Avoiding misdiagnosis of multilocular thymic cysts as malignant tumors on computer tomography

This editorial provides insights from a case report by Sun et al published in the World Journal of Clinical Cases.The case report focuses on a case where a multilocular thymic cyst(MTC)was misdiagnosed as a thymic tumor,resulting in an unnecessary surgical procedure.Both MTCs and thymic tumors are rare conditions that heavily rely on radiological imaging for accurate diagnosis.However,the similarity in their imaging presentations can lead to misinterpretation,resulting in unnecessary surgical procedures.Due to the ongoing lack of comprehensive knowledge about MTCs and thymic tumors,we offer a summary of diagnostic techniques documented in recent literature and examine potential causes of misdiagnosis.When computer tomography(CT)values surpass 20 Hounsfield units and display comparable morphology,there is a risk of misdiagnosing MTCs as thymic tumors.Employing various differential diagnostic methods like biopsy,molecular biology,multi-slice CT,CT functional imaging,positron emission tomography/CT molecular functional imaging,magnetic resonance imaging and radiomics,proves advantageous in reducing clinical misdiagnosis.A deeper understanding of these conditions requires increased attention and exploration by healthcare providers.Moreover,the continued advancement and utilization of various diagnostic methods are expected to enhance precise diagnoses,provide appropriate treatment options,and improve the quality of life for patients with thymic tumors and MTCs in the future.continued advancement and utilization of various diagnostic methods are expected to enhance precise diagnoses,provide appropriate treatment options,and improve the quality of life for patients with thymic tumors and MTCs in the future.

Artificial intelligence for characterization of diminutive colorectal polyps:A feasibility study comparing two computer-aided diagnosis systems

BACKGROUND Artificial intelligence(AI)has potential in the optical diagnosis of colorectal polyps.AIM To evaluate the feasibility of the real-time use of the computer-aided diagnosis system(CADx)AI for ColoRectal Polyps(AI4CRP)for the optical diagnosis of diminutive colorectal polyps and to compare the performance with CAD EYE^(TM)(Fujifilm,Tokyo,Japan).CADx influence on the optical diagnosis of an expert endoscopist was also investigated.METHODS AI4CRP was developed in-house and CAD EYE was proprietary software provided by Fujifilm.Both CADxsystems exploit convolutional neural networks.Colorectal polyps were characterized as benign or premalignant and histopathology was used as gold standard.AI4CRP provided an objective assessment of its characterization by presenting a calibrated confidence characterization value(range 0.0-1.0).A predefined cut-off value of 0.6 was set with values<0.6 indicating benign and values≥0.6 indicating premalignant colorectal polyps.Low confidence characterizations were defined as values 40%around the cut-off value of 0.6(<0.36 and>0.76).Self-critical AI4CRP’s diagnostic performances excluded low confidence characterizations.RESULTS AI4CRP use was feasible and performed on 30 patients with 51 colorectal polyps.Self-critical AI4CRP,excluding 14 low confidence characterizations[27.5%(14/51)],had a diagnostic accuracy of 89.2%,sensitivity of 89.7%,and specificity of 87.5%,which was higher compared to AI4CRP.CAD EYE had a 83.7%diagnostic accuracy,74.2%sensitivity,and 100.0%specificity.Diagnostic performances of the endoscopist alone(before AI)increased nonsignificantly after reviewing the CADx characterizations of both AI4CRP and CAD EYE(AI-assisted endoscopist).Diagnostic performances of the AI-assisted endoscopist were higher compared to both CADx-systems,except for specificity for which CAD EYE performed best.CONCLUSION Real-time use of AI4CRP was feasible.Objective confidence values provided by a CADx is novel and self-critical AI4CRP showed higher diagnostic performances compared to AI4CRP.

Stage at diagnosis of colorectal cancer through diagnostic route:Who should be screened?

BACKGROUND Colorectal cancer(CRC)is a global health concern,with advanced-stage diagnoses contributing to poor prognoses.The efficacy of CRC screening has been well-established;nevertheless,a significant proportion of patients remain unscreened,with>70%of cases diagnosed outside screening.Although identifying specific subgroups for whom CRC screening should be particularly recommended is crucial owing to limited resources,the association between the diagnostic routes and identification of these subgroups has been less appreciated.In the Japanese cancer registry,the diagnostic routes for groups discovered outside of screening are primarily categorized into those with comorbidities found during hospital visits and those with CRC-related symptoms.AIM To clarify the stage at CRC diagnosis based on diagnostic routes.METHODS We conducted a retrospective observational study using a cancer registry of patients with CRC between January 2016 and December 2019 at two hospitals.The diagnostic routes were primarily classified into three groups:Cancer screening,follow-up,and symptomatic.The early-stage was defined as Stages 0 or I.Multivariate and univariate logistic regressions were exploited to determine the odds of early-stage diagnosis in the symptomatic and cancer screening groups,referencing the follow-up group.The adjusted covariates were age,sex,and tumor location.RESULTS Of the 2083 patients,715(34.4%),1064(51.1%),and 304(14.6%)belonged to the follow-up,symptomatic,and cancer screening groups,respectively.Among the 2083 patients,CRCs diagnosed at an early stage were 57.3%(410 of 715),23.9%(254 of 1064),and 59.5%(181 of 304)in the follow-up,symptomatic,and cancer screening groups,respectively.The symptomatic group exhibited a lower likelihood of early-stage diagnosis than the follow-up group[P<0.001,adjusted odds ratio(aOR),0.23;95%confidence interval(95%CI):0.19-0.29].The likelihood of diagnosis at an early stage was similar between the follow-up and cancer screening groups(P=0.493,aOR for early-stage diagnosis in the cancer screening group vs follow-up group=1.11;95%CI=0.82-1.49).CONCLUSION CRCs detected during hospital visits for comorbidities were diagnosed earlier,similar to cancer screening.CRC screening should be recommended,particularly for patients without periodical hospital visits for comorbidities.

Value of procalcitonin and presepsin in the diagnosis and severity stratification of sepsis and septic shock

Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection.[1,2]Septic shock,the most severe form of sepsis,is characterized by circulatory and cellular/metabolic abnormalities,and can increase mortality to>40%.[1-3]Early recognition and risk stratification of septic shock are crucial but challenging because of the heterogeneity of its presentation and progression.

Dynamic Vision Enabled Contactless Cross-Domain Machine Fault Diagnosis With Neuromorphic Computing

Dear Editor,This letter presents a novel dynamic vision enabled contactless cross-domain fault diagnosis method with neuromorphic computing.The event-based camera is adopted to capture the machine vibration states in the perspective of vision.

Diagnosis of poorly differentiated adenocarcinoma of the stomach by confocal laser endomicroscopy:A case report

BACKGROUND In recent years,confocal laser endomicroscopy(CLE)has become a new endoscopic imaging technology at the microscopic level,which is extensively performed for real-time in vivo histological examination.CLE can be performed to distinguish benign from malignant lesions.In this study,we diagnosed using CLE an asymptomatic patient with poorly differentiated gastric adenocarcinoma.CASE SUMMARY A 63-year-old woman was diagnosed with gastric mucosal lesions,which may be gastric cancer,in the small curvature of the stomach by gastroscopy.She consented to undergo CLE for morphological observation of the gastric mucosa.Through the combination of CLE diagnosis and postoperative pathology,the intraoperative CLE diagnosis was considered to be reliable.According to our experience,CLE can be performed as the first choice for the diagnosis of gastric cancer.CONCLUSION CLE has several advantages over pathological diagnosis.We believe that CLE has great potential in the diagnosis of benign and malignant gastric lesions.

Handheld bedside ultrasound in the diagnosis of myocarditis

Myocarditis is a disease process that every emergency physician fears missing.Its severity can be mild to life-threatening,and many cases are likely undetected because they are subclinical with nonspecifi c signs.[1]Subtle cardiac signs may be overshadowed by systemic symptoms of the underlying infectious process.Fever,myalgias,lethargy,symptoms commonly associated with viral syndrome,can mask the life-threatening myocarditis that may be present.In fact,in the United States Myocarditis Treatment Trial,almost 90%of patients reported symptoms consistent with a viral prodrome.[2]Ammirati et al[3]reported that 27%of patients with myocarditis had either reduced left ventricular ejection fraction,ventricular arrhythmias,or low cardiac output.Here,we present a case report,in which handheld point-of-care ultrasound was utilized at the bedside to aid in the critical diagnosis of myocarditis.With the additional information provided through this imaging modality,this patient was able to be transferred to the appropriate tertiary care facility in an expeditious manner and receive possible defi nitive treatment.

Circular RNAs in breast cancer diagnosis,treatment and prognosis

Breast cancer has surpassed lung cancer to become the most common malignancy worldwide.The incidence rate and mortality rate of breast cancer continue to rise,which leads to a great burden on public health.Circular RNAs(circRNAs),a new class of noncoding RNAs(ncRNAs),have been recognized as important oncogenes or suppressors in regulating cancer initiation and progression.In breast cancer,circRNAs have significant roles in tumorigenesis,recurrence and multidrug resistance that are mediated by various mechanisms.Therefore,circRNAs may serve as promising targets of therapeutic strategies for breast cancer management.This study reviews the most recent studies about the biosynthesis and characteristics of circRNAs in diagnosis,treatment and prognosis evaluation,as well as the value of circRNAs in clinical applications as biomarkers or therapeutic targets in breast cancer.Understanding the mechanisms by which circRNAs function could help transform basic research into clinical applications and facilitate the development of novel circRNA-based therapeutic strategies for breast cancer treatment.

Chinese Guidelines for the Diagnosis and Management of Atrial Fibrillation

Atrial fibrillation(AF)is the most common sustained cardiac arrhythmia,significantly impacting patients’quality of life and increasing the risk of death,stroke,heart failure,and dementia.Over the past two decades,there have been significant breakthroughs in AF risk prediction and screening,stroke prevention,rhythm control,catheter ablation,and integrated management.During this period,the scale,quality,and experience of AF management in China have greatly improved,providing a solid foundation for the development of guidelines for the diagnosis and management of AF.To further promote standardized AF management,and apply new technologies and concepts to clinical practice in a timely and comprehensive manner,the Chinese Society of Cardiology of the Chinese Medical Association and the Heart Rhythm Committee of the Chinese Society of Biomedical Engineering have jointly developed the Chinese Guidelines for the Diagnosis and Management of Atrial Fibrillation.The guidelines have comprehensively elaborated on various aspects of AF management and proposed the CHA2DS2-VASc-60 stroke risk score based on the characteristics of AF in the Asian population.The guidelines have also reevaluated the clinical application of AF screening,emphasized the significance of early rhythm control,and highlighted the central role of catheter ablation in rhythm control.

Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain.However,in engineering scenarios,achieving such high-quality label annotation is difficult and expensive.The incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled samples.To overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three parts.First,a residual network with dual classifiers is to learn features from cross-domain samples.Second,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain.With the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised learning.Third,the adaptation trajectories are designed for sample distributions across domains.This ensures that the target domain samples are only adapted with the pure-labeled samples.The LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines.The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.

Expert Experience and Data-Driven Based Hybrid Fault Diagnosis for High-SpeedWire Rod Finishing Mills

The reliable operation of high-speed wire rod finishing mills is crucial in the steel production enterprise.As complex system-level equipment,it is difficult for high-speed wire rod finishing mills to realize fault location and real-time monitoring.To solve the above problems,an expert experience and data-driven-based hybrid fault diagnosis method for high-speed wire rod finishing mills is proposed in this paper.First,based on its mechanical structure,time and frequency domain analysis are improved in fault feature extraction.The approach of combining virtual value,peak value with kurtosis value index,is adopted in time domain analysis.Speed adjustment and side frequency analysis are proposed in frequency domain analysis to obtain accurate component characteristic frequency and its corresponding sideband.Then,according to time and frequency domain characteristics,fault location based on expert experience is proposed to get an accurate fault result.Finally,the proposed method is implemented in the equipment intelligent diagnosis system.By taking an equipment fault on site,for example,the effectiveness of the proposed method is illustrated in the system.

Marker Ki-67 is a potential biomarker for the diagnosis and prognosis of prostate cancer based on two cohorts

BACKGROUND Prostate cancer(PCa)is a widespread malignancy,predominantly affecting elderly males,and current methods for diagnosis and treatment of this disease continue to fall short.The marker Ki-67(MKI67)has been previously demonstrated to correlate with the proliferation and metastasis of various cancer cells,including those of PCa.Hence,verifying the association between MKI67 and the diagnosis and prognosis of PCa,using bioinformatics databases and clinical data analysis,carries significant clinical implications.AIM To explore the diagnostic and prognostic efficacy of antigens identified by MKI67 expression in PCa.METHODS For cohort 1,the efficacy of MKI67 diagnosis was evaluated using data from The Cancer Genome Atlas(TCGA)and Genotype-Tissue Expression(GTEx)databases.For cohort 2,the diagnostic and prognostic power of MKI67 expression was further validated using data from 271 patients with clinical PCa.RESULTS In cohort 1,MKI67 expression was correlated with prostate-specific antigen(PSA),Gleason Score,T stage,and N stage.The receiver operating characteristic(ROC)curve showed a strong diagnostic ability,and the Kaplan-Meier method demonstrated that MKI67 expression was negatively associated with the progression-free interval(PFI).The time-ROC curve displayed a weak prognostic capability for MKI67 expression in PCa.In cohort 2,MKI67 expression was significantly related to the Gleason Score,T stage,and N stage;however,it was negatively associated with the PFI.The time-ROC curve revealed the stronger prognostic capability of MKI67 in patients with PCa.Multivariate COX regression analysis was performed to select risk factors,including PSA level,N stage,and MKI67 expression.A nomogram was established to predict the 3-year PFI.CONCLUSION MKI67 expression was positively associated with the Gleason Score,T stage,and N stage and showed a strong diagnostic and prognostic ability in PCa.

Activatable fluorescent probes for imaging and diagnosis of rheumatoid arthritis

Rheumatoid arthritis(RA)is a systemic autoimmune disease that is primarily manifested as synovitis and polyarticular opacity and typically leads to serious joint damage and irreversible disability,thus adversely affecting locomotion ability and life quality.Consequently,good prognosis heavily relies on the early diagnosis and effective therapeutic monitoring of RA.Activatable fluorescent probes play vital roles in the detection and imaging of biomarkers for disease diagnosis and in vivo imaging.Herein,we review the fluorescent probes developed for the detection and imaging of RA biomarkers,namely reactive oxygen/nitrogen species(hypochlorous acid,peroxynitrite,hydroxyl radical,nitroxyl),pH,and cysteine,and address the related challenges and prospects to inspire the design of novel fluorescent probes and the improvement of their performance in RA studies.

An Automatic Implementation of Oropharyngeal Swab Sampling for Diagnosing Respiratory Infectious Diseases via Soft Robotic End-Effectors

The most widely adopted method for diagnosing respiratory infectious diseases is to conduct polymerase chain reaction(PCR)assays on patients’respiratory specimens,which are collected through either nasal or oropharyngeal swabs.The manual swab sampling process poses a high risk to the examiner and may cause false-negative results owing to improper sampling.In this paper,we propose a pneumatically actuated soft end-effector specifically designed to achieve all of the tasks involved in swab sampling.The soft end-effector utilizes circumferential instability to ensure grasping stability,and exhibits several key properties,including high load-to-weight ratio,error tolerance,and variable swab-tip stiffness,leading to successful automatic robotic oropharyngeal swab sampling,from loosening and tightening the transport medium tube cap,holding the swab,and conducting sampling,to snapping off the swab tail and sterilizing itself.Using an industrial collaborative robotic arm,we integrated the soft end-effector,force sensor,camera,lights,and remote-control stick,and developed a robotic oropharyngeal swab sampling system.Using this swab sampling system,we conducted oropharyngeal swab-sampling tests on 20 volunteers.Our Digital PCR assay results(RNase P RNA gene absolute copy numbers for the samples)revealed that our system successfully collected sufficient numbers of cells from the pharyngeal wall for respiratory disease diagnosis.In summary,we have developed a pharyngeal swab-sampling system based on an“enveloping”soft actuator,studied the sampling process,and imple-mented whole-process robotic oropharyngeal swab-sampling.

Real-Time Intelligent Diagnosis of Co-frequency Vibration Faults in Rotating Machinery Based on Lightweight-Convolutional Neural Networks

The co-frequency vibration fault is one of the common faults in the operation of rotating equipment,and realizing the real-time diagnosis of the co-frequency vibration fault is of great significance for monitoring the health state and carrying out vibration suppression of the equipment.In engineering scenarios,co-frequency vibration faults are highlighted by rotational frequency and are difficult to identify,and existing intelligent methods require more hardware conditions and are exclusively time-consuming.Therefore,Lightweight-convolutional neural networks(LW-CNN)algorithm is proposed in this paper to achieve real-time fault diagnosis.The critical parameters are discussed and verified by simulated and experimental signals for the sliding window data augmentation method.Based on LW-CNN and data augmentation,the real-time intelligent diagnosis of co-frequency is realized.Moreover,a real-time detection method of fault diagnosis algorithm is proposed for data acquisition to fault diagnosis.It is verified by experiments that the LW-CNN and sliding window methods are used with high accuracy and real-time performance.

Effectiveness of Histopathological Examination of Ultrasound-guided Puncture Biopsy Samples for Diagnosis of Extrapulmonary Tuberculosis

Objective To evaluate the diagnostic value of histopathological examination of ultrasound-guided puncture biopsy samples in extrapulmonary tuberculosis(EPTB).Methods This study was conducted at the Shanghai Public Health Clinical Center.A total of 115patients underwent ultrasound-guided puncture biopsy,followed by MGIT 960 culture(culture),smear,Gene Xpert MTB/RIF(Xpert),and histopathological examination.These assays were performed to evaluate their effectiveness in diagnosing EPTB in comparison to two different diagnostic criteria:liquid culture and composite reference standard(CRS).Results When CRS was used as the reference standard,the sensitivity and specificity of culture,smear,Xpert,and histopathological examination were(44.83%,89.29%),(51.72%,89.29%),(70.11%,96.43%),and(85.06%,82.14%),respectively.Based on liquid culture tests,the sensitivity and specificity of smear,Xpert,and pathological examination were(66.67%,72.60%),(83.33%,63.01%),and(92.86%,45.21%),respectively.Histopathological examination showed the highest sensitivity but lowest specificity.Further,we found that the combination of Xpert and histopathological examination showed a sensitivity of 90.80%and a specificity of 89.29%.Conclusion Ultrasound-guided puncture sampling is safe and effective for the diagnosis of EPTB.Compared with culture,smear,and Xpert,histopathological examination showed higher sensitivity but lower specificity.The combination of histopathology with Xpert showed the best performance characteristics.

Causal temporal graph attention network for fault diagnosis of chemical processes

Fault detection and diagnosis(FDD)plays a significant role in ensuring the safety and stability of chemical processes.With the development of artificial intelligence(AI)and big data technologies,data-driven approaches with excellent performance are widely used for FDD in chemical processes.However,improved predictive accuracy has often been achieved through increased model complexity,which turns models into black-box methods and causes uncertainty regarding their decisions.In this study,a causal temporal graph attention network(CTGAN)is proposed for fault diagnosis of chemical processes.A chemical causal graph is built by causal inference to represent the propagation path of faults.The attention mechanism and chemical causal graph were combined to help us notice the key variables relating to fault fluctuations.Experiments in the Tennessee Eastman(TE)process and the green ammonia(GA)process showed that CTGAN achieved high performance and good explainability.

Digital pathology-based artificial intelligence models for differential diagnosis and prognosis of sporadic odontogenic keratocysts

Odontogenic keratocyst(OKC)is a common jaw cyst with a high recurrence rate.OKC combined with basal cell carcinoma as well as skeletal and other developmental abnormalities is thought to be associated with Gorlin syndrome.Moreover,OKC needs to be differentiated from orthokeratinized odontogenic cyst and other jaw cysts.Because of the different prognosis,differential diagnosis of several cysts can contribute to clinical management.We collected 519 cases,comprising a total of 2157 hematoxylin and eosinstained images,to develop digital pathology-based artificial intelligence(AI)models for the diagnosis and prognosis of OKC.The Inception_v3 neural network was utilized to train and test models developed from patch-level images.Finally,whole slide imagelevel AI models were developed by integrating deep learning-generated pathology features with several machine learning algorithms.The AI models showed great performance in the diagnosis(AUC=0.935,95%CI:0.898–0.973)and prognosis(AUC=0.840,95%CI:0.751–0.930)of OKC.The advantages of multiple slides model for integrating of histopathological information are demonstrated through a comparison with the single slide model.Furthermore,the study investigates the correlation between AI features generated by deep learning and pathological findings,highlighting the interpretative potential of AI models in the pathology.Here,we have developed the robust diagnostic and prognostic models for OKC.The AI model that is based on digital pathology shows promise potential for applications in odontogenic diseases of the jaw.

Hierarchical multihead self-attention for time-series-based fault diagnosis

Fault diagnosis is important for maintaining the safety and effectiveness of chemical process.Considering the multivariate,nonlinear,and dynamic characteristic of chemical process,many time-series-based data-driven fault diagnosis methods have been developed in recent years.However,the existing methods have the problem of long-term dependency and are difficult to train due to the sequential way of training.To overcome these problems,a novel fault diagnosis method based on time-series and the hierarchical multihead self-attention(HMSAN)is proposed for chemical process.First,a sliding window strategy is adopted to construct the normalized time-series dataset.Second,the HMSAN is developed to extract the time-relevant features from the time-series process data.It improves the basic self-attention model in both width and depth.With the multihead structure,the HMSAN can pay attention to different aspects of the complicated chemical process and obtain the global dynamic features.However,the multiple heads in parallel lead to redundant information,which cannot improve the diagnosis performance.With the hierarchical structure,the redundant information is reduced and the deep local time-related features are further extracted.Besides,a novel many-to-one training strategy is introduced for HMSAN to simplify the training procedure and capture the long-term dependency.Finally,the effectiveness of the proposed method is demonstrated by two chemical cases.The experimental results show that the proposed method achieves a great performance on time-series industrial data and outperforms the state-of-the-art approaches.

Movement analysis in the diagnosis and management of Parkinson’s disease

Challenges in the diagnosis and treatment of Parkinson’s disease:Parkinson’s disease(PD)is an increasingly prevalent neurodegenerative disease,at first sight primarily characterized by motor symptoms,although non-motor symptoms also constitute a major part of the overall phenotype.Clinically,this disease cannot be diagnosed reliably until a large part of the vulnerable dopaminergic neurons has been irretrievably lost,and the disease progresses inexorably.New biological criteria for PD have been proposed recently and might eventually improve early diagnosis,but they require further validation,and their use will initially be restricted to a research environment(Darweesh et al.,2024).