Application of artificial intelligence in the diagnosis and treatment of Kawasaki disease

This editorial provides commentary on an article titled"Potential and limitationsof ChatGPT and generative artificial intelligence(AI)in medical safety education"recently published in the World Journal of Clinical Cases.AI has enormous potentialfor various applications in the field of Kawasaki disease(KD).One is machinelearning(ML)to assist in the diagnosis of KD,and clinical prediction models havebeen constructed worldwide using ML;the second is using a gene signalcalculation toolbox to identify KD,which can be used to monitor key clinicalfeatures and laboratory parameters of disease severity;and the third is using deeplearning(DL)to assist in cardiac ultrasound detection.The performance of the DLalgorithm is similar to that of experienced cardiac experts in detecting coronaryartery lesions to promoting the diagnosis of KD.To effectively utilize AI in thediagnosis and treatment process of KD,it is crucial to improve the accuracy of AIdecision-making using more medical data,while addressing issues related topatient personal information protection and AI decision-making responsibility.AIprogress is expected to provide patients with accurate and effective medicalservices that will positively impact the diagnosis and treatment of KD in thefuture.

Utilization of aggregation-induced emission materials in urinary system diseases

With the development of aggregation-induced emission(AIE)materials,the draw-backs of conventionalfluorescence materials subjected to aggregation-caused quenching(ACQ)have been resolved.This has allowed for the improvement of novel AIEfluorescent materials that exhibit enhanced photostability,a higher signal-to-noise ratio,and better imaging quality.Meanwhile,the enhanced phototherapeutic effect of AIE materials has garnered widespread attention in the realm of tumor treatment.The distinct physiological and anatomical characteristics of the urinary system make it suitable for the use of AIE materials.Additionally,AIE-based pho-totherapy provides a superior solution to deal with the weaknesses of conventional treatments for urologic neoplasms.In this review,the scientific advancement on the use of AIE materials in urinary system diseases since the emergence of the AIE con-cept is reviewed in detail.The review highlights the promise of AIE materials for biomarkers detection,fluorescence imaging(FLI)in vivo and in vitro,AIE-based phototherapy,and synergistic therapy from both diagnostic and therapeutic view-points.It isfirmly believed that AIE materials hold immense untapped potential for the diagnosis and treatment of urologic disease,as well as all diseases of the human body.

Simulated Annealing with Deep Learning Based Tongue Image Analysis for Heart Disease Diagnosis

Tongue image analysis is an efficient and non-invasive technique to determine the internal organ condition of a patient in oriental medicine,for example,traditional Chinese medicine(TCM),Japanese traditional herbal medicine,and traditional Korean medicine(TKM).The diagnosis procedure is mainly based on the expert’s knowledge depending upon the visual inspec-tion comprising color,substance,coating,form,and motion of the tongue.But conventional tongue diagnosis has limitations since the procedure is inconsistent and subjective.Therefore,computer-aided tongue analyses have a greater potential to present objective and more consistent health assess-ments.This manuscript introduces a novel Simulated Annealing with Transfer Learning based Tongue Image Analysis for Disease Diagnosis(SADTL-TIADD)model.The presented SADTL-TIADD model initially pre-processes the tongue image to improve the quality.Next,the presented SADTL-TIADD technique employed an EfficientNet-based feature extractor to generate useful feature vectors.In turn,the SA with the ELM model enhances classification efficiency for disease detection and classification.The design of SA-based parameter tuning for heart disease diagnosis shows the novelty of the work.A wide-ranging set of simulations was performed to ensure the improved performance of the SADTL-TIADD algorithm.The experimental outcomes highlighted the superior of the presented SADTL-TIADD system over the compared methods with maximum accuracy of 99.30%.

Use of various CT imaging methods for diagnosis of acute ischemic cerebrovascular disease

Thirty-four patients with cerebral infarction and 18 patients with transient ischemic attack were examined by multi-slice spiral CT scan, CT perfusion imaging, and CT angiography within 6 hours after onset. By CT perfusion imaging, 29 cases in the cerebral infarction group and 10 cases in the transient ischemic attack group presented with abnormal blood flow perfusion, which corresponded to the clinical symptoms. By CT angiography, various degrees of vascular stenosis could be detected in 41 patients, including 33 in the cerebral infarction group and eight in the transient ischemic attack group. The incidence of intracranial artery stenosis was higher than that of extracranial artery stenosis. The intracranial artery stenosis was located predominantly in the middle cerebral artery and carotid artery siphon, while the extracranial artery stenosis occurred mainly in the bifurcation of the common carotid artery and the opening of the vertebral artery. There were 34 cases （83%） with convict vascular stenosis and perfusion abnormalities, and five cases （45%） with perfusion abnormalities but without convict vascular stenosis. The incidence of cerebral infarction in patients with National Institutes of Health Stroke Scale scores 〉 5 points during onset was significantly higher than that in patients with National Institutes of Health Stroke Scale scores 〈 5 points. These experimental findings indicate that the combined application of various CT imaging methods allows early diagnosis of acute ischemic cerebrovascular disease, which can comprehensively analyze the pathogenesis and severity of acute ischemic cerebrovascular disease at the morphological and functional levels.

Magnetic resonance imaging markers for early diagnosis of Parkinson's disease

Parkinson＇s disease （PD） is a neurodegenerative disorder characterized by selective and progressive degeneration, as well as loss of dopaminergic neurons in the substantia nigra. In PD, approximately 60-70% of nigrostriatal neurons are degenerated and 80% of content of the striatal dopamine is reduced before the diagnosis can be established according to widely accepted clinical diagnostic criteria. This condition describes a stage of disease called ＂prodromal＂, where non-motor symptoms, such as olfactory dysfunction, constipation, rapid eye movement behaviour disorder, depression, precede motor sign of PD. Detection of prodromal phase of PD is becoming an important goal for determining the prognosis and choosing a suitable treatment strategy. In this review, we present some non-invasive instrumental approaches that could be useful to identify patients in the prodromal phase of PD or in an early clinical phase, when the first motor symptoms begin to be apparent. Conventional magnetic resonance imaging （MRI） and advanced MRI techniques, such as magnetic resonance spectroscopy imaging, diffusion-weighted and diffusion tensor imaging and functional MRI, are useful to differentiate early PD with initial motor symptoms from atypical parkinsonian disorders, thus, making easier early diagnosis. Functional MRI and diffusion tensor imaging techniques can show abnormalities in the olfactory system in prodromal PD.

Clinical Effect of Magnetic Resonance Imaging and 64-slice Spiral CT in the Diagnosis of Ischemic Heart Disease Patients

Objective:To explore the clinical methods and clinical effects of applying magnetic resonance imaging(MRI)and 64・slice spiral computed tomography(CT)in the diagnosis of patients with ischemic heart disease.Methods:100 patients with ischemic heart disease were selected as the research objects.Selecting the patients from May 2020 to May 2021 as a sample,the patients were divided into two groups,and different diagnostic methods were used to compare the clinical diagnosis effects.Results:In terms of the diagnostic accuracy of the two groups of patients,the maximum value was 92.00%(experimental group)and the minimum value was 80.00%(control group).There was a big difference in data between the two groups,P<0.05,which was statistically significant.The patient9s(experimental group)diagnosis accuracy rate is highe Conclusion:In the process of research work for patients with ischemic heart disease,it is particularly important to diagnose the patients.The combined application of and 64-slice spiral CT can improve the clinical diagnosis efficiency and achieve significant results.

Applications of deep learning for detecting ophthalmic diseases with ultrawide-field fundus

AIM:To summarize the application of deep learning in detecting ophthalmic disease with ultrawide-field fundus images and analyze the advantages,limitations,and possible solutions common to all tasks.METHODS:We searched three academic databases,including PubMed,Web of Science,and Ovid,with the date of August 2022.We matched and screened according to the target keywords and publication year and retrieved a total of 4358 research papers according to the keywords,of which 23 studies were retrieved on applying deep learning in diagnosing ophthalmic disease with ultrawide-field images.RESULTS:Deep learning in ultrawide-field images can detect various ophthalmic diseases and achieve great performance,including diabetic retinopathy,glaucoma,age-related macular degeneration,retinal vein occlusions,retinal detachment,and other peripheral retinal diseases.Compared to fundus images,the ultrawide-field fundus scanning laser ophthalmoscopy enables the capture of the ocular fundus up to 200°in a single exposure,which can observe more areas of the retina.CONCLUSION:The combination of ultrawide-field fundus images and artificial intelligence will achieve great performance in diagnosing multiple ophthalmic diseases in the future.

Novel multi-parametric diagnosis of non-alcoholic fatty liver disease using ultrasonography,body mass index,and Fib-4 index

BACKGROUND Shear wave speed(SWS),shear wave dispersion(SWD),and attenuation imaging(ATI)are new diagnostic parameters for non-alcoholic fatty liver disease.To differentiate between non-alcoholic steatohepatitis(NASH)and non-alcoholic fatty liver(NAFL),we developed a clinical index we refer to as the“NASH pentagon”consisting of the 3 abovementioned parameters,body mass index(BMI),and Fib-4 index.AIM To investigate whether the area of the NASH pentagon we propose is useful in discriminating between NASH and NAFL.METHODS This non-invasive,prospective,observational study included patients diagnosed with fatty liver by abdominal ultrasound between September 2021 and August 2022 in whom shear wave elastography,SWD,and ATI were measured.Histological diagnosis based on liver biopsy was performed in 31 patients.The large pentagon group(LP group)and the small pentagon group(SP group),using an area of 100 as the cutoff,were compared;the NASH diagnosis rate was also investigated.In patients with a histologically confirmed diagnosis,receiveroperating characteristic(ROC)curve analyses were performed.RESULTS One hundred-seven patients(61 men,46 women;mean age 55.1 years;mean BMI 26.8 kg/m2)were assessed.The LP group was significantly older(mean age:60.8±15.2 years vs 46.4±13.2 years;P<0.0001).Twenty-five patients who underwent liver biopsies were diagnosed with NASH,and 6 were diagnosed with NAFL.On ROC curve analyses,the areas under the ROC curves for SWS,dispersion slope,ATI value,BMI,Fib-4 index,and the area of the NASH pentagon were 0.88000,0.82000,0.58730,0.63000,0.59333,and 0.93651,respectively;the largest was that for the area of the NASH pentagon.CONCLUSION The NASH pentagon area appears useful for discriminating between patients with NASH and those with NAFL.

Convolutional Neural Network-Based Classificationof Multiple Retinal Diseases Using Fundus Images

Use of deep learning algorithms for the investigation and analysis of medical images has emerged as a powerful technique.The increase in retinal dis-eases is alarming as it may lead to permanent blindness if left untreated.Automa-tion of the diagnosis process of retinal diseases not only assists ophthalmologists in correct decision-making but saves time also.Several researchers have worked on automated retinal disease classification but restricted either to hand-crafted fea-ture selection or binary classification.This paper presents a deep learning-based approach for the automated classification of multiple retinal diseases using fundus images.For this research,the data has been collected and combined from three distinct sources.The images are preprocessed for enhancing the details.Six layers of the convolutional neural network(CNN)are used for the automated feature extraction and classification of 20 retinal diseases.It is observed that the results are reliant on the number of classes.For binary classification(healthy vs.unhealthy),up to 100%accuracy has been achieved.When 16 classes are used(treating stages of a disease as a single class),93.3%accuracy,92%sensitivity and 93%specificity have been obtained respectively.For 20 classes(treating stages of the disease as separate classes),the accuracy,sensitivity and specificity have dropped to 92.4%,92%and 92%respectively.

Diagnostic performance of texture analysis in the differential diagnosis of perianal fistulising Crohn’s disease and glandular anal fistula

BACKGROUND Perianal fistulising Crohn's disease(PFCD)and glandular anal fistula have many similarities on conventional magnetic resonance imaging.However,many patients with PFCD show concomitant active proctitis,but only few patients with glandular anal fistula have active proctitis.AIM To explore the value of differential diagnosis of PFCD and glandular anal fistula by comparing the textural feature parameters of the rectum and anal canal in fat suppression T2-weighted imaging(FS-T2WI).METHODS Patients with rectal water sac implantation were screened from the first part of this study(48 patients with PFCD and 22 patients with glandular anal fistula).Open-source software ITK-SNAP(Version 3.6.0,http://www.itksnap.org/)was used to delineate the region of interest(ROI)of the entire rectum and anal canal wall on every axial section,and then the ROIs were input in the Analysis Kit software(version V3.0.0.R,GE Healthcare)to calculate the textural feature parameters.Textural feature parameter differences of the rectum and anal canal wall between the PFCD group vs the glandular anal fistula group were analyzed using Mann-Whitney U test.The redundant textural parameters were screened by bivariate Spearman correlation analysis,and binary logistic regression analysis was used to establish the model of textural feature parameters.Finally,diagnostic accuracy was assessed by receiver operating characteristic-area under the curve(AUC)analysis.RESULTS In all,385 textural parameters were obtained,including 37 parameters with statistically significant differences between the PFCD and glandular anal fistula groups.Then,16 texture feature parameters remained after bivariate Spearman correlation analysis,including one histogram parameter(Histogram energy);four grey level co-occurrence matrix(GLCM)parameters(GLCM energy_all direction_offset1_SD,GLCM entropy_all direction_offset4_SD,GLCM entropy_all direction_offset7_SD,and Haralick correlation_all direction_offset7_SD);four texture parameters(Correlation_all direction_offset1_SD,cluster prominence_angle 90_offset4,Inertia_all direction_offset7_SD,and cluster shade_angle 45_offset7);five grey level run-length matrix parameters(grey level nonuniformity_angle 90_offset1,grey level nonuniformity_all direction_offset4_SD,long run high grey level emphasis_all direction_offset1_SD,long run emphasis_all direction_offset4_SD,and long run high grey level emphasis_all direction_offset4_SD);and two form factor parameters(surface area and maximum 3D diameter).The AUC,sensitivity,and specificity of the model of textural feature parameters were 0.917,85.42%,and 86.36%,respectively.CONCLUSION The model of textural feature parameters showed good diagnostic performance for PFCD.The texture feature parameters of the rectum and anal canal in FS-T2WI are helpful to distinguish PFCD from glandular anal fistula.

Advanced diffusion magnetic resonance imaging in patients with Alzheimer’s and Parkinson’s diseases

The prevalence of neurodegenerative diseases is increasing as human longevity increases. The objective biomarkers that enable the staging and early diagnosis of neurodegenerative diseases are eagerly anticipated. It has recently become possible to determine pathological changes in the brain without autopsy with the advancement of diffusion magnetic resonance imaging techniques. Diffusion magnetic resonance imaging is a robust tool used to evaluate brain microstructural complexity and integrity, axonal order, density, and myelination via the micron-scale displacement of water molecules diffusing in tissues. Diffusion tensor imaging, a type of diffusion magnetic resonance imaging technique is widely utilized in clinical and research settings;however, it has several limitations. To overcome these limitations, cutting-edge diffusion magnetic resonance imaging techniques, such as diffusional kurtosis imaging, neurite orientation dispersion and density imaging, and free water imaging, have been recently proposed and applied to evaluate the pathology of neurodegenerative diseases. This review focused on the main applications, findings, and future directions of advanced diffusion magnetic resonance imaging techniques in patients with Alzheimer's and Parkinson's diseases, the first and second most common neurodegenerative diseases, respectively.

A multiple-tissue-specific magnetic resonance imaging model for diagnosing Parkinson’s disease: a brain radiomics study

Brain radiomics can reflect the characteristics of brain pathophysiology.However,the value of T1-weighted images,quantitative susceptibility mapping,and R2*mapping in the diagnosis of Parkinson’s disease(PD)was underestimated in previous studies.In this prospective study to establish a model for PD diagnosis based on brain imaging information,we collected high-resolution T1-weighted images,R2*mapping,and quantitative susceptibility imaging data from 171 patients with PD and 179 healthy controls recruited from August 2014 to August 2019.According to the inclusion time,123 PD patients and 121 healthy controls were assigned to train the diagnostic model,while the remaining 106 subjects were assigned to the external validation dataset.We extracted 1408 radiomics features,and then used data-driven feature selection to identify informative features that were significant for discriminating patients with PD from normal controls on the training dataset.The informative features so identified were then used to construct a diagnostic model for PD.The constructed model contained 36 informative radiomics features,mainly representing abnormal subcortical iron distribution(especially in the substantia nigra),structural disorganization(e.g.,in the inferior temporal,paracentral,precuneus,insula,and precentral gyri),and texture misalignment in the subcortical nuclei(e.g.,caudate,globus pallidus,and thalamus).The predictive accuracy of the established model was 81.1±8.0%in the training dataset.On the external validation dataset,the established model showed predictive accuracy of 78.5±2.1%.In the tests of identifying early and drug-naïve PD patients from healthy controls,the accuracies of the model constructed on the same 36 informative features were 80.3±7.1%and 79.1±6.5%,respectively,while the accuracies were 80.4±6.3%and 82.9±5.8%for diagnosing middle-to-late PD and those receiving drug management,respectively.The accuracies for predicting tremor-dominant and non-tremor-dominant PD were 79.8±6.9%and 79.1±6.5%,respectively.In conclusion,the multiple-tissue-specific brain radiomics model constructed from magnetic resonance imaging has the ability to discriminate PD and exhibits the advantages for improving PD diagnosis.

Deep Learning Based Automated Diagnosis of Skin Diseases Using Dermoscopy

Biomedical image analysis has been exploited considerably by recent technology involvements,carrying about a pattern shift towards‘automation’and‘error free diagnosis’classification methods with markedly improved accurate diagnosis productivity and cost effectiveness.This paper proposes an automated deep learning model to diagnose skin disease at an early stage by using Dermoscopy images.The proposed model has four convolutional layers,two maxpool layers,one fully connected layer and three dense layers.All the convolutional layers are using the kernel size of 3∗3 whereas the maxpool layer is using the kernel size of 2∗2.The dermoscopy images are taken from the HAM10000 dataset.The proposed model is compared with the three different models of ResNet that are ResNet18,ResNet50 and ResNet101.The models are simulated with 32 batch size and Adadelta optimizer.The proposed model has obtained the best accuracy value of 0.96 whereas the ResNet101 model has obtained 0.90,the ResNet50 has obtained 0.89 and the ResNet18 model has obtained value as 0.86.Therefore,features obtained from the proposed model are more capable for improving the classification performance of multiple skin disease classes.This model can be used for early diagnosis of skin disease and can also act as a second opinion tool for dermatologists.

Intelligent Deep Learning Based Disease Diagnosis Using Biomedical Tongue Images

The rapid development of biomedical imaging modalities led to its wide application in disease diagnosis.Tongue-based diagnostic procedures are proficient and non-invasive in nature to carry out secondary diagnostic processes ubiquitously.Traditionally,physicians examine the characteristics of tongue prior to decision-making.In this scenario,to get rid of qualitative aspects,tongue images can be quantitatively inspected for which a new disease diagnosis model is proposed.This model can reduce the physical harm made to the patients.Several tongue image analytical methodologies have been proposed earlier.However,there is a need exists to design an intelligent Deep Learning(DL)based disease diagnosis model.With this motivation,the current research article designs an Intelligent DL-basedDisease Diagnosis method using Biomedical Tongue Images called IDLDD-BTI model.The proposed IDLDD-BTI model incorporates Fuzzy-based Adaptive Median Filtering(FADM)technique for noise removal process.Besides,SqueezeNet model is employed as a feature extractor in which the hyperparameters of SqueezeNet are tuned using Oppositional Glowworm Swarm Optimization(OGSO)algorithm.At last,Weighted Extreme Learning Machine(WELM)classifier is applied to allocate proper class labels for input tongue color images.The design of OGSO algorithm for SqueezeNet model shows the novelty of the work.To assess the enhanced diagnostic performance of the presented IDLDD-BTI technique,a series of simulations was conducted on benchmark dataset and the results were examined in terms of several measures.The resultant experimental values highlighted the supremacy of IDLDD-BTI model over other state-of-the-art methods.

Classification Methods Based on Pattern Discrimination Models for Web-Based Diagnosis of Rice Diseases

Two classification and identification methods based on pattern discrimination models and the majority-vote technique were investigated for implementing a World Wide Web-based system for the identification of rice diseases. The experiment was carried out using color and shape patterns in 425 images of three rice diseases, which were classified into four classes： two classes of leaf blast, and one class each of sheath blight and brown spot. A method consisting of two discrimination steps involving application of multiple discrimination models of a support vector machine gave the best result because of its capacity to evaluate the similarity of disease types. This accuracy of the method was 88% for leaf blast （A-type）, 94% for sheath blight, and 80% for leaf blast （B-type） and brown spot; on average, the accuracy of this method was 5% greater than that of the other method when three classes were used in the model. Although the accuracy of both methods was inadequate, the results of this study show that it is possible to estimate the least number of possible or similar diseases from a large number of diseases. Therefore, we conclude that there is merit in grouping classes into subgroups rather than attempting to discriminate between all classes simultaneously and that these methods are effective in identifying diseases for web-based diagnosis.

Use of curcumin in diagnosis,prevention,and treatment of Alzheimer＇s disease

This review summarizes and describes the use of curcumin in diagnosis,prevention,and treatment of Alzheimer＇s disease.For diagnosis of Alzheimer＇s disease,amyloid-β and highly phosphorylated tau protein are the major biomarkers.Curcumin was developed as an early diagnostic probe based on its natural fluorescence and high binding affinity to amyloid-β.Because of its multi-target effects,curcumin has protective and preventive effects on many chronic diseases such as cerebrovascular disease,hypertension,and hyperlipidemia.For prevention and treatment of Alzheimer＇s disease,curcumin has been shown to effectively maintain the normal structure and function of cerebral vessels,mitochondria,and synapses,reduce risk factors for a variety of chronic diseases,and decrease the risk of Alzheimer＇s disease.The effect of curcumin on Alzheimer＇s disease involves multiple signaling pathways：anti-amyloid and metal iron chelating properties,antioxidation and anti-inflammatory activities.Indeed,there is a scientific basis for the rational application of curcumin in prevention and treatment of Alzheimer＇s disease.

An Image-Based Diagnostic Expert System for Corn Diseases

The annual worldwide yield losses due to pests are estimated to be billions of dollars. Integrated pest management （IPM） is one of the most important components of crop production in most agricultural areas of the world, and the effectiveness of crop protection depends on accurate and timely diagnosis of phytosanitary problems. Accurately identifying and treatment depends on the method which used in disease and insect pests diagnosis. Identifying plant diseases is usually difficult and requires a plant pathologist or well-trained technician to accurately describe the case. Moreover, quite a few diseases have similar symptoms making it difficult for non-experts to distinguish disease correctly. Another method of diagnosis depends on comparison of the concerned case with similar ones through one image or more of the symptoms and helps enormously in overcoming difficulties of non-experts. The old adage ＇a picture is worth a thousand words＇ is crucially relevant. Considering the user＇s capability to deal and interact with the expert system easily and clearly, a webbased diagnostic expert-system shell based on production rules （i.e., IF 〈 effects 〉 THEN 〈 causes 〉） and frames with a color image database was developed and applied to corn disease diagnosis as a case study. The expert-system shell was made on a 32-bit multimedia desktop microcomputer. The knowledge base had frames, production rules and synonym words as the result of interview and arrangement. It was desired that 80% of total frames used visual color image data to explain the meaning of observations and conclusions. Visual color image displays with the phrases of questions and answers from the expert system, enables users to identify any disease, makes the right decision, and chooses the right treatment. This may increase their level of understanding of corn disease diagnosis. The expert system can be applied to diagnosis of other plant pests or diseases by easy changes to the knowledge base.

Nonalcoholic fatty liver disease:Updates in noninvasive diagnosis and correlation with cardiovascular disease

Nonalcoholic fatty liver disease(NAFLD)refers to the accumulation of fat(mainly triglycerides)within hepatocytes.Approximately 20%-30%of adults in the general population in developed countries have NAFLD;this trend is increasing because of the pandemicity of obesity and diabetes,and is becoming a serious public health burden.Twenty percent of individuals with NAFLD develop chronic hepatic inflammation[nonalcoholic steatohepatitis(NASH)],which can be associated with the development of cirrhosis,portal hypertension,and hepatocellular carcinoma in a minority of patients.And thus,the detection and diagnosis of NAFLD is important for general practitioners.Liver biopsy is the gold standard for diagnosing NAFLD and confirming the presence of NASH.However,the invasiveness of this procedure limits its application to screening the general population or patients with contraindications for liver biopsy.The development of noninvasive diagnostic methods for NAFLD is of paramount importance.This review focuses on the updates of noninvasive diagnosis of NAFLD.Besides,we review clinical evidence supporting a strong association between NAFLD and the risk of cardiovascular disease because of the cross link between these two disorders.

Magnetic resonance imaging may predict deep remission in patients with perianal fistulizing Crohn's disease

To evaluate the imaging course of Crohn’s disease (CD) patients with perianal fistulas on long-term maintenance anti-tumor necrosis factor (TNF)-α therapy and identify predictors of deep remission.METHODSAll patients with perianal CD treated with anti-TNF-α therapy at our tertiary care center were evaluated by magnetic resonance imaging (MRI) and clinical assessment. Two MR examinations were performed: at initiation of anti-TNF-α treatment and then at least 2 years after. Clinical assessment (remission, response and non-response) was based on Present’s criteria. Rectoscopic patterns, MRI Van Assche score, and MRI fistula activity signs (T2 signal and contrast enhancement) were collected for the two MR examinations. Fistula healing was defined as the absence of T2 hyperintensity and contrast enhancement on MRI. Deep remission was defined as the association of both clinical remission, absence of anal canal ulcers and healing on MRI. Characteristics and imaging patterns of patients with and without deep remission were compared by univariate and multivariate analyses.RESULTSForty-nine consecutive patients (31 females and 18 males) were included. They ranged in age from 14-70 years (mean, 33 years). MRI and clinical assessment were performed after a mean period of exposure to anti-TNF-α therapy of 40 ± 3.7 mo. Clinical remission, response and non-response were observed in 53.1%, 20.4%, and 26.5% of patients, respectively. Deep remission was observed in 32.7% of patients. Among the 26 patients in clinical remission, 10 had persisting inflammation of fistulas on MRI (T2 hyperintensity, n = 7; contrast enhancement, n = 10). Univariate analysis showed that deep remission was associated with the absence of rectal involvement and the absence of switch of anti-TNF-α treatment or surgery requirement. Multivariate analysis demonstrated that only the absence of rectal involvement (OR = 4.6; 95%CI: 1.03-20.5) was associated with deep remission.CONCLUSIONDeep remission is achieved in approximately one third of patients on maintenance anti-TNF-α therapy. Absence of rectal involvement is predictive of deep remission.

Reversible lesions in the brain parenchyma in Wilson's disease confirmed by magnetic resonance imaging:earlier administration of chelating therapy can reduce the damage to the brain

The aim of this study was to evaluate the resolution of brain lesions in patients with Wilson’s disease during the long-term chelating therapy using magnetic resonance imaging and a possible signiifcance of the time latency between the initial symptoms of the disease and the introduction of this therapy. Initial magnetic resonance examination was performed in 37 patients with proven neurological form of Wilson’s disease with cerebellar, parkinsonian and dystonic presentation. Magnetic resonance reexamination was done 5.7 ± 1.3 years later in 14 patients. Patients were divided into： group A, where chelating therapy was initiated 〈 24 months from the ifrst symp-toms and group B, where the therapy started≥ 24 months after the initial symptoms. Symmetry of the lesions was seen in 100% of patients. There was a signiifcant difference between groups A and B regarding complete resolution of brain stem and putaminal lesions （P= 0.005 andP=0.024, respectively）. If the correct diagnosis and adequate treatment are not established less than 24 months after onset of the symptoms, irreversible lesions in the brain parenchyma could be ex-pected. Signal abnormalities on magnetic resonance imaging might therefore, at least in the early stages, represent reversible myelinolisis or cytotoxic edema associated with copper toxicity.