• Crohn's disease, pneumothorax
• Hydrocephalus
• Mycobacterium Bovis (M. Bovis)
• Mycobacterium Tuberculosis (M. TB)

• Crohn's disease
• artificial intelligence
• diagnose
• gastrointestinal tuberculosis

• Humans
• Tuberculosis, Gastrointestinal/diagnosis
• Crohn Disease/diagnosis
• Diagnosis, Differential
• Artificial Intelligence
• Colonoscopy/methods
• Female
• Male
• Adult
• Sensitivity and Specificity
• Middle Aged
• Young Adult

• anti-inflammatory
• anti-tubercular therapy
• crohn’s disease
• endoscopic follow up
• indeterminate lesions
• india
• intestinal stricture
• tuberculosis

• Children
• Cryptogenic multifocal ulcerous stenosing enteritis (CMUSE)
• Obscure gastrointestinal bleeding
• SLCO2A1
• Small bowel stricture
• Small bowel ulcers

• Humans
• Tuberculosis, Gastrointestinal/diagnosis
• Tuberculosis, Gastrointestinal/complications
• Crohn Disease/complications
• Crohn Disease/diagnosis
• Retrospective Studies
• Female
• Male
• Child
• Enteritis/complications
• Enteritis/diagnosis
• Enteritis/pathology
• Intestine, Small/pathology
• Constriction, Pathologic/etiology
• Diagnosis, Differential
• Ulcer/etiology
• Ulcer/pathology
• Adolescent
• Intestinal Obstruction/etiology
• Intestinal Obstruction/surgery
• Child, Preschool
• Capsule Endoscopy

• Core gut microbiome
• Crohn’s disease
• Dysbiosis
• Gut inflammatory disorders
• Gut microbiome
• Intestinal tuberculosis
• Machine learning
• Microbiome meta-network analysis
• Mycobiome

• 55/4/11/CARE-ID/2018-NCD-II Indian Council of Medical Research

• Humans
• Crohn Disease/diagnosis
• Tuberculosis, Gastrointestinal/diagnosis
• Diagnosis, Differential
• Gastrointestinal Microbiome

• Crohn's disease
• immunosuppressive therapy
• intestinal tuberculosis
• steroids

• Humans
• Crohn Disease/diagnosis
• Diagnosis, Differential
• Diagnostic Errors
• Myanmar/epidemiology
• Tuberculosis, Gastrointestinal/diagnosis
• Tuberculosis, Gastrointestinal/epidemiology

• artificial intelligence
• crohn’s disease
• intestinal tuberculosis
• machine learning
• multidisciplinary team

• Hunan Provincial Innovation Foundation for Postgraduates

• Crohn’s disease
• Differential diagnosis
• Inflammatory bowel disease
• Infrared spectroscopy
• Intestinal tuberculosis
• Machine learning

• Humans
• Crohn Disease/diagnosis
• Crohn Disease/pathology
• Spectroscopy, Fourier Transform Infrared
• Diagnosis, Differential
• Paraffin
• Tuberculosis, Gastrointestinal/diagnosis
• Tuberculosis, Gastrointestinal/pathology
• Enteritis/diagnosis
• Machine Learning
• Ataxia Telangiectasia Mutated Proteins

• Crohn’s disease
• barriers
• inflammatory bowel disease
• ulcerative colitis

• Endoscopy
• Ileal diseases
• Prognosis

• Humans
• Retrospective Studies
• Ulcer/diagnosis
• Intestinal Diseases/diagnosis
• Colonoscopy
• Prognosis
• Disease Progression

• Abdominal tuberculosis
• Colonoscopy
• Computed tomography
• Histopathology
• Inflammatory bowel disease
• Intestinal tuberculosis
• Xpert Mtb/Rif

• Humans
• Male
• Middle Aged
• BCG Vaccine/adverse effects
• Colitis, Ulcerative/drug therapy
• Urinary Bladder Neoplasms/drug therapy
• Neoplasm Recurrence, Local

China is a region with a high incidence of tuberculosis, and the incidence of IBD has also been rising rapidly in recent years. Differentiating Crohn’s disease(CD) from intestinal tuberculosis (ITB) has become a very challenging issue. We aimed to develop and assess a diagnostic nomogram to differentiate between CD and ITB to improve the accuracy and practicability of the model.

A total of 133 patients (CD 90 and ITB 43) were analyzed retrospectively. Univariate and multivariate logistic regression analysis was included to determine the independent predictive factors and establish the regression equation. On this basis, the nomogram prediction model was constructed. The discrimination, calibration and clinical efficiency of the nomogram were assessed using area under the curve(AUC), C-index, calibration curve, decision curve analysis (DCA) and clinical impact curve.

T-SPOT positive, cobblestone appearance, comb sign and granuloma were significant predictors in differentiating CD from ITB. Base on the above independent predictors, a diagnostic nomogram was successfully established. The sensitivity, specificity, accuracy of the prediction model are 94.4%, 93.0%, 94.0% respectively. The AUC and the C-index of the prediction model are both 0.988, which suggest that the model had a good discrimination power. The calibration curve indicated a high calibration degree of the prediction model. The DCA and clinical impact curve indicated a good clinical efficiency of the prediction model which could bring clinical benefits.

A nomogram prediction model for distinguishing CD from ITB was developed and assessed, with high discrimination, calibration and clinical efficiency. It can be used as an accurate and convenient diagnostic tool to distinguish CD from ITB, facilitating clinical decision-making.

The online version contains supplementary material available at 10.1186/s12876-022-02519-z.

• antegrade and retrograde enteroscopy
• balloon‐assisted enteroscopy
• pan‐enteroscopy
• power spiral enteroscopy
• small‐bowel diseases

Differential diagnosis of Crohn’s disease (CD) and ulcerative primary intestinal lymphoma (UPIL) is a tough problem in clinical practice.

Our study identified key differences between CD and UPIL patients and aimed to further establish a scoring model for differential diagnosis.

A total of 91 CD and 50 UPIL patients from 9 tertiary inflammatory bowel disease centers were included. Univariate and multivariate analyses were used to determine significant markers for differentiating CD and UPIL. A differential scoring model was established by logistic regression analysis.

The differential model was based on clinical symptoms, endoscopic and imaging features that were assigned different scores: intestinal bleeding (−2 points), extraintestinal manifestation (2 points), segmental lesions (1 point), cobblestone sign (2 points), homogeneous enhancement (−1 point), mild enhancement (−1 point), engorged vasa recta (1 point). A total score of ≥1 point indicates CD, otherwise UPIL was indicated. This model produced an accuracy of 83.66% and an area under the ROC curve of 0.947. The area under the ROC curve for validation using the 10-fold validation method was 0.901.

This study provided a convenient and useful model to differentiate CD from UPIL.

• Crohn’s disease
• DNA methylation
• Differentially methylated CpG sites
• Intestinal tuberculosis

• Biomarkers
• Crohn Disease/diagnosis
• Crohn Disease/genetics
• DNA
• DNA Methylation/genetics
• Enteritis
• Genome-Wide Association Study
• Humans
• Tuberculosis, Gastrointestinal/diagnosis
• Tuberculosis, Gastrointestinal/genetics

• Core indicators
• Inflammatory bowel disease
• Inflammatory bowel disease quality care evaluation center
• Quality of care

• Crohn’s disease
• Inflammatory bowel diseases
• bacterial infections
• diagnostic imaging
• diverticulosis
• drug-induced abnormalities
• endoscopy
• fungal infections
• immune checkpoint inhibitors
• ischaemic colitis
• parasitic infections
• pathology
• radiation-induced abnormalities
• tuberculosis
• ulcerative colitis
• vasculitis
• viral infections

Differentiation between Crohn’s disease and intestinal tuberculosis is difficult but crucial for medical decisions. This study aims to develop an effective framework to distinguish these two diseases through an explainable machine learning (ML) model. After feature selection, a total of nine variables are extracted, including intestinal surgery, abdominal, bloody stool, PPD, knot, ESAT-6, CFP-10, intestinal dilatation and comb sign. Besides, we compared the predictive performance of the ML methods with traditional statistical methods. This work also provides insights into the ML model’s outcome through the SHAP method for the first time. A cohort consisting of 200 patients’ data (CD = 160, ITB = 40) is used in training and validating models. Results illustrate that the XGBoost algorithm outperforms other classifiers in terms of area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision and Matthews correlation coefficient (MCC), yielding values of 0.891, 0.813, 0.969, 0.867 and 0.801 respectively. More importantly, the prediction outcomes of XGBoost can be effectively explained through the SHAP method. The proposed framework proves that the effectiveness of distinguishing CD from ITB through interpretable machine learning, which can obtain a global explanation but also an explanation for individual patients.

To establish a clinical radiomics nomogram to differentiate Crohn’s disease (CD) from intestinal tuberculosis (ITB).

Ninety-three patients with CD and 67 patients with ITB were recruited (111 in training cohort and 49 in test cohort). The region of interest (ROI) for the lesions in the ileocecal region was delineated on computed tomography enterography and radiomics features extracted. Radiomics features were filtered by the gradient boosting decision tree (GBDT), and a radiomics score was calculated by using the radiomics signature-based formula. We constructed a clinical radiomics model and nomogram combining clinical factors and radiomics score through multivariate logistic regression analysis, and the internal validation was undertaken by ten-fold cross validation. Analyses of receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were used to evaluate the prediction performance. DeLong test was applied to evaluate the performance of the clinical, radiomics and combined model.

The clinical radiomics nomogram, which was based on the 9 radiomics signature and two clinical factors, indicated that the clinical radiomics model had an area under the ROC curve (AUC) value of 0.96 (95% confidence interval [CI]: 0.93–0.99) in the training cohort and 0.93 (95% CI: 0.86–1.00) in validation cohort. The clinical radiomics model was superior to the clinical model and radiomics model, and the difference was significant (P = 0.006, 0.004) in the training cohort. DCA confirmed the clinical utility of clinical radiomics nomogram.

CTE-based radiomics model has a good performance in distinguishing CD from ITB. A nomogram constructed by combining radiomics and clinical factors can help clinicians accurately diagnose and select appropriate treatment strategies between CD and ITB.

• Crohn’s disease
• computed tomography
• intestinal tuberculosis
• nomogram
• radiomics

• Crohn’s disease
• TB culture
• Xpert MTB/RIF
• colonoscopy
• histopathology
• intestinal tuberculosis

Differentiation of Crohn’s disease (CD) from intestinal tuberculosis (ITB) is a big challenge to gastroenterologists because of their indistinguishable features and insensitive diagnostic tools. A non-invasive biomarker is urgently required to distinguish ITB/CD patients particularly in India, a TB endemic region, where CD frequency is increasing rapidly due to urbanization. Among the three differentially expressed miRNAs obtained from small RNA transcriptomic profiling of ileocaecal/terminal ileal tissue of ITB/CD patients (n = 3), only two down-regulated miRNAs, miR-31-5p, and miR-215-5p showed comparable data in qRT-PCR. Out of which, only miR-215-5p was detectable in the patient’s plasma, but there was no significant difference in expression between ITB/CD. On the other hand, miR-375-3p, the pulmonary TB specific marker was found in higher amount in the plasma of ITB patients than CD while reverse expression was observed in the ileocaecal/terminal ileal tissues of the same patients. Next, using Bioplex pro-human cytokine 48-plex screening panel, only three chemokines, Eotaxin-1/CCL11, SDF-1α/CXCL12, and G-CSF have noted significantly different levels in the serum of ITB/CD patients. ROC analysis has revealed that compared to a single molecule, a combination of miR-375-3p + Eotaxin-1/CCL11 + SDF-1α /CXCL12 + G-CSF showed a better AUC of 0.83, 95% CI (0.69–0.96) with 100% specificity and positive predictive value while sensitivity, negative predictive value, and accuracy were 56%, 69%, and 78% respectively in distinguishing ITB from CD. This study suggests that a combination of plasma markers shows better potential in differentiating ITB from CD than a single marker and this panel of markers may be used for clinical management of ITB/CD patients.

The diagnosis of intestinal tuberculosis (ITB) is often challenging. Therapeutic anti-tubercular trial (TATT) is sometimes used for the diagnosis of ITB. We aimed to evaluate the changing pattern of fecal calprotectin (FC) levels during TATT in patients with ITB.

A retrospective review was performed on the data of 39 patients who underwent TATT between September 2015 and November 2018 in five university hospitals in Daegu, South Korea. The analysis was performed for 33 patients with serial FC measurement reports.

The mean age of the participants was 48.8 years. The final diagnosis of ITB was confirmed in 30 patients based on complete mucosal healing on follow-up colonoscopy performed after 2 months of TATT. Before starting TATT, the mean FC level of the ITB patients was 170.2 μg/g (range, 11.5-646.5). It dropped to 25.4 μg/g (range, 11.5-75.3) and then 23.3 μg/g (range, 11.5-172.2) after one and two months of TATT, respectively. The difference in mean FC before and one month after TATT was statistically significant (p<0.001), and FC levels decreased to below 100 μg/g in all patients after one month of TATT.

All ITB patients showed FC decline after only 1 month of TATT, and this finding correlated with complete mucosal healing in the follow-up colonoscopy after 2 months of TATT.

• Diagnosis
• Fecal calprotectin
• Intestinal tuberculosis
• Therapeutic anti-tubercular trial

• Behçet's disease
• Crohn's disease
• deep learning
• intestinal tuberculosis

Sometimes in clinical practice, it is a great challenge to distinguish Crohn's disease (CD) and intestinal tuberculosis (ITB), we conducted this study to identify simple and useful algorithm for distinguishing them.

We retrospectively reviewed the medical history of the patients who were diagnosed as ITB or CD. We firstly identified ITB patients, and then the patients diagnosed with CD were matched by age, sex, and admission time in a 1:1 ratio. Patients who admitted between May 1, 2013 and April 30, 2019 were regarded as training cohort, and patients admitted between May 1, 2019 and May 1, 2020 were regarded as validation cohort. We used multivariate analysis to identify the potential variables, and then we used R package rpart to build the classification and regression tree (CART), and validated the newly developed model.

In total, the training cohort included 84 ITB and 84 CD patients, the validation cohort included 22 ITB and 22 CD patients. Multivariate analysis showed that, positive interferon-gamma release assays (IGRAs), ≥ 4 segments involved, longitudinal ulcer, circular ulcer, and aphthous ulcer were confirmed as independent discriminating factors. Using these parameters to build the CART model made an overall accuracy rate was 88.64%, with sensitivity, specificity, NPV, and PPV being 90.91%, 86.36%, 90.48% and 86.96%, respectively.

We developed a simple and novel algorithm model covering laboratory, imaging, and endoscopy parameters with CART to differentiate ITB and CD with good accuracy. Positive IGRAs and circular ulcer were suggestive of ITB, while ≥ 4 segments involved, longitudinal ulcer, and aphthous ulcer were suggestive of CD.

The online version contains supplementary material available at 10.1186/s12876-021-01838-x.

• Algorithm
• CART
• Crohn's disease
• Diagnosis
• Intestinal tuberculosis

• Abdominal tuberculosis
• clinical features
• diagnosis
• extrapulmonary tuberculosis
• gastrointestinal tuberculosis
• pathogenesis
• risk factors
• treatment
• tuberculosis

Diagnosing intestinal tuberculosis (TB) with uncommon clinical manifestations is often challenging. Here, we report a case of an alcoholic patient who presented with vague symptoms and was later diagnosed with intestinal TB. This patient experienced multiorgan failure causing hemodynamic instability requiring ionotropic support; acute hypoxic respiratory failure managed with non-invasive positive pressure ventilation, hepatic failure, transudative peritoneal effusion, and transudative pleural effusion. These conditions clouded our judgment to pursue colonoscopy for a definite diagnosis and delayed the anti-tuberculosis treatment. When intestinal tuberculosis TB is suspected, the differential diagnosis must be established with other gastrointestinal involving diseases, including mycobacterium avium complex (MAC) and Crohn's disease (CD). MAC can show overlapping features with intestinal TB or coexist with it; Acid-fast stain and tissue culture are the key tests to differentiate these two. In the presence of diagnostic uncertainty between intestinal TB and CD, a therapeutic trial with anti-tuberculous therapy may be warranted.

• abdominal tb
• abdominal tuberculosis
• intestinal tb
• intestinal tuberculosis
• mac
• mtb
• mycobacterium
• mycobacterium tuberculosis
• tb
• tuberculosis

Isoniazid (INH) is highly bactericidal against replicating tubercule bacilli and is involved in all antituberculous chemotherapeutic regimens. Several neurological adverse effects, following both therapeutic and overdose use of INH, have been reported in adults in the literature. Here, we present a case of a 5-year-old girl with intestinal Tuberculosis, who developed hemiclonic seizure as a side effect of INH therapeutic dose after 2 weeks of tuberculosis therapy.

• gastroenterology and hepatology
• infectious diseases and tropical medicine
• pediatrics

• Asia/epidemiology
• Humans
• Inflammatory Bowel Diseases/diagnosis
• Inflammatory Bowel Diseases/epidemiology
• Inflammatory Bowel Diseases/therapy
• Medically Underserved Area
• Resource Allocation

• Peritoneal tuberculosis
• cocoon
• hepatic tuberculosis
• intestinal tuberculosis
• pancreatic tuberculosis
• strictures
• ulcer healing.

• Crohn’s disease
• Montreal classification
• antitubercular therapy
• natural history

• behavioral progression
• bowel surgery
• crohn disease
• hospital-based cohort
• immunomodulators
• thailand

• Adult
• Crohn Disease/diagnosis
• Crohn Disease/drug therapy
• Crohn Disease/epidemiology
• Crohn Disease/surgery
• Digestive System Surgical Procedures/statistics & numerical data
• Female
• Hospitalization/statistics & numerical data
• Humans
• Immunosuppressive Agents/therapeutic use
• Male
• Mercaptopurine/therapeutic use
• Middle Aged
• Retrospective Studies
• Thailand/epidemiology

• Anti-tubercular therapy
• Apoptosis
• CD4+ cells
• Chronic diarrhea
• Crohn’s disease
• Differential diagnosis
• Ileo-cecal tuberculosis
• Inflammatory bowel disease
• Intestinal tuberculosis
• Mycobacteriumtuberculosis

Crohn’s disease (CD) and intestinal tuberculosis (ITB) remain “difficult-to-differentiate” diseases. We have previously documented peripheral blood frequency of CD4⁺ CD25⁺ FOXP3⁺ T-regulatory cells (Treg) as a biomarker to differentiate CD and ITB. We tried to validate these results in a larger cohort of CD and ITB patients.

Seventy treatment naïve patients of CD (n = 23) and ITB (n = 47) (diagnosed by standard criteria) were recruited prospectively from October 2016 to May 2017. Patients with history of antitubercular therapy in the past were excluded. The frequency of Treg cells in peripheral blood was determined by flow cytometry, and compared between CD and ITB patients.

Similar to our previous study, frequency of Treg cells in peripheral blood was significantly increased in ITB as compared to CD patients (40.9 [interquartile range, 33–50] vs. 24.9 [interquartile range, 14.4–29.6], P<0.001). Further, the receiver operating characteristics curve also showed good diagnostic accuracy with an area under the curve (AUC) of 0.77 (95% confidence interval, 0.65–0.89) and a FOXP3⁺ cutoff value of > 31.3% had a sensitivity and specificity of 83% and 82.6% respectively, to differentiate ITB from CD. Even for the indeterminate cases (n = 33), Treg cell frequency had similar diagnostic accuracy with an AUC of 0.85 (95% confidence interval, 0.68–0.95) and a cutoff of 32.37% had sensitivity and specificity of 87% and 95% respectively, to differentiate ITB from CD.

The current findings validate that the increased frequency of CD4⁺ CD25⁺ FOXP3⁺ Treg in the peripheral blood can be used as a biomarker with high diagnostic accuracy to differentiate ITB from CD.

• Crohn disease
• FOXP3
• Intestinal tuberculosis
• Regulatory T cells

Differentiating Crohn’s disease (CD) from intestinal tuberculosis (ITB) is a diagnostic dilemma, particularly in regions where ITB is prevalent and CD incidence is increasing, because both diseases can present quite similarly, and diagnostic tests to identify Mycobacterium tuberculosis in tissue samples have rather poor sensitivity. Studies that were conducted to determine the factors that differentiate CD from ITB identified some significant characteristics, but none of those characteristics are exclusive to either ITB or CD. Many diagnostic models or scoring systems that use one to several diagnostic parameters have been proposed to help distinguish these two intestinal diseases. Early models consisted of parameters common to routine clinical practice, such as clinical features, and endoscopic and pathologic findings. The later models also include more advanced diagnostic parameters like high-resolution imaging and serological testing. However, the number and types of parameters differ among diagnostic models, and the systems used to calculate scoring also vary from model to model. Enhanced awareness and understanding of the currently available diagnostic models will help physicians determine which model(s) is/are most suitable for differentiating CD from ITB in their clinical practice.

• Crohn disease
• Diagnosis
• Intestinal tuberculosis

• Diarrhea
• acute
• antibiotics
• antimicrobial
• bacteria
• c. difficile
• dysentery
• infection
• infectious
• microbiota
• protozoa
• rehydration
• stool
• virus

• Adult
• Anti-Bacterial Agents/therapeutic use
• Clostridioides difficile
• Diarrhea/drug therapy
• Diarrhea/therapy
• Dysentery/drug therapy
• Humans
• Travel

• crohn disease
• differentiation
• intestinal tuberculosis
• primary intestinal lymphoma
• proteome

• Adult
• Crohn Disease/diagnosis
• Diagnosis, Differential
• Female
• Follow-Up Studies
• Humans
• Intestinal Neoplasms/blood
• Intestinal Neoplasms/diagnosis
• Lymphoma/blood
• Lymphoma/diagnosis
• Male
• Mass Spectrometry
• Pilot Projects
• Proteome/analysis
• Proteomics/methods
• Retrospective Studies
• Tuberculosis, Gastrointestinal/blood
• Tuberculosis, Gastrointestinal/diagnosis

• 5-ASA
• 5-aminosalicylate
• CMV
• Crohn’s disease, CD
• JAK
• Ulcerative colitis, UC
• adalimumab
• adherence
• adolescent
• adult
• anaemia
• anti-TNF
• anti-drug antibodies
• antibiotic
• antibiotics
• azathioprine
• biomarker
• breastfeeding
• budesonide
• calprotectin
• cancer
• care
• chemoprevention
• ciclosporin
• classification
• clostridium difficile
• cognitive behavioural therapy, CBT
• colectomy
• colitis
• colonoscopy
• computerised tomography, CT
• corticosteroid
• cyclosporine
• cytomegalovirus
• diagnosis
• diet
• endoscopic
• endoscopy
• enema
• faecal microbial transplant
• fatigue
• fetus
• fistula
• granuloma
• guideline
• hydrocortisone
• ileitis
• inflammatory bowel disease, IBD
• infliximab
• integrin
• janus kinase inhibator
• magnetic resonance, MR
• management
• mercaptopurine
• mesalazine
• methylprednisolone
• monitoring
• multidisciplinary team, MDT
• nutrition
• perianal
• pouch
• pouchitis
• prednisolone
• pregnancy
• primary stress
• probiotic
• psychology
• psychotherapy
• self-management
• shared care
• sigmoidoscopy
• smoking
• stress
• stricture
• suppository
• surgery
• surveillance
• telephone clinic
• therapeutic drug monitoring
• therapy
• thiopurine
• tofacitinib
• tuberculosis, TB
• ultrasound
• ustekinumab
• vaccination
• vaccine
• vedolizumab
• virtual clinic
• vitamin D