Artificial Intelligence for Disease Assessment in Inflammatory Bowel Disease: How Will it Change Our Practice?

Clinical usefulness of hypoxia imaging colonoscopy for the objective measurement of ulcerative colitis disease activity

BACKGROUND AND AIMS

Colonic mucosal hypoxia is associated with mucosal inflammation in ulcerative colitis (UC). We aimed to assess the clinical usefulness of hypoxia imaging colonoscopy for the evaluation of clinical, endoscopic, and histologic disease activities of UC.

METHODS

This retrospective cohort study comprised 100 consecutive patients with UC who underwent hypoxia imaging colonoscopy between September 2022 and September 2023 at the University of Tsukuba Hospital. Colonic tissue oxygen saturation (StO2) was measured at the biopsy sites, and StO2 values between different disease activities were compared. Receiver-operating characteristic (ROC) analysis was used to calculate the area under the ROC curve (AUROC).

RESULTS

A significant correlation was identified between rectal StO2 and the Simple Clinical Colitis Activity Index, with moderate accuracy to predict bowel urgency at a 40.5% cutoff (AUROC, .74; 95% confidence interval [CI], .62-.87). Our analysis of 490 images showed median StO2 values for Mayo endoscopic subscores 0, 1, 2, and 3 as 52% (interquartile range [IQR], 48%-56%), 47% (IQR, 43%-52%), 42% (IQR, 38.8%-47%), and 39.5% (IQR, 37.3%-41.8%), respectively. Differences for all pairs were significant. Median StO2 was 49% (IQR, 44%-54%) for Geboes scores 0 to 2, significantly higher than histologically active disease (Geboes score ≥3). At a colonic StO2 cutoff of 45.5%, AUROCs for endoscopically and histologically active diseases were .79 (95% CI, .74-.84) and .72 (95% CI, .66-.77).

CONCLUSIONS

StO2 obtained by hypoxia imaging colonoscopy is useful for assessing clinical, endoscopic, and histologic activities of UC, suggesting that StO2 may be a novel and objective endoscopic measurement.

Artificial intelligence and endo-histo-omics: new dimensions of precision endoscopy and histology in inflammatory bowel disease

Integrating artificial intelligence into inflammatory bowel disease (IBD) has the potential to revolutionise clinical practice and research. Artificial intelligence harnesses advanced algorithms to deliver accurate assessments of IBD endoscopy and histology, offering precise evaluations of disease activity, standardised scoring, and outcome prediction. Furthermore, artificial intelligence offers the potential for a holistic endo-histo-omics approach by interlacing and harmonising endoscopy, histology, and omics data towards precision medicine. The emerging applications of artificial intelligence could pave the way for personalised medicine in IBD, offering patient stratification for the most beneficial therapy with minimal risk. Although artificial intelligence holds promise, challenges remain, including data quality, standardisation, reproducibility, scarcity of randomised controlled trials, clinical implementation, ethical concerns, legal liability, and regulatory issues. The development of standardised guidelines and interdisciplinary collaboration, including policy makers and regulatory agencies, is crucial for addressing these challenges and advancing artificial intelligence in IBD clinical practice and trials.

Artificial intelligence-assisted colonoscopy to identify histologic remission and predict the outcomes of patients with ulcerative colitis: A systematic review

This systematic review evaluated the current status of AI-assisted colonoscopy to identify histologic remission and predict the clinical outcomes of patients with ulcerative colitis. The use of artificial intelligence (AI) has increased substantially across several medical fields, including gastrointestinal endoscopy. Evidence suggests that it may be helpful to predict histologic remission and relapse, which would be beneficial because current histological diagnosis is limited by the inconvenience of obtaining biopsies and the high cost and time-intensiveness of pathological diagnosis. MEDLINE and the Cochrane Central Register of Controlled Trials were searched for studies published between January 1, 2000, and October 31, 2023. Nine studies fulfilled the selection criteria and were included; five evaluated the prediction of histologic remission, two assessed the prediction of clinical outcomes, and two evaluated both. Seven were prospective observational or cohort studies, while two were retrospective observational studies. No randomized controlled trials were identified. AI-assisted colonoscopy demonstrated sensitivity between 65 %-98 % and specificity values of 80 %-97 % for identifying histologic remission. Furthermore, it was able to predict future relapse in patients with ulcerative colitis. However, several challenges and barriers still exist to its routine clinical application, which should be overcome before the true potential of AI-assisted colonoscopy can be fully realized.

Remote Between Visit Monitoring in Inflammatory Bowel Disease Care: A Qualitative Study of CAPTURE-IBD Participants and Care Team Members

Introduction

We recently showed that CAPTURE-inflammatory bowel disease (IBD)-a care coordination intervention comprised of routine remote monitoring of patient-reported outcomes (PRO) and a care coordinator-triggered care pathway-was more effective at reducing symptom burden for patients with IBD compared to usual care. We aimed to understand how patients and care team providers experienced the intervention and evaluate purported mechanisms of action to plan for future implementation.

Methods

In this study, 205 patients were randomized to CAPTURE-IBD (n = 100) or usual care(n = 105). We conducted semi-structured interviews with 16 of the 100 participants in the CAPTURE-IBD arm and 5 care team providers to achieve thematic saturation. We used qualitative rapid analysis to generate a broad understanding of experiences, perceived impact, the coordinator role, and suggested improvements.

Results

Findings highlight that the intervention was acceptable and user-friendly, despite concerns regarding increased nursing workload. Both participants and care team providers perceived the intervention as valuable in supporting symptom monitoring, psychosocial care, and between-visit action plans to improve IBD care and health outcomes. However, few participants leveraged the care coordinator as intended. Finally, participants reported that the intervention could be better tailored to capture day-to-day symptom changes and to meet the needs of patients with specific comorbid conditions (eg, ostomies).

Conclusions

Remote PRO monitoring is acceptable and may be valuable in improving care management, promoting tight control, and supporting whole health in IBD. Future efforts should focus on testing and implementing refined versions of CAPTURE-IBD tailored to different clinical settings.

Refining nanoprobes for monitoring of inflammatory bowel disease

Inflammatory bowel disease (IBD) is a gastrointestinal immune disease that requires clear diagnosis, timely treatment, and lifelong monitoring. The diagnosis and monitoring methods of IBD mainly include endoscopy, imaging examination, and laboratory examination, which are constantly developed to achieve early definite diagnosis and accurate monitoring. In recent years, with the development of nanotechnology, the diagnosis and monitoring methods of IBD have been remarkably enriched. Nanomaterials, characterized by their minuscule dimensions that can be tailored, along with their distinctive optical, magnetic, and biodistribution properties, have emerged as valuable contrast agents for imaging and targeted agents for endoscopy. Through both active and passive targeting mechanisms, nanoparticles accumulate at the site of inflammation, thereby enhancing IBD detection. This review comprehensively outlines the existing IBD detection techniques, expounds upon the utilization of nanoparticles in IBD detection and diagnosis, and offers insights into the future potential of in vitro diagnostics. STATEMENT OF SIGNIFICANCE: Due to their small size and unique physical and chemical properties, nanomaterials are widely used in the biological and medical fields. In the area of oncology and inflammatory disease, an increasing number of nanomaterials are being developed for diagnostics and drug delivery. Here, we focus on inflammatory bowel disease, an autoimmune inflammatory disease that requires early diagnosis and lifelong monitoring. Nanomaterials can be used as contrast agents to visualize areas of inflammation by actively or passively targeting them through the intestinal mucosal epithelium where gaps exist due to inflammation stimulation. In this article, we summarize the utilization of nanoparticles in inflammatory bowel disease detection and diagnosis, and offers insights into the future potential of in vitro diagnostics.

Imaging and endoscopic tools in pediatric inflammatory bowel disease: What's new?

Pediatric inflammatory bowel disease (IBD) is a chronic inflammatory disorder, with increasing incidence and prevalence worldwide. There have been recent advances in imaging and endoscopic technology for disease diagnosis, treatment, and monitoring. Intestinal ultrasound, including transabdominal, transperineal, and endoscopic, has been emerging for the assessment of transmural bowel inflammation and disease complications (e.g., fistula, abscess). Aside from surgery, IBD-related intestinal strictures now have endoscopic treatment options including through-the-scope balloon dilatation, injection, and needle knife stricturotomy and new evaluation tools such as endoscopic functional lumen imaging probe. Unsedated transnasal endoscopy may have a role in patients with upper gastrointestinal Crohn's disease or those with IBD with new upper gastrointestinal symptoms. Improvements to dysplasia screening in pediatric patients with longstanding colonic disease or primary sclerosing cholangitis hold promise with the addition of virtual chromoendoscopy and ongoing research in the field of artificial intelligence-assisted endoscopic detection. Artificial intelligence and machine learning is a rapidly evolving field, with goals of further personalizing IBD diagnosis and treatment selection as well as prognostication. This review summarized these advancements, focusing on pediatric patients with IBD.

Predictive Insights Into Exocrine Pancreatic Insufficiency in Chronic Pancreatitis and Autoimmune Pancreatitis: A Decision Tree Approach

OBJECTIVE

Exocrine pancreatic insufficiency (EPI) is a common manifestation of chronic pancreatitis (CP) and autoimmune pancreatitis (AIP). This study aimed to estimate the presence of EPI in patients with CP or AIP using alternative clinical markers.

MATERIALS AND METHODS

A machine learning analysis employing a decision tree model was conducted on a retrospective training cohort comprising 57 patients with CP or AIP to identify EPI, defined as fecal elastase-1 levels less than 200 μg/g. The outcomes were then confirmed in a validation cohort of 26 patients.

RESULTS

Thirty-nine patients (68%) exhibited EPI in the training cohort. The decision tree algorithm revealed body mass index (≤21.378 kg/m 2 ) and total protein level (≤7.15 g/dL) as key variables for identifying EPI. The algorithm's performance was assessed using 5-fold cross-validation, yielding area under the receiver operating characteristic curve values of 0.890, 0.875, 0.750, 0.625, and 0.771, respectively. The results from the validation cohort closely replicated those in the training cohort.

CONCLUSIONS

Decision tree analysis revealed that EPI in patients with CP or AIP can be identified based on body mass index and total protein. These findings may help guide the implementation of appropriate treatments for EPI.

Modern practical management of acute severe colitis

Acute severe ulcerative colitis (ASUC) is one of life-threatening complications that occur in one-fifth of ulcerative colitis (UC) patients with significant morbidity and an estimated mortality rate up to 1%. There are no validated clinical scoring systems for ASUC. Intravenous corticosteroids remain the cornerstone for the management of ASUC patients However, one-third of patients are steroid refractory and require colectomy in the pre-biologic era or salvage therapy in the post-biologic era. The currently available predictors of non-response to steroids and salvages therapy are sub-optimal. Furthermore, there is a need for the development of clear outcome measures for ASUC patients. Although infliximab and cyclosporin are both effective as salvage therapy, they still carry a rate of treatment failure. Hence, there is an unmet need to explore alternative therapeutic options before colectomy particularly in prior infliximab-exposed patients. This may include the introduction of small molecules with rapid onset of action as a salvage or sequential therapy and the use of slow-onset other biological therapy after "bridging" with cyclosporine. In this article, we explore the current best evidence-based practice and detail the gaps in knowledge in the management of ASUC.

Impact of artificial intelligence on prognosis, shared decision-making, and precision medicine for patients with inflammatory bowel disease: a perspective and expert opinion

INTRODUCTION

Artificial intelligence (AI) is expected to impact all facets of inflammatory bowel disease (IBD) management, including disease assessment, treatment decisions, discovery and development of new biomarkers and therapeutics, as well as clinician-patient communication.

AREAS COVERED

This perspective paper provides an overview of the application of AI in the clinical management of IBD through a review of the currently available AI models that could be potential tools for prognosis, shared decision-making, and precision medicine. This overview covers models that measure treatment response based on statistical or machine-learning methods, or a combination of the two. We briefly discuss a computational model that allows integration of immune/biological system knowledge with mathematical modeling and also involves a 'digital twin', which allows measurement of temporal trends in mucosal inflammatory activity for predicting treatment response. A viewpoint on AI-enabled wearables and nearables and their use to improve IBD management is also included.

EXPERT OPINION

Although challenges regarding data quality, privacy, and security; ethical concerns; technical limitations; and regulatory barriers remain to be fully addressed, a growing body of evidence suggests a tremendous potential for integration of AI into daily clinical practice to enable precision medicine and shared decision-making.

Application of deep learning in the diagnosis and evaluation of ulcerative colitis disease severity

Background

Achieving endoscopic and histological remission is a critical treatment objective in ulcerative colitis (UC). Nevertheless, interobserver variability can significantly impact overall assessment performance.

Objectives

We aimed to develop a deep learning algorithm for the real-time and objective evaluation of endoscopic disease activity and prediction of histological remission in UC.

Design

This is a retrospective diagnostic study.

Methods

Two convolutional neural network (CNN) models were constructed and trained using 12,257 endoscopic images and biopsy results sourced from 1124 UC patients who underwent colonoscopy at a single center from January 2018 to December 2022. Mayo Endoscopy Subscore (MES) and UC Endoscopic Index of Severity Score (UCEIS) assessments were conducted by two experienced and independent reviewers. Model performance was evaluated in terms of accuracy, sensitivity, and positive predictive value. The output of the CNN models was also compared with the corresponding histological results to assess histological remission prediction performance.

Results

The MES-CNN model achieved 97.04% accuracy in diagnosing endoscopic remission of UC, while the MES-CNN and UCEIS-CNN models achieved 90.15% and 85.29% accuracy, respectively, in evaluating endoscopic severity of UC. For predicting histological remission, the CNN models achieved accuracy and kappa values of 91.28% and 0.826, respectively, attaining higher accuracy than human endoscopists (87.69%).

Conclusion

The proposed artificial intelligence model, based on MES and UCEIS evaluations from expert gastroenterologists, offered precise assessment of inflammation in UC endoscopic images and reliably predicted histological remission.

Role of artificial intelligence in imaging and endoscopy for the diagnosis, monitoring and prognostication of inflammatory bowel disease: a scoping review protocol

INTRODUCTION

Inflammatory bowel diseases (IBD) are immune-mediated conditions that are increasing in incidence and prevalence worldwide. Their assessment and monitoring are becoming increasingly important, though complex. The best disease control is achieved through tight monitoring of objective inflammatory parameters (such as serum and stool inflammatory markers), cross-sectional imaging and endoscopic assessment. Considering the complexity of the information obtained throughout a patient's journey, artificial intelligence (AI) provides an ideal adjunct to existing tools to help diagnose, monitor and predict the course of disease of patients with IBD. Therefore, we propose a scoping review assessing AI's role in diagnosis, monitoring and prognostication tools in patients with IBD. We aim to detect gaps in the literature and address them in future research endeavours.

METHODS AND ANALYSIS

We will search electronic databases, including Medline, Embase, Cochrane CENTRAL, CINAHL Complete, Web of Science and IEEE Xplore. Two reviewers will independently screen the abstracts and titles first and then perform the full-text review. A third reviewer will resolve any conflict. We will include both observational studies and clinical trials. Study characteristics will be extracted using a data extraction form. The extracted data will be summarised in a tabular format, following the imaging modality theme and the study outcome assessed. The results will have an accompanying narrative review.

ETHICS AND DISSEMINATION

Considering the nature of the project, ethical review by an institutional review board is not required. The data will be presented at academic conferences, and the final product will be published in a peer-reviewed journal.

Prognostic modelling in IBD

In the ideal world prognostication or predicting disease course in any chronic condition would allow the clinician to anticipate disease behaviour, providing crucial information for the patient and data regarding best use of resources. Prognostication also allows an understanding of likely response to treatment and the risk of adverse effects of a treatment leading to withdrawal in any individual patient. Therefore, the ability to predict outcomes from the onset of disease is the key step to developing precision personalised medicine, which is the design of medical care to optimise efficiency or therapeutic benefit based on careful profiling of patients. An important corollary is to prevent unnecessary healthcare costs. This paper outlines currently available predictors of disease outcome in IBD and looks to the future which will involve the use of artificial intelligence to interrogate big data derived from various important 'omes' to tease out a more holistic approach to IBD.

A systematic review and meta-analysis of artificial intelligence-diagnosed endoscopic remission in ulcerative colitis

Endoscopic remission is an important therapeutic goal in ulcerative colitis (UC). The Ulcerative Colitis Endoscopic Index of Severity (UCEIS) and Mayo Endoscopic Score (MES) are the commonly used endoscopic scoring criteria. This systematic review and meta-analysis aimed to evaluate the accuracy of artificial intelligence (AI) in diagnosing endoscopic remission in UC. We also performed a meta-analysis of each of the four endoscopic remission criteria (UCEIS = 0, MES = 0, UCEIS = <1, MES = <1). Eighteen studies involving 13,687 patients were included. The combined sensitivity and specificity of AI for diagnosing endoscopic remission in UC was 87% (95% confidence interval [CI]:81-92%) and 92% (95% CI: 89-94%), respectively. The area under the curve (AUC) was 0.96 (95% CI: 0.94-0.97). The results showed that the AI model performed well regardless of which criteria were used to define endoscopic remission of UC.

The interaction of bile acids and gut inflammation influences the pathogenesis of inflammatory bowel disease

Bile acids (BA) are amphipathic molecules originating from cholesterol in the liver and from microbiota-driven biotransformation in the colon. In the gut, BA play a key role in fat digestion and absorption and act as potent signaling molecules on the nuclear farnesoid X receptor (FXR) and membrane-associated G protein-coupled BA receptor-1 (GPBAR-1). BA are, therefore, involved in the maintenance of gut barrier integrity, gene expression, metabolic homeostasis, and microbiota profile and function. Disturbed BA homeostasis can activate pro-inflammatory pathways in the gut, while inflammatory bowel diseases (IBD) can induce gut dysbiosis and qualitative and/or quantitative changes of the BA pool. These factors contribute to impaired repair capacity of the mucosal barrier, due to chronic inflammation. A better understanding of BA-dependent mechanisms paves the way to innovative therapeutic tools by administering hydrophilic BA and FXR agonists and manipulating gut microbiota with probiotics and prebiotics. We discuss the translational value of pathophysiological and therapeutic evidence linking BA homeostasis to gut inflammation in IBD.

Systematic review: Scores used to predict outcomes in acute severe ulcerative colitis

Predictive scores for ASUC outcomes according to time of application.

The pathobiology of follicular lymphoma

Follicular lymphoma is one of the most frequent lymphomas. Histologically, it is characterized by a follicular (nodular) growth pattern of centrocytes and centroblasts; mixed with variable immune microenvironment cells. Clinically, it is characterized by diffuse lymphadenopathy, bone marrow involvement, and splenomegaly. It is biologically and clinically heterogeneous. In most patients it is indolent, but others have a more aggressive evolution with relapses; and transformation to diffuse large B-cell lymphoma. Tumorigenesis includes an asymptomatic preclinical phase in which premalignant B-lymphocytes with the t(14;18) chromosomal translocation acquire additional genetic alterations in the germinal centers, and clonal evolution occurs, although not all the cells progress to the tumor stage. This manuscript reviews the pathobiology and clinicopathological characteristics of follicular lymphoma. It includes a description of the physiology of the germinal center, the genetic alterations of BCL2 and BCL6, the mutational profile, the immune checkpoint, precision medicine, and highlights in the lymphoma classification. In addition, a comment and review on artificial intelligence and machine (deep) learning are made.

Advanced machine learning model for predicting Crohn's disease with enhanced ant colony optimization

Changes in human lifestyles have led to a dramatic increase in the incidence of Crohn's disease worldwide. Predicting the activity and remission of Crohn's disease has become an urgent research problem. In addition, the influence of each attribute in the test sample on the prediction results and the interpretability of the model still deserves further investigation. Therefore, in this paper, we proposed a wrapper feature selection classification model based on a combination of the improved ant colony optimization algorithm and the kernel extreme learning machine, called bIACOR-KELM-FS. IACOR introduces an evasive strategy and astrophysics strategy to balance the exploration and exploitation phases of the algorithm and enhance its optimization capabilities. The optimization capability of the proposed IACOR was validated on the IEEE CEC2017 benchmark test function. And the prediction was performed on Crohn's disease dataset. The results of the quantitative analysis showed that the prediction accuracy of bIACOR-KELM-FS for predicting the activity and remission of Crohn's disease reached 98.98%. The analysis of important attributes improved the interpretability of the model and provided a reference for the diagnosis of Crohn's disease. Therefore, the proposed model is considered a promising adjunctive diagnostic method for Crohn's disease.

Magnetic Resonance Imaging of the Gastrointestinal Tract: Current Role, Recent Advancements and Future Prospectives

This review focuses on the role of magnetic resonance imaging (MRI) in the evaluation of the gastrointestinal tract (GI MRI), analyzing the major technical advances achieved in this field, such as diffusion-weighted imaging, molecular imaging, motility studies, and artificial intelligence. Today, MRI performed with the more advanced imaging techniques allows accurate assessment of many bowel diseases, particularly inflammatory bowel disease and rectal cancer; in most of these diseases, MRI is invaluable for diagnosis, staging, and disease monitoring under treatment. Several MRI parameters are currently considered activity biomarkers for inflammation and neoplastic disease. Furthermore, in younger patients with acute or chronic GI disease, MRI can be safely used for short-term follow-up studies in many critical clinical situations because it is radiation-free. MRI assessment of functional gastro-esophageal and small bowel disorders is still in its infancy but very promising, while it is well established and widely used for dynamic assessment of anorectal and pelvic floor dysfunction; MRI motility biomarkers have also been described. There are still some limitations to GI MRI related to high cost and limited accessibility. However, technical advances are expected, such as faster sequences, more specific intestinal contrast agents, AI analysis of MRI data, and possibly increased accessibility to GI MRI studies. Clinical interest in the evaluation of bowel disease using MRI is already very high, but is expected to increase significantly in the coming years.

Artificial Intelligence Enables Quantitative Assessment of Ulcerative Colitis Histology

Ulcerative colitis is a chronic inflammatory bowel disease that is characterized by a relapsing and remitting course. Assessment of disease activity critically informs treatment decisions. In addition to endoscopic remission, histologic remission is emerging as a treatment target and a key factor in the evaluation of disease activity and therapeutic efficacy. However, manual pathologist evaluation is semiquantitative and limited in granularity. Machine learning approaches are increasingly being developed to aid pathologists in accurate and reproducible scoring of histology, enabling precise quantitation of clinically relevant features. Here, we report the development and validation of convolutional neural network models that quantify histologic features pertinent to ulcerative colitis disease activity, directly from hematoxylin and eosin-stained whole slide images. Tissue and cell model predictions were used to generate quantitative human-interpretable features to fully characterize the histology samples. Tissue and cell predictions showed comparable agreement to pathologist annotations, and the extracted slide-level human-interpretable features demonstrated strong correlations with disease severity and pathologist-assigned Nancy histological index scores. Moreover, using a random forest classifier based on 13 human-interpretable features derived from the tissue and cell models, we were able to accurately predict Nancy histological index scores, with a weighted kappa (κ = 0.91) and Spearman correlation (⍴ = 0.89, P < .001) when compared with pathologist consensus Nancy histological index scores. We were also able to predict histologic remission, based on the absence of neutrophil extravasation, with a high accuracy of 0.97. This work demonstrates the potential of computer vision to enable a standardized and robust assessment of ulcerative colitis histopathology for translational research and improved evaluation of disease activity and prognosis.

Salvage Therapy in Acute Severe Ulcerative Colitis: Current Practice and a Look to the Future

The risk of urgent bowel resection increases significantly among patients hospitalized with acute severe ulcerative colitis. In-hospital management requires quick diagnostic, therapeutic, and decision-making, combined with a multi-disciplinary approach and accessibility to multiple therapeutic options. However, the optimal strategy is still debatable. We performed a review of the current options for salvage therapy as well as novel therapy options emerging. We reviewed studies reporting outcomes of hospitalized steroid-refractory acute severe ulcerative colitis treated with salvage therapy (calcineurin inhibitors, infliximab) as well as studies using novel biologic, small molecules, antibiotics, and artificial intelligence to optimize therapy. We collected statistical data about patient factors that impact clinical management and how these can be applied to the real-life practice in order to prescribe a more personalized medicine. Several new drugs and approaches have shown benefits during the last decades for the management of acute severe ulcerative colitis. This effort is driven by the necessity of more effective, safe, and rapidly active therapeutic options with better convenient routes of administration, in order to improve therapeutic outcomes and quality of life for patients. The next step will be tailored medicine according to patients' profiles, taking into account disease characteristics, laboratory parameters, and patients' preferences.

Rosiglitazone Does Not Affect the Risk of Inflammatory Bowel Disease: A Retrospective Cohort Study in Taiwanese Type 2 Diabetes Patients

Human studies on the effect of rosiglitazone on inflammatory bowel disease (IBD) are still lacking. We investigated whether rosiglitazone might affect IBD risk by using the reimbursement database of Taiwan's National Health Insurance to enroll a propensity-score-matched cohort of ever users and never users of rosiglitazone. The patients should have been newly diagnosed with diabetes mellitus between 1999 and 2006 and should have been alive on 1 January 2007. We then started to follow the patients from 1 January 2007 until 31 December 2011 for a new diagnosis of IBD. Propensity-score-weighted hazard ratios were estimated with regards to rosiglitazone exposure in terms of ever users versus never users and in terms of cumulative duration and cumulative dose of rosiglitazone therapy for dose-response analyses. The joint effects and interactions between rosiglitazone and risk factors of psoriasis/arthropathies, dorsopathies, and chronic obstructive pulmonary disease/tobacco abuse and the use of metformin were estimated by Cox regression after adjustment for all covariates. A total of 6226 ever users and 6226 never users were identified and the respective numbers of incident IBD were 95 and 111. When we compared the risk of IBD in ever users to that of the never users, the estimated hazard ratio (0.870, 95% confidence interval: 0.661-1.144) was not statistically significant. When cumulative duration and cumulative dose of rosiglitazone therapy were categorized by tertiles and hazard ratios were estimated by comparing the tertiles of rosiglitazone exposure to the never users, none of the hazard ratios reached statistical significance. In secondary analyses, rosiglitazone has a null association with Crohn's disease, but a potential benefit on ulcerative colitis (UC) could not be excluded. However, because of the low incidence of UC, we were not able to perform detailed dose-response analyses for UC. In the joint effect analyses, only the subgroup of psoriasis/arthropathies (-)/rosiglitazone (-) showed a significantly lower risk in comparison to the subgroup of psoriasis/arthropathies (+)/rosiglitazone (-). No interactions between rosiglitazone and the major risk factors or metformin use were observed. We concluded that rosiglitazone has a null effect on the risk of IBD, but the potential benefit on UC awaits further investigation.

Mise au point de l’utilisation de l’intelligence artificielle dans la prise en charge des maladies inflammatoires chroniques de l’intestin

Complexity of inflammatory bowel diseases (IBD) lies on their management and their biology. Clinics, blood and fecal samples tests, endoscopy and histology are the main tools guiding IBD treatment, but they generate a large amount of data, difficult to analyze by clinicians. Because of its capacity to analyze large number of data, artificial intelligence is currently generating enthusiasm in medicine, and this technology could be used to improve IBD management. In this review, after a short summary on IBD management and artificial intelligence, we will report pragmatic examples of artificial intelligence utilisation in IBD. Lastly, we will discuss the limitations of this technology.

Systematic review of artificial intelligence-based image diagnosis for inflammatory bowel disease

OBJECTIVES

Diagnosis of inflammatory bowel diseases (IBD) involves combining clinical, laboratory, endoscopic, histologic, and radiographic data. Artificial intelligence (AI) is rapidly being developed in various fields of medicine, including IBD. Because a key part in the diagnosis of IBD involves evaluating imaging data, AI is expected to play an important role in this aspect in the coming decades. We conducted a systematic literature review to highlight the current advancement of AI in diagnosing IBD from imaging data.

METHODS

We performed an electronic PubMed search of the MEDLINE database for studies up to January 2022 involving IBD and AI. Studies using imaging data as input were included, and nonimaging data were excluded.

RESULTS

A total of 27 studies are reviewed, including 18 studies involving endoscopic images and nine studies involving other imaging data.

CONCLUSION

We highlight in this review the recent advancement of AI in diagnosing IBD from imaging data by summarizing the relevant studies, and discuss the future role of AI in clinical practice.

Chronic stress induces colonic tertiary lymphoid organ formation and protection against secondary injury through IL-23/IL-22 signaling

Psychological stress has been previously reported to worsen symptoms of inflammatory bowel disease (IBD). Similarly, intestinal tertiary lymphoid organs (TLOs) are associated with more severe inflammation. While there is active debate about the role of TLOs and stress in IBD pathogenesis, there are no studies investigating TLO formation in the context of psychological stress. Our mouse model of Crohn's disease-like ileitis, the SAMP1/YitFc (SAMP) mouse, was subjected to 56 consecutive days of restraint stress (RS). Stressed mice had significantly increased colonic TLO formation. However, stress did not significantly increase small or large intestinal inflammation in the SAMP mice. Additionally, 16S analysis of the stressed SAMP microbiome revealed no genus-level changes. Fecal microbiome transplantation into germ-free SAMP mice using stool from unstressed and stressed mice replicated the behavioral phenotype seen in donor mice. However, there was no difference in TLO formation between recipient mice. Stress increased the TLO formation cytokines interleukin-23 (IL-23) and IL-22 followed by up-regulation of antimicrobial peptides. SAMP × IL-23r-/- (knockout [KO]) mice subjected to chronic RS did not have increased TLO formation. Furthermore, IL-23, but not IL-22, production was increased in KO mice, and administration of recombinant IL-22 rescued TLO formation. Following secondary colonic insult with dextran sodium sulfate, stressed mice had reduced colitis on both histology and colonoscopy. Our findings demonstrate that psychological stress induces colonic TLOs through intrinsic alterations in IL-23 signaling, not through extrinsic influence from the microbiome. Furthermore, chronic stress is protective against secondary insult from colitis, suggesting that TLOs may function to improve the mucosal barrier.