plasma levels of total protein and tau phosphorylated protein threonine 181(p-tau181)also showed a negative correlation with sample storage time.This was not observed in individuals with cognitive impairment.Consequently,we speculate that the diagnostic accuracy of plasma p-tau181 and the p-tau181 to total tau ratio may be influenced by sample storage time.Therefore,caution is advised when using these plasma biomarkers for the identification of neurodegenerative diseases,such as Alzheimer's disease.Furthermore,in cohort studies,it is important to consider the impact of storage time on the overall results.展开更多
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
Objective To qualitatively assess the diagnostic performance of dynamic contrast enhancement(DCE),diffusionweighted imaging(DWI),and T2-weighted imaging(T2WI),alone or in combination,in the evaluation of breast cancer...Objective To qualitatively assess the diagnostic performance of dynamic contrast enhancement(DCE),diffusionweighted imaging(DWI),and T2-weighted imaging(T2WI),alone or in combination,in the evaluation of breast cancer.Methods We retrospectively reviewed the records of 394 consecutive patients with pathologically confirmed breast lesions who had undergone 3-T magnetic resonance imaging(MRI).The morphological characteristics of breast lesions were evaluated using DCE,DWI,and T2WI based on BI-RADS lexicon descriptors by trained radiologists.Patients were categorized into mass and non-mass groups based on MRI characteristics of the lesions,and the differences between benign and malignant lesions in each group were compared.Clinical prediction models for breast cancer diagnosis were constructed using logistic regression analysis.Diagnostic efficacies were compared using the area under the receiver operating characteristic curve(AUC)and DeLong test.Results For mass-like lesions,all the morphological parameters significantly differentiated benign and malignant lesions on consensus DCE,DWI,and T2WI(P<0.05).The combined method(DCE+DWI+T2WI)had a higher AUC(0.865)than any of the individual modality(DCE:0.786;DWI:0.793;T2WI:0.809)(P<0.05).For non-mass-like lesions,DWI signal intensity was a significant predictor of malignancy(P=0.036),but the model using DWI alone had a low AUC(0.669).Conclusion Morphological assessment using the combination of DCE,DWI,and T2WI provides better diagnostic value in differentiating benign and malignant breast mass-like lesions than assessment with only one of the modalities.展开更多
Objective:In recent years,the incidence and detection rate of pancreatic cystic lesions(PCLs)have increased significantly.Endoscopic ultrasound(EUS)plays an indispensable role in the diagnosis and differential diagnos...Objective:In recent years,the incidence and detection rate of pancreatic cystic lesions(PCLs)have increased significantly.Endoscopic ultrasound(EUS)plays an indispensable role in the diagnosis and differential diagnosis of PCLs.However,evidence comparing the diagnostic performance of EUS-guided fine-needle aspiration(EUS-FNA)and fine-needle biopsy(FNB)remains limited.This study aims to compare the diagnostic yield,adequacy of tissue acquisition,and safety between EUS-FNA and EUS-FNB in evaluating PCLs to inform clinical practice.Methods:A retrospective review was conducted on patients with PCLs who underwent either EUS-FNA or EUS-FNB between January 2014 and August 2021.The diagnostic yield,tissue acquisition adequacy,and incidence of adverse events were compared between the 2 groups.Results:A total of 90 patients with PCLs were included(52 in the FNA group and 38 in the FNB group).The diagnostic yield was similar between the FNA and FNB groups(94.2%vs 94.7%,P>0.05).The adequacy of tissue acquisition was 71.2%in the FNA group and 81.6%in the FNB group(P>0.05).No statistically significant difference was observed in the incidence of adverse events between the 2 groups(P>0.05).Conclusion:Both EUS-FNA and EUS-FNB demonstrate equally high diagnostic yields and tissue adequacy in PCLs,with excellent safety profiles.Both methods are safe and effective diagnostic tools for evaluating PCLs.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
BACKGROUND Early detection of precancerous lesions is of vital importance for reducing the incidence and mortality of upper gastrointestinal(UGI)tract cancer.However,traditional endoscopy has certain limitations in de...BACKGROUND Early detection of precancerous lesions is of vital importance for reducing the incidence and mortality of upper gastrointestinal(UGI)tract cancer.However,traditional endoscopy has certain limitations in detecting precancerous lesions.In contrast,real-time computer-aided detection(CAD)systems enhanced by artificial intelligence(AI)systems,although they may increase unnecessary medical procedures,can provide immediate feedback during examination,thereby improving the accuracy of lesion detection.This article aims to conduct a meta-analysis of the diagnostic performance of CAD systems in identifying precancerous lesions of UGI tract cancer during esophagogastroduodenoscopy(EGD),evaluate their potential clinical application value,and determine the direction for further research.AIM To investigate the improvement of the efficiency of EGD examination by the realtime AI-enabled real-time CAD system(AI-CAD)system.METHODS PubMed,EMBASE,Web of Science and Cochrane Library databases were searched by two independent reviewers to retrieve literature with per-patient analysis with a deadline up until April 2025.A meta-analysis was performed with R Studio software(R4.5.0).A random-effects model was used and subgroup analysis was carried out to identify possible sources of heterogeneity.RESULTS The initial search identified 802 articles.According to the inclusion criteria,2113 patients from 10 studies were included in this meta-analysis.The pooled accuracy difference,logarithmic difference of diagnostic odds ratios,sensitivity,specificity and the area under the summary receiver operating characteristic curve(area under the curve)of both AI group and endoscopist group for detecting precancerous lesion were 0.16(95%CI:0.12-0.20),-0.19(95%CI:-0.75-0.37),0.89(95%CI:0.85-0.92,AI group),0.67(95%CI:0.63-0.71,endoscopist group),0.89(95%CI:0.84-0.93,AI group),0.77(95%CI:0.70-0.83,endoscopist group),0.928(95%CI:0.841-0.948,AI group),0.722(95%CI:0.677-0.821,endoscopist group),respectively.CONCLUSION The present studies further provide evidence that the AI-CAD is a reliable endoscopic diagnostic tool that can be used to assist endoscopists in detection of precancerous lesions in the UGI tract.It may be introduced on a large scale for clinical application to enhance the accuracy of detecting precancerous lesions in the UGI tract.展开更多
BACKGROUND Brush cytology is the most commonly used technique for tissue acquisition during endoscopic retrograde cholangiopancreatography for the evaluation of biliary strictures.Nonetheless,brush cytology is limited...BACKGROUND Brush cytology is the most commonly used technique for tissue acquisition during endoscopic retrograde cholangiopancreatography for the evaluation of biliary strictures.Nonetheless,brush cytology is limited by its low sensitivity due to insufficient cellular yield.AIM To evaluate the impact of the sheath-rinse technique on improving the cellularity yield.METHODS A total of 112 patients with suspected malignant biliary strictures were enrolled at two tertiary centers in South Korea.The sample cellularity and diagnostic accuracy of brush-wash and sheath-rinse specimens were compared.RESULTS A significantly increased number of total cell clusters per representative 20×field was recorded in the sheath-rinse compared with the brush-wash specimens(median:12 vs 3,P<0.001).This trend persisted when large(>50 cells)clusters(median:8 vs 3,P<0.001),medium(6-49 cells)(median:7 vs 3,P<0.001),and small(2-5 cells)clusters(median:9 vs 3,P<0.001)were evaluated.Diagnostic accuracy and sensitivity for differentiating malignancy were superior with sheath-rinsing than with the brush-wash method(72.3%vs 62.5%,P<0.001 and 69.9%vs 59.2%,P<0.001,respectively).CONCLUSION Incorporating sheath-rinse specimens significantly improved the yield and diagnostic accuracy of biliary brush cytology.Sheath-rinsing should be integrated into routine clinical practice to improve diagnostic performance for biliary strictures.展开更多
Background:Convolutional neural networks(CNN)have achieved remarkable success in medical image analysis.However,unlike some general-domain tasks where model accuracy is paramount,medical applications demand both accur...Background:Convolutional neural networks(CNN)have achieved remarkable success in medical image analysis.However,unlike some general-domain tasks where model accuracy is paramount,medical applications demand both accuracy and explainability due to the high stakes affecting patients'lives.Based on model explanations,clinicians can evaluate the diagnostic decisions suggested by CNN.Nevertheless,prior explainable artificial intelligence methods treat medical image tasks akin to general vision tasks,following end-to-end paradigms to generate explanations and frequently overlooking crucial clinical domain knowledge.Methods:We propose a plug-and-play module that explicitly integrates anatomic boundary information into the explanation process for CNN-based thoracopathy classifiers.To generate the anatomic boundary of the lung parenchyma,we utilize a lung segmentation model developed on external public datasets and deploy it on the unseen target dataset to constrain model ex-planations within the lung parenchyma for the clinical task of thoracopathy classification.Results:Assessed by the intersection over union and dice similarity coefficient between model-extracted explanations and expert-annotated lesion areas,our method consistently outperformed the baseline devoid of clinical domain knowledge in 71 out of 72 scenarios,encompassing 3 CNN architectures(VGG-11,ResNet-18,and AlexNet),2 classification settings(binary and multi-label),3 explanation methods(Saliency Map,Grad-CAM,and Integrated Gradients),and 4 co-occurred thoracic diseases(Atelectasis,Fracture,Mass,and Pneumothorax).Conclusions:We underscore the effectiveness of leveraging radiology knowledge in improving model explanations for CNN and envisage that it could inspire future efforts to integrate clinical domain knowledge into medical image analysis.展开更多
BACKGROUND Diagnostic laparoscopy is a minimally invasive surgical method to diagnose intra-abdominal diseases.AIM To evaluate patients with unexplained ascites who could not be definitively diagnosed via advanced rad...BACKGROUND Diagnostic laparoscopy is a minimally invasive surgical method to diagnose intra-abdominal diseases.AIM To evaluate patients with unexplained ascites who could not be definitively diagnosed via advanced radiological and endoscopic methods and serological,cytological,and microbiological examinations and,therefore,underwent diag-nostic laparoscopy.METHODS This retrospective analysis evaluated 82 patients who underwent diagnostic laparoscopy due to unexplained ascites.Patients’medical records were obtained from the hospital database.Their age,sex,complaints at admission,laboratory results,radiological imaging results,diagnostic laparoscopy reports,and pa-thology reports were analyzed.RESULTS The serum-ascites albumin gradient was<1.1 in 96.3%of the patients(n=79).Among patients,22(26.8%)had benign diagnoses and 60(73.2%)had malignant diagnoses.In addition,55(67.1%)were deceased,and the median follow-up time from diagnosis to death was four months.The overall follow-up time ranged from 1 to 142 months,with a median of 14 months.Patients’diagnoses were significantly associated with their survival(P<0.05,χ2 test).The mortality rate was 86.7%among patients with malignant diagnoses and 13.6%among patients with benign diagnoses.CONCLUSION Diagnostic laparoscopy is minimally invasive,has a low complication rate,and requires a short hospital stay.It can be safely performed to diagnose and treat ascites that remain unexplained after advanced radiological and endoscopic examinations.展开更多
Introduction: Crohn’s Disease (CD) is a chronic inflammatory disorder with a heterogeneous presentation. While diarrhea, abdominal pain, and weight loss are hallmarks, atypical manifestations can obscure the diagnosi...Introduction: Crohn’s Disease (CD) is a chronic inflammatory disorder with a heterogeneous presentation. While diarrhea, abdominal pain, and weight loss are hallmarks, atypical manifestations can obscure the diagnosis. This report highlights an unusual presentation of CD to emphasize the need for comprehensive diagnostic strategies. Case Report: A 25-year-old male presented with peripheral edema, anorexia, and abdominal distension but lacked classic gastrointestinal (GI) symptoms. Laboratory findings included microcytic anemia and hypoalbuminemia, while imaging revealed ascites and bowel wall thickening. Elevated fecal calprotectin and positive Anti-Saccharomyces cerevisiae antibodies (ASCA) supported the diagnosis. Endoscopy confirmed ileocolic Crohn’s Disease (L3 + L4). Infliximab therapy resulted in marked clinical improvement. Discussion: This case underscores the complexity of atypical CD presentations. Early use of serological markers, imaging, and endoscopy guided the diagnosis. Recognition of CD’s diverse manifestations is critical for timely intervention. Conclusion: Atypical CD presentations require heightened clinical suspicion and a multidisciplinary approach to reduce diagnostic delays and improve patient outcomes.展开更多
Artificial intelligence(AI)is rapidly transforming radiology and computed tomography(CT)imaging by enabling automated image analysis,improved diagnostic accuracy,and clinical decision-support.We performed a systematic...Artificial intelligence(AI)is rapidly transforming radiology and computed tomography(CT)imaging by enabling automated image analysis,improved diagnostic accuracy,and clinical decision-support.We performed a systematic review of peerreviewed studies published between January 1,2010 and March 31,2025 to quantify reported gains in diagnostic performance and workflow efficiency,to evaluate clinical decision-support benefits and risks,and to identify integration priorities.We searched PubMed,IEEE Xplore,Scopus,ScienceDirect,and Google Scholar and screened 128 records;26 studies met the inclusion criteria.Extracted data included study design,AI architecture,sample size,and quantitative performance metrics;study quality was assessed using Newcastle-Ottawa Scales(NOS),Cochrane RoB 2,or AMSTAR 2 as appropriate.Across included studies,AI applications in CT showed consistent improvements in sensitivity,specificity,and time-to-diagnosis in specific tasks(notably lung-nodule detection and intracranial hemorrhage triage),with reported detection-rate increases up to~20%and reduced turnaround times in several real-world implementations.Barriers include dataset bias,limited external validation,interpretability(“black-box”)concerns,workflow integration challenges,and evolving regulatory issues.Economic analyses suggest potentially favorable return on investment(ROI)in high-volume settings but are sensitive to licensing and infrastructure costs.To realize AI's benefits in CT imaging,rigorous multi-center validation,transparent reporting,humancentered workflow design,and post-deployment surveillance are essential.展开更多
In this editorial we comment on the article by Jiang et al.We focus on the Ence-phalApp Stroop test which is an innovative,smartphone-based tool specifically designed for screening minimal hepatic encephalopathy(MHE)i...In this editorial we comment on the article by Jiang et al.We focus on the Ence-phalApp Stroop test which is an innovative,smartphone-based tool specifically designed for screening minimal hepatic encephalopathy(MHE)in cirrhosis patients.Traditional MHE screening methods,while highly sensitive and specific,are often complex,time-consuming,and require controlled environmental con-ditions,limiting their widespread clinical use.The EncephalApp Stroop test si-mplifies the screening process,enhances diagnostic efficiency,and is applicable across diverse cultural contexts.However,the combination of additional bio-markers could further improve diagnostic accuracy.Despite its promising po-tential,more multicenter clinical studies are required to validate its effectiveness and applicability on a global scale.展开更多
The World Health Organization South-East Asia Regional Office estimates a significantly higher burden-around 15 million cases and 20000 deaths per year.India alone accounts for approximately 77%of the total malaria ca...The World Health Organization South-East Asia Regional Office estimates a significantly higher burden-around 15 million cases and 20000 deaths per year.India alone accounts for approximately 77%of the total malaria cases reported in this region[1,2].Among the five Plasmodium species infecting humans,Plasmodium(P.)vivax is the most prevalent species in India.Its asexual blood stages-ring forms,trophozoites,schizonts-and gametocytes are routinely identified in peripheral blood smears.However,the observation of exflagellated microgametes in human blood is exceedingly rare and typically occurs under certain in vitro conditions[3].展开更多
基金supported by the College of Oral Medicine,Taipei Medical University,Taipei,Taiwan(Grant No.TMUCOM202502)supported by Taipei Medical University Hospital,Taipei,Taiwan(Grant No.114TMUH-NE-05).
文摘This narrative review examines recent advances in salivary biomarkers for oral squamous cell carcinoma(OSCC),a major subtype of oral cancer with persistently low five-year survival rates due to delayed diagnosis.Saliva has emerged as a noninvasive diagnostic medium capable of reflecting both local tumor activity and systemic physiological changes.Various salivary biomarkers,including microRNAs,cytokines,proteins,metabolites,and exosomes,have been linked to oncogenic signaling pathways involved in tumor progression,immune modulation,and therapeutic resistance.Advances in quantitative polymerase chain reaction,mass spectrometry,and next-generation sequencing have enabled comprehensive biomarker profiling,while point-of-care detection systems and saliva-based omics platforms are accelerating clinical translation.Remaining challenges include variability in salivary composition,lack of standardized collection protocols,and insufficient validation across large patient cohorts.This review highlights the mechanistic relevance,diagnostic potential,and translational challenges of salivary biomarkers in OSCC.
文摘This study investigates the variability in cancer diagnosis across different tissues and organs, with a focus on the role of diagnostic methods such as Hematoxylin and Eosin (H&E) staining and immunohistochemistry (IHC). The predominance of female breast cancer (30%) aligns with global trends, underscoring the need for robust diagnostic protocols, particularly in developing regions. Other prevalent cancers, including skin, stomach, and cervix uteri, reflect a mix of environmental, genetic, and infectious factors. The underrepresentation of gallbladder and thyroid cancers (<1%) suggests potential underdiagnosis or lower prevalence. Age distribution data indicate peak cancer incidence in individuals aged 31 - 45 years, with gender-specific cancers like breast and cervical cancer predominantly affecting females (63.4%). The analysis also highlights significant diagnostic gaps, as 61.2% of cases did not undergo IHC testing due to resource constraints, leading to potential biases in cancer prevalence and diagnostic accuracy. The study emphasizes the complementary role of IHC in confirming ambiguous H&E findings, with strong alignment observed when both methods were used. However, the absence of IHC in many cases limits the robustness of conclusions, suggesting the need for increased access to IHC testing. The findings advocate for integrating IHC into routine diagnostics, expanding diagnostic capabilities, and improving sample sizes to ensure more reliable and comprehensive cancer data.
基金the Special Project of State Key Laboratory of Dampness Syndrome of Chinese Medicine:Study on Criteria for Diagnosis of Dampness Syndrome of Idiopathic Membranous Nephropathy,Cohort Study on Pathogenesis and Material Basis of Dampness Syndrome of Idiopathic Membranous Nephropathy,Randomized Controlled Clinical Study of Sanqi Qushi Granule in Treatment of Membranous Nephropathy(No.SZ2021ZZ02,SZ2021ZZ09 and SZ2021ZZ36)the 2020 Guangdong Provincial Science and Technology Innovation Strategy Special Fund:Guangdong-Hong Kong-Macao Joint Lab(No.2020B1212030006)+2 种基金the Natural Science Foundation of Guangdong Province:Study on the Mechanism of Sanqi Qushi Prescription Delaying Podocellular Senescence in Membranous Nephropathy based on Cyclic Guanosine Monophosphate-Adenosine Monophosphate Synthase-Stimulator of Interferon Genes-Nuclear Factor Kappa-B Signaling Pathway(No.2022A1515011628)the Guangzhou Science and Technology Plan Project:to Explore the Mechanism of Treating Membranous Nephropathy from the Perspective of Regulating Amino Acid Metabolism Disorder(No.2023A03J0746)Special Funding for Scientific and Technological Research on Traditional Chinese Medicine,Guangdong Provincial Hospital of Chinese Medicine:a Multimodular Machine Learning Prediction Model based on Pathological Image-transcriptomics and Traditional Chinese Medicine Syndromes was Used to Investigate the Prognostic Correlation of Long non-coding RNA Molecules in Nephropathy and the Intervention Mechanism of Sanqi Qushi Formula,to Investigate the Pathogenesis and Microbiological Mechanism of Dampness Syndrome of Membranous Nephropathy based on the Microecological Changes of Tongue Coating(No.YN2023MB02,YN2023MB10)。
文摘OBJECTIVE:To reach consensus on the diagnostic criteria of syndrome of dampness obstruction in idiopathic membranous nephropathy(IMN)patients by literature research and expert investigation(interviews and a Delphi method).METHODS:Our study was consistent with T/CACM 1336-2020.We searched the monographs and references published in the past 40 years(1983-2022),and established the diagnostic criteria pool of waterdampness syndrome and dampness-turbidity syndrome in Traditional Chinese Medicine(TCM)based on literature by using frequency statistics and correlation analysis.Expert investigation(interview method and two rounds of Delphi method)was used to form the diagnostic criteria of water-dampness syndrome and dampnessturbidity syndrome of idiopathic membranous nephropathy.Clinical diagnostic test research was carried out,and compared with“Diagnostic Criteria for dampness syndrome”(T/CACM 1454-2023)to evaluate the authenticity,reliability and clinical application value of the standard.RESULTS:A total of 122 relevant guides,standards,monographs and documents were included through searching books and Chinese databases.Four experts were interviewed and two rounds of delphi method(75 experts nationwide)were carried out.The experts'opinions are relatively concentrated and the differences are small.Based on the weight of each index,the diagnostic criteria indexes of water-dampness syndrome and dampness-turbidity syndrome were selected.After discussion by the core group members,the diagnostic model of"necessary symptoms and optional symptoms"was established,and the final diagnostic criteria of waterdampness syndrome and dampness-turbidity syndrome were established.One hundred and ninety-one inpatients and outpatients of Guangdong Provincial Hospital of Chinese Medicine from January 2021 to February 2023 were included in Diagnostic test study.There was no statistical difference in gender,age and course of disease(P>0.05).The sensitivity and specificity of the trial standard were 90.34%and 73.33%respectively,while the sensitivity and specificity of T/CACM 1454-2023 were 99.43%and 6.67%,respectively.CONCLUSIONS:The consensus-based diagnostic criteria for IMN can be widely incorporated in TCM.A further clinical study will be conducted to analyze the diagnosis value and cut-off score of our IMN criteria.
基金Supported by National Key Research and Development Program of China,No.2019YFE0190800.
文摘BACKGROUND Studies investigating diagnostic delays and their effects on patients with alcoholic cirrhosis.AIM To investigate the current status and associated factors influencing diagnostic delays in 401 patients with alcoholic cirrhosis.METHODS A cross-sectional analysis was conducted at a tertiary hospital in China from June 2020 to December 2023.Data were collected through telephone follow-ups and questionnaires.The Wilcoxon and Kruskal-Wallis H tests were used to compare diagnostic delays across various characteristics.Multivariate linear regression was employed to identify factors associated with diagnostic delays.RESULTS The median diagnostic delay was 5 months,with an interquartile range of 2-11 months.The proportions of patients with alcoholic cirrhosis who initially visited tertiary,secondary,and primary hospitals were 38.9%,37.91%,and 23.19%,respectively.Furthermore,the rates of patients undergoing liver computed tomography(CT)during their first visit at tertiary,secondary,and primary hospitals were 92.95%,13.82%,and 1.08%,respectively(P<0.001).Significant differences were observed in diagnostic delay-related characteristics,including residence,resident type,initial diagnosis,medical insurance,liver CT,and liver ultrasound during the first visit,age,years of education,family size,marital status,annual family income,years of drinking,daily alcohol consumption,and type of alcohol consumed(P<0.01).Furthermore,diagnostic delays were variably associated with daily alcohol consumption and other characteristics(i.e.residence,years of drinking,medical insurance,years of education,annual family income,liver CT and ultrasound during the first visit).Significant predictors of diagnostic delay identified on multivariate linear regression analysis included years of education,daily alcohol consumption,annual family income and blood ammonia levels(P<0.01).Patients with alcoholic cirrhosis experience varying degrees of diagnostic delays,necessitating interventions targeting potential contributing factors.CONCLUSION Our study indicates that patients with alcoholic cirrhosis may experience varying degrees of diagnostic delay.Interventions targeting potential factors contributing to diagnostic delay are necessary.
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.
文摘Pancreatic cancer is recognized as one of the leading causes of cancer mortality,representing the second most common source of cancer-related deaths within the gastrointestinal domain.Surgical resection is currently the only definitive treatment;however,the subtle emergence of symptoms often leads to a diagnosis at an advanced stage,with merely 10%-15%of patients being eligible for surgical intervention.The primary obstacle to achieving a potential radical resection is the presence of distant metastatic disease or invasion of adjacent major vascular structures.This review aims to highlight the critical role of endoscopic ultrasound in the diagnosis and staging of pancreatic tumors.We systematically searched PubMed,MEDLINE and Web of Science by using‘pancreatic cancer’and‘endoscopic ultrasonography’as keywords.Relevant studies were reviewed and analyzed.Endoscopic ultrasonography(EUS)is efficient in the diagnosis and staging of pancreatic cancer,past studies reported the accuracy of EUS is 63%to 94%for T-staging and 44%to 82%for N-staging but there are still limitations that need to be comprehensively applied with other diagnostic methods to evaluation of distant metastasis for surgical resectability.Our review aims to reveal the value for the staging of pancreatic cancer.
基金supported by the National Key Research&Development Program of China,Nos.2021YFC2501205(to YC),2022YFC24069004(to JL)the STI2030-Major Project,Nos.2021ZD0201101(to YC),2022ZD0211800(to YH)+2 种基金the National Natural Science Foundation of China(Major International Joint Research Project),No.82020108013(to YH)the Sino-German Center for Research Promotion,No.M-0759(to YH)a grant from Beijing Municipal Science&Technology Commission(Beijing Brain Initiative),No.Z201100005520018(to JL)。
文摘Several promising plasma biomarker proteins,such as amyloid-β(Aβ),tau,neurofilament light chain,and glial fibrillary acidic protein,are widely used for the diagnosis of neurodegenerative diseases.However,little is known about the long-term stability of these biomarker proteins in plasma samples stored at-80°C.We aimed to explore how storage time would affect the diagnostic accuracy of these biomarkers using a large cohort.Plasma samples from 229 cognitively unimpaired individuals,encompassing healthy controls and those experiencing subjective cognitive decline,as well as 99 patients with cognitive impairment,comprising those with mild cognitive impairment and dementia,were acquired from the Sino Longitudinal Study on Cognitive Decline project.These samples were stored at-80°C for up to 6 years before being used in this study.Our results showed that plasma levels of Aβ42,Aβ40,neurofilament light chain,and glial fibrillary acidic protein were not significantly correlated with sample storage time.However,the level of total tau showed a negative correlation with sample storage time.Notably,in individuals without cognitive impairment,plasma levels of total protein and tau phosphorylated protein threonine 181(p-tau181)also showed a negative correlation with sample storage time.This was not observed in individuals with cognitive impairment.Consequently,we speculate that the diagnostic accuracy of plasma p-tau181 and the p-tau181 to total tau ratio may be influenced by sample storage time.Therefore,caution is advised when using these plasma biomarkers for the identification of neurodegenerative diseases,such as Alzheimer's disease.Furthermore,in cohort studies,it is important to consider the impact of storage time on the overall results.
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Objective To qualitatively assess the diagnostic performance of dynamic contrast enhancement(DCE),diffusionweighted imaging(DWI),and T2-weighted imaging(T2WI),alone or in combination,in the evaluation of breast cancer.Methods We retrospectively reviewed the records of 394 consecutive patients with pathologically confirmed breast lesions who had undergone 3-T magnetic resonance imaging(MRI).The morphological characteristics of breast lesions were evaluated using DCE,DWI,and T2WI based on BI-RADS lexicon descriptors by trained radiologists.Patients were categorized into mass and non-mass groups based on MRI characteristics of the lesions,and the differences between benign and malignant lesions in each group were compared.Clinical prediction models for breast cancer diagnosis were constructed using logistic regression analysis.Diagnostic efficacies were compared using the area under the receiver operating characteristic curve(AUC)and DeLong test.Results For mass-like lesions,all the morphological parameters significantly differentiated benign and malignant lesions on consensus DCE,DWI,and T2WI(P<0.05).The combined method(DCE+DWI+T2WI)had a higher AUC(0.865)than any of the individual modality(DCE:0.786;DWI:0.793;T2WI:0.809)(P<0.05).For non-mass-like lesions,DWI signal intensity was a significant predictor of malignancy(P=0.036),but the model using DWI alone had a low AUC(0.669).Conclusion Morphological assessment using the combination of DCE,DWI,and T2WI provides better diagnostic value in differentiating benign and malignant breast mass-like lesions than assessment with only one of the modalities.
基金supported by the Special Project for the Construction of Innovative Provinces in Hunan Province,China(2020SK2013)。
文摘Objective:In recent years,the incidence and detection rate of pancreatic cystic lesions(PCLs)have increased significantly.Endoscopic ultrasound(EUS)plays an indispensable role in the diagnosis and differential diagnosis of PCLs.However,evidence comparing the diagnostic performance of EUS-guided fine-needle aspiration(EUS-FNA)and fine-needle biopsy(FNB)remains limited.This study aims to compare the diagnostic yield,adequacy of tissue acquisition,and safety between EUS-FNA and EUS-FNB in evaluating PCLs to inform clinical practice.Methods:A retrospective review was conducted on patients with PCLs who underwent either EUS-FNA or EUS-FNB between January 2014 and August 2021.The diagnostic yield,tissue acquisition adequacy,and incidence of adverse events were compared between the 2 groups.Results:A total of 90 patients with PCLs were included(52 in the FNA group and 38 in the FNB group).The diagnostic yield was similar between the FNA and FNB groups(94.2%vs 94.7%,P>0.05).The adequacy of tissue acquisition was 71.2%in the FNA group and 81.6%in the FNB group(P>0.05).No statistically significant difference was observed in the incidence of adverse events between the 2 groups(P>0.05).Conclusion:Both EUS-FNA and EUS-FNB demonstrate equally high diagnostic yields and tissue adequacy in PCLs,with excellent safety profiles.Both methods are safe and effective diagnostic tools for evaluating PCLs.
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BACKGROUND Early detection of precancerous lesions is of vital importance for reducing the incidence and mortality of upper gastrointestinal(UGI)tract cancer.However,traditional endoscopy has certain limitations in detecting precancerous lesions.In contrast,real-time computer-aided detection(CAD)systems enhanced by artificial intelligence(AI)systems,although they may increase unnecessary medical procedures,can provide immediate feedback during examination,thereby improving the accuracy of lesion detection.This article aims to conduct a meta-analysis of the diagnostic performance of CAD systems in identifying precancerous lesions of UGI tract cancer during esophagogastroduodenoscopy(EGD),evaluate their potential clinical application value,and determine the direction for further research.AIM To investigate the improvement of the efficiency of EGD examination by the realtime AI-enabled real-time CAD system(AI-CAD)system.METHODS PubMed,EMBASE,Web of Science and Cochrane Library databases were searched by two independent reviewers to retrieve literature with per-patient analysis with a deadline up until April 2025.A meta-analysis was performed with R Studio software(R4.5.0).A random-effects model was used and subgroup analysis was carried out to identify possible sources of heterogeneity.RESULTS The initial search identified 802 articles.According to the inclusion criteria,2113 patients from 10 studies were included in this meta-analysis.The pooled accuracy difference,logarithmic difference of diagnostic odds ratios,sensitivity,specificity and the area under the summary receiver operating characteristic curve(area under the curve)of both AI group and endoscopist group for detecting precancerous lesion were 0.16(95%CI:0.12-0.20),-0.19(95%CI:-0.75-0.37),0.89(95%CI:0.85-0.92,AI group),0.67(95%CI:0.63-0.71,endoscopist group),0.89(95%CI:0.84-0.93,AI group),0.77(95%CI:0.70-0.83,endoscopist group),0.928(95%CI:0.841-0.948,AI group),0.722(95%CI:0.677-0.821,endoscopist group),respectively.CONCLUSION The present studies further provide evidence that the AI-CAD is a reliable endoscopic diagnostic tool that can be used to assist endoscopists in detection of precancerous lesions in the UGI tract.It may be introduced on a large scale for clinical application to enhance the accuracy of detecting precancerous lesions in the UGI tract.
基金Supported by The Catholic Medical Center Research Foundation Made in The Program Year of 2022.
文摘BACKGROUND Brush cytology is the most commonly used technique for tissue acquisition during endoscopic retrograde cholangiopancreatography for the evaluation of biliary strictures.Nonetheless,brush cytology is limited by its low sensitivity due to insufficient cellular yield.AIM To evaluate the impact of the sheath-rinse technique on improving the cellularity yield.METHODS A total of 112 patients with suspected malignant biliary strictures were enrolled at two tertiary centers in South Korea.The sample cellularity and diagnostic accuracy of brush-wash and sheath-rinse specimens were compared.RESULTS A significantly increased number of total cell clusters per representative 20×field was recorded in the sheath-rinse compared with the brush-wash specimens(median:12 vs 3,P<0.001).This trend persisted when large(>50 cells)clusters(median:8 vs 3,P<0.001),medium(6-49 cells)(median:7 vs 3,P<0.001),and small(2-5 cells)clusters(median:9 vs 3,P<0.001)were evaluated.Diagnostic accuracy and sensitivity for differentiating malignancy were superior with sheath-rinsing than with the brush-wash method(72.3%vs 62.5%,P<0.001 and 69.9%vs 59.2%,P<0.001,respectively).CONCLUSION Incorporating sheath-rinse specimens significantly improved the yield and diagnostic accuracy of biliary brush cytology.Sheath-rinsing should be integrated into routine clinical practice to improve diagnostic performance for biliary strictures.
文摘Background:Convolutional neural networks(CNN)have achieved remarkable success in medical image analysis.However,unlike some general-domain tasks where model accuracy is paramount,medical applications demand both accuracy and explainability due to the high stakes affecting patients'lives.Based on model explanations,clinicians can evaluate the diagnostic decisions suggested by CNN.Nevertheless,prior explainable artificial intelligence methods treat medical image tasks akin to general vision tasks,following end-to-end paradigms to generate explanations and frequently overlooking crucial clinical domain knowledge.Methods:We propose a plug-and-play module that explicitly integrates anatomic boundary information into the explanation process for CNN-based thoracopathy classifiers.To generate the anatomic boundary of the lung parenchyma,we utilize a lung segmentation model developed on external public datasets and deploy it on the unseen target dataset to constrain model ex-planations within the lung parenchyma for the clinical task of thoracopathy classification.Results:Assessed by the intersection over union and dice similarity coefficient between model-extracted explanations and expert-annotated lesion areas,our method consistently outperformed the baseline devoid of clinical domain knowledge in 71 out of 72 scenarios,encompassing 3 CNN architectures(VGG-11,ResNet-18,and AlexNet),2 classification settings(binary and multi-label),3 explanation methods(Saliency Map,Grad-CAM,and Integrated Gradients),and 4 co-occurred thoracic diseases(Atelectasis,Fracture,Mass,and Pneumothorax).Conclusions:We underscore the effectiveness of leveraging radiology knowledge in improving model explanations for CNN and envisage that it could inspire future efforts to integrate clinical domain knowledge into medical image analysis.
文摘BACKGROUND Diagnostic laparoscopy is a minimally invasive surgical method to diagnose intra-abdominal diseases.AIM To evaluate patients with unexplained ascites who could not be definitively diagnosed via advanced radiological and endoscopic methods and serological,cytological,and microbiological examinations and,therefore,underwent diag-nostic laparoscopy.METHODS This retrospective analysis evaluated 82 patients who underwent diagnostic laparoscopy due to unexplained ascites.Patients’medical records were obtained from the hospital database.Their age,sex,complaints at admission,laboratory results,radiological imaging results,diagnostic laparoscopy reports,and pa-thology reports were analyzed.RESULTS The serum-ascites albumin gradient was<1.1 in 96.3%of the patients(n=79).Among patients,22(26.8%)had benign diagnoses and 60(73.2%)had malignant diagnoses.In addition,55(67.1%)were deceased,and the median follow-up time from diagnosis to death was four months.The overall follow-up time ranged from 1 to 142 months,with a median of 14 months.Patients’diagnoses were significantly associated with their survival(P<0.05,χ2 test).The mortality rate was 86.7%among patients with malignant diagnoses and 13.6%among patients with benign diagnoses.CONCLUSION Diagnostic laparoscopy is minimally invasive,has a low complication rate,and requires a short hospital stay.It can be safely performed to diagnose and treat ascites that remain unexplained after advanced radiological and endoscopic examinations.
文摘Introduction: Crohn’s Disease (CD) is a chronic inflammatory disorder with a heterogeneous presentation. While diarrhea, abdominal pain, and weight loss are hallmarks, atypical manifestations can obscure the diagnosis. This report highlights an unusual presentation of CD to emphasize the need for comprehensive diagnostic strategies. Case Report: A 25-year-old male presented with peripheral edema, anorexia, and abdominal distension but lacked classic gastrointestinal (GI) symptoms. Laboratory findings included microcytic anemia and hypoalbuminemia, while imaging revealed ascites and bowel wall thickening. Elevated fecal calprotectin and positive Anti-Saccharomyces cerevisiae antibodies (ASCA) supported the diagnosis. Endoscopy confirmed ileocolic Crohn’s Disease (L3 + L4). Infliximab therapy resulted in marked clinical improvement. Discussion: This case underscores the complexity of atypical CD presentations. Early use of serological markers, imaging, and endoscopy guided the diagnosis. Recognition of CD’s diverse manifestations is critical for timely intervention. Conclusion: Atypical CD presentations require heightened clinical suspicion and a multidisciplinary approach to reduce diagnostic delays and improve patient outcomes.
文摘Artificial intelligence(AI)is rapidly transforming radiology and computed tomography(CT)imaging by enabling automated image analysis,improved diagnostic accuracy,and clinical decision-support.We performed a systematic review of peerreviewed studies published between January 1,2010 and March 31,2025 to quantify reported gains in diagnostic performance and workflow efficiency,to evaluate clinical decision-support benefits and risks,and to identify integration priorities.We searched PubMed,IEEE Xplore,Scopus,ScienceDirect,and Google Scholar and screened 128 records;26 studies met the inclusion criteria.Extracted data included study design,AI architecture,sample size,and quantitative performance metrics;study quality was assessed using Newcastle-Ottawa Scales(NOS),Cochrane RoB 2,or AMSTAR 2 as appropriate.Across included studies,AI applications in CT showed consistent improvements in sensitivity,specificity,and time-to-diagnosis in specific tasks(notably lung-nodule detection and intracranial hemorrhage triage),with reported detection-rate increases up to~20%and reduced turnaround times in several real-world implementations.Barriers include dataset bias,limited external validation,interpretability(“black-box”)concerns,workflow integration challenges,and evolving regulatory issues.Economic analyses suggest potentially favorable return on investment(ROI)in high-volume settings but are sensitive to licensing and infrastructure costs.To realize AI's benefits in CT imaging,rigorous multi-center validation,transparent reporting,humancentered workflow design,and post-deployment surveillance are essential.
基金Supported by The Basic and Clinical Integration Project of Xi'an Jiaotong University,No.YXJLRH2022067.
文摘In this editorial we comment on the article by Jiang et al.We focus on the Ence-phalApp Stroop test which is an innovative,smartphone-based tool specifically designed for screening minimal hepatic encephalopathy(MHE)in cirrhosis patients.Traditional MHE screening methods,while highly sensitive and specific,are often complex,time-consuming,and require controlled environmental con-ditions,limiting their widespread clinical use.The EncephalApp Stroop test si-mplifies the screening process,enhances diagnostic efficiency,and is applicable across diverse cultural contexts.However,the combination of additional bio-markers could further improve diagnostic accuracy.Despite its promising po-tential,more multicenter clinical studies are required to validate its effectiveness and applicability on a global scale.
文摘The World Health Organization South-East Asia Regional Office estimates a significantly higher burden-around 15 million cases and 20000 deaths per year.India alone accounts for approximately 77%of the total malaria cases reported in this region[1,2].Among the five Plasmodium species infecting humans,Plasmodium(P.)vivax is the most prevalent species in India.Its asexual blood stages-ring forms,trophozoites,schizonts-and gametocytes are routinely identified in peripheral blood smears.However,the observation of exflagellated microgametes in human blood is exceedingly rare and typically occurs under certain in vitro conditions[3].
