How Artificial Intelligence Will Transform Clinical Care, Research, and Trials for Inflammatory Bowel Disease.

Artificial intelligence (AI) refers to computer-based methodologies which use data to teach a computer to solve pre-defined tasks; these methods can be applied to identify patterns in large multi-modal data sources. AI applications in inflammatory bowel disease (IBD) includes predicting response to therapy, disease activity scoring of endoscopy, drug discovery, and identifying bowel damage in images. As a complex disease with entangled relationships between genomics, metabolomics, microbiome, and the environment, IBD stands to benefit greatly from methodologies that can handle this complexity. We describe current applications, critical challenges, and propose future directions of AI in IBD.

Artificial intelligence-assisted video colonoscopy for disease monitoring of ulcerative colitis: A prospective study.

BACKGROUNDS AND AIMS: The Mayo endoscopic subscore (MES) is the most popular endoscopic disease activity measure of ulcerative colitis (UC). Artificial intelligence (AI)-assisted colonoscopy is expected to reduce diagnostic variability among endoscopists. However, no study has been conducted to ascertain whether AI-based MES assignments can help predict clinical relapse, nor has AI been verified to improve the diagnostic performance of non-specialists. METHODS: This open-label, prospective cohort study enrolled 110 patients with UC in clinical remission. The AI algorithm was developed using 74713 images from 898 patients who underwent colonoscopy at three centers. Patients were followed up after colonoscopy for 12 months, and clinical relapse was defined as a partial Mayo score >2. A multi-video, multi-reader analysis involving 124 videos was conducted to determine whether the AI system reduced the diagnostic variability among six non-specialists. RESULTS: The clinical relapse rate for patients with AI-based MES = 1 (24.5% [12/49]) was significantly higher (log-rank test, P = 0.01) than that for patients with AI-based MES = 0 (3.2% [1/31]). Relapse occurred during the 12-month follow-up period in 16.2% (13/80) of patients with AI-based MES = 0 or 1 and 50.0% (10/20) of those with AI-based MES = 2 or 3 (log-rank test, P = 0.03). Using AI resulted in better inter- and intra-observer reproducibility than endoscopists alone. CONCLUSIONS: Colonoscopy using the AI-based MES system can stratify the risk of clinical relapse in patients with UC and improve the diagnostic performance of non-specialists.

Opening the doors of precision medicine: novel tools to assess intestinal barrier in inflammatory bowel disease and colitis-associated neoplasia.

Mounting evidence underscores the pivotal role of the intestinal barrier and its convoluted network with diet and intestinal microbiome in the pathogenesis of inflammatory bowel disease (IBD) and colitis-associated colorectal cancer (CRC). Moreover, the bidirectional association of the intestinal barrier with the liver and brain, known as the gut-brain axis, plays a crucial role in developing complications, including extraintestinal manifestations of IBD and CRC metastasis. Consequently, barrier healing represents a crucial therapeutic target in these inflammatory-dependent disorders, with barrier assessment predicting disease outcomes, response to therapy and extraintestinal manifestations.New advanced technologies are revolutionising our understanding of the barrier paradigm, enabling the accurate assessment of the intestinal barrier and aiding in unravelling the complexity of the gut-brain axis. Cutting-edge endoscopic imaging techniques, such as ultra-high magnification endocytoscopy and probe-based confocal laser endomicroscopy, are new technologies allowing real-time exploration of the 'cellular' intestinal barrier. Additionally, novel advanced spatial imaging technology platforms, including multispectral imaging, upconversion nanoparticles, digital spatial profiling, optical spectroscopy and mass cytometry, enable a deep and comprehensive assessment of the 'molecular' and 'ultrastructural' barrier. In this promising landscape, artificial intelligence plays a pivotal role in standardising and integrating these novel tools, thereby contributing to barrier assessment and prediction of outcomes.Looking ahead, this integrated and comprehensive approach holds the promise of uncovering new therapeutic targets, breaking the therapeutic ceiling in IBD. Novel molecules, dietary interventions and microbiome modulation strategies aim to restore, reinforce, or modulate the gut-brain axis. These advancements have the potential for transformative and personalised approaches to managing IBD.

ECCO Guidelines on Therapeutics in Crohn's Disease: Surgical Treatment.

This article is the second in a series of two publications on the European Crohn's and Colitis Organisation [ECCO] evidence-based consensus on the management of Crohn's disease. The first article covers medical management; the present article addresses surgical management, including preoperative aspects and drug management before surgery. It also provides technical advice for a variety of common clinical situations. Both articles together represent the evidence-based recommendations of the ECCO for Crohn's disease and an update of prior ECCO guidelines.

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.

Clinical outcomes of diverticular disease in young adults: results from a tertiary referral center.

Introduction: Diverticular disease (DD), commonly associated with the elderly, is becoming more prevalent among younger individuals. This retrospective study aimed to evaluate the differences in the natural history and outcomes between young and old patients with DD. Methods: Adult patients with DD diagnosed between 2010 and 2022 at an Italian tertiary referral center were enrolled, and their demographic and clinical data were retrieved. The patients were categorized as young or old based on the 25th percentile of the population's age at diagnosis. Univariate and multivariate analyses were performed to assess the association between the collected variables and the age of disease presentation. Additionally, survival analyses were conducted to evaluate the association between the age of diagnosis and clinical outcomes at follow-up, including disease recurrence, hospital access, surgery, and death. Results: A total of 220 DD patients (with a median age of 66 years, IQR 55-74, and a female-to-male ratio of 1.4:1) were included in the study, comprising 54 patients receiving a diagnosis before the age of 49 years (young DD patients) and 166 patients diagnosed after the age of 49 years (old DD patients). Male sex (57 vs. 36%, p < 0.01), smoking (38 vs. 14%, p < 0.01), and alcohol consumption (54 vs. 38%) were highly prevalent in young patients. The complications at the time of diagnosis, particularly abscesses and free perforations, occurred more frequently in younger patients (p = 0.04). Moreover, young DD patients experienced a higher rate of hospitalization and surgical intervention (p = 0.01 and p = 0.04, respectively) over a median follow-up period of 5 years. Conclusion: Preventive strategies and prompt diagnosis are crucial in young patients with DD for achieving better disease outcomes and preventing complications.

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.

Accuracy of Information given by ChatGPT for patients with Inflammatory Bowel Disease in relation to ECCO Guidelines.

INTRODUCTION: As acceptance of AI platforms increases, more patients will consider these tools as sources of information. The ChatGPT architecture utilizes a neural network to process natural language, thus generating responses based on the context of input text. The accuracy and completeness of ChatGPT3.5 in the context of Inflammatory Bowel Disease remains unclear. METHODS: In this prospective study, 38 questions worded by IBD patients were inputted into ChatGPT3.5. The following topics were covered: 1) CD, UC and malignancy, 2) maternal medicine 3) infection and vaccination 4) complementary medicine. Responses given by Chat GPT were assessed for accuracy (1 - completely incorrect to 5 - completely correct) and completeness (3-point Likert scale; range 1 - incomplete to 3 - complete) by 14 expert gastroenterologists, in comparison with relevant ECCO guidelines. RESULTS: In terms of accuracy, most replies (84.2%) had a median score of ≥4 (IQR:2) and a mean score of 3.87 (SD: +/- 0.6). For completeness, 34.2% of the replies had a median score of 3 and 55.3 % had a median score of between 2 and <3. Overall, the mean rating was 2.24 (SD: +/- 0.4, Median:2 IQR :1). Though group 3 and 4 had a higher mean for both accuracy and completeness, there was no significant scoring variation between the 4 question groups (Kruskal-Wallis test p:>0.05). However, statistical analysis for the different individual questions revealed a significant difference both for accuracy (p<0.001) and completeness (p<0.001). The questions which rated the highest for both accuracy and completeness were related to smoking, while the lowest rating was related to screening for malignancy and vaccinations especially in the context of immunosuppression and family planning. CONCLUSION: This is the first study to demonstrate the capability of an AI-based system to provide accurate and comprehensive answers to real-world patient queries in IBD. AI systems may serve as a useful adjunct for patients, in addition to standard of care in clinic and validated patient information resources. However, responses in specialist areas may deviate from evidence-based guidance and the replies need to give more firm advice.

A novel artificial intelligence-assisted "vascular healing" diagnosis for prediction of future clinical relapse in patients with ulcerative colitis: a prospective cohort study (with video).

BACKGROUND AND AIMS: Image-enhanced endoscopy has attracted attention as a method for detecting inflammation and predicting outcomes in patients with ulcerative colitis (UC); however, the procedure requires specialist endoscopists. Artificial intelligence (AI)-assisted image-enhanced endoscopy may help nonexperts provide objective accurate predictions with the use of optical imaging. We aimed to develop a novel AI-based system using 8853 images from 167 patients with UC to diagnose "vascular-healing" and establish the role of AI-based vascular-healing for predicting the outcomes of patients with UC. METHODS: This open-label prospective cohort study analyzed data for 104 patients with UC in clinical remission. Endoscopists performed colonoscopy using the AI system, which identified the target mucosa as AI-based vascular-active or vascular-healing. Mayo endoscopic subscore (MES), AI outputs, and histologic assessment were recorded for 6 colorectal segments from each patient. Patients were followed up for 12 months. Clinical relapse was defined as a partial Mayo score >2 RESULTS: The clinical relapse rate was significantly higher in the AI-based vascular-active group (23.9% [16/67]) compared with the AI-based vascular-healing group (3.0% [1/33)]; P = .01). In a subanalysis predicting clinical relapse in patients with MES ≤1, the area under the receiver operating characteristic curve for the combination of complete endoscopic remission and vascular healing (0.70) was increased compared with that for complete endoscopic remission alone (0.65). CONCLUSIONS: AI-based vascular-healing diagnosis system may potentially be used to provide more confidence to physicians to accurately identify patients in remission of UC who would likely relapse rather than remain stable.

Present and future of endoscopy precision for inflammatory bowel disease.

Several advanced imaging techniques are now available for endoscopists managing inflammatory bowel disease (IBD) patients. These tools, including dye-based and virtual chromoendoscopy, probe-based confocal laser endomicroscopy and endocytoscopy, are increasingly innovative applications in clinical practice. They allow for a more in-depth and refined evaluation of the mucosal and vascular bowel surface, getting closer to histology. They have demonstrated a remarkable ability in assessing intestinal inflammation, histologic remission, and predicting relapse and favorable long-term outcomes. In addition, the future application of molecular endoscopy to predict biological drug responses has yielded preliminary but encouraging results. Furthermore, these techniques are crucial in detecting and characterizing IBD-related dysplasia, assisting endoscopic mucosal resection and submucosal dissection towards a surgery-sparing approach. Artificial intelligence (AI) holds great potential in this promising landscape, as it can provide an objective and reproducible assessment of inflammation and dysplasia. Moreover, it can improve the prediction of outcomes and aid in subsequent therapeutic decision-making. This review aims to summarize the promising role of state-of-the-art advanced endoscopic techniques and related AI-enabled models for managing IBD, paving the way for precision medicine.

Definitions of Histological Abnormalities in Inflammatory Bowel Disease: an ECCO Position Paper.

Histological assessment of endoscopic biopsies in inflammatory bowel disease [IBD] plays an important role in clinical management, investigative studies, and clinical trials. Scoring schemes consisting of multiple histological items and offering considerable precision are widely available. However, definitions of histological abnormalities are often inconsistent. Furthermore, interobserver variability for their recognition and assessment may be high. The European Crohn's and Colitis Organisation [ECCO] formed an expert panel to explore definitions of histological abnormalities in IBD, with the aim of improving the quality of diagnosis and facilitating development of scoring schemes. The process confirmed that the current definitions often have no evidence base and vary between sources. Using available evidence and expert knowledge, the panel produced a series of ECCO consensus position statements on histological features in IBD.

Can artificial intelligence replace endoscopists when assessing mucosal healing in ulcerative colitis? A systematic review and diagnostic test accuracy meta-analysis.

BACKGROUNDS AND AIMS: Mucosal healing (MH) in inflammatory bowel diseases (IBD) is an important landmark for clinical decision making. Artificial intelligence systems (AI) that automatically deliver the grade of endoscopic inflammation may solve moderate interobserver agreement and the need of central reading in clinical trials. METHODS: We performed a systematic review of EMBASE and MEDLINE databases up to 01/12/2022 following PRISMA and the Joanna Briggs Institute methodologies to answer the following question: "Can AI replace endoscopists when assessing MH in IBD?". The research was restricted to ulcerative colitis (UC), and a diagnostic odds ratio (DOR) meta-analysis was performed. Risk of bias was evaluated with QUADAS-2 tool. RESULTS: A total of 21 / 739 records were selected for full text evaluation, and 12 were included in the meta-analysis. Deep learning algorithms based on convolutional neural networks architecture achieved a satisfactory performance in evaluating MH on UC, with sensitivity, specificity, DOR and SROC of respectively 0.91(CI95 %:0.86-0.95);0.89(CI95 %:0.84-0.93);92.42(CI95 %:54.22-157.53) and 0.957 when evaluating fixed images (n = 8) and 0.86(CI95 %:0.75-0.93);0.91(CI95 %:0.87-0.94);70.86(CI95 %:24.63-203.86) and 0.941 when evaluating videos (n = 6). Moderate-high levels of heterogeneity were noted, limiting the quality of the evidence. CONCLUSIONS: AI systems showed high potential in detecting MH in UC with optimal diagnostic performance, although moderate-high heterogeneity of the data was noted. Standardised and shared AI training may reduce heterogeneity between systems.

Rates of Endoscopic Recurrence In Postoperative Crohn's Disease Based on Anastomotic Techniques: A Systematic Review And Meta-Analysis.

BACKGROUND: Patients with Crohn's disease (CD) after ileocolic resection may develop an endoscopic postoperative recurrence (ePOR) that reaches 40% to 70% of incidence within 6 months. Recently, there has been growing interest in the potential effect of anastomotic configurations on ePOR. Kono-S anastomosis has been proposed for reducing the risk of clinical and ePOR. Most studies have assessed the association of ileocolonic anastomosis and ePOR individually, while there is currently limited data simultaneously comparing several types of anastomosis. Therefore, we performed a systematic review and meta-analysis to assess the impact of different ileocolonic anastomosis on ePOR in CD. METHODS: We searched PubMed and Embase from inception to January 2023 for eligible studies reporting the types of anastomoses and, based on these, the rate of endoscopic recurrence at ≥6 months. Studies were grouped by conventional anastomosis, including side-to-side, end-to-end, and end-to-side vs Kono-S, and comparisons were made between these groups. Pooled incidence rates of ePOR were computed using random-effect modelling. RESULTS: Seventeen studies, with 2087 patients who underwent ileocolic resection for CD were included. Among these patients, 369 (17,7%) Kono-S anastomoses were performed, while 1690 (81,0%) were conventional ileocolic anastomosis. Endoscopic postoperative recurrence at ≥6 months showed a pooled incidence of 37.2% (95% CI, 27.7-47.2) with significant heterogeneity among the studies (P < .0001). In detail, patients receiving a Kono-S anastomosis had a pooled incidence of ePOR of 24.7% (95% CI, 6.8%-49.4%), while patients receiving a conventional anastomosis had an ePOR of 42.6% (95% CI, 32.2%-53.4%). CONCLUSIONS: Kono-S ileocolic anastomosis was more likely to decrease the risk of ePOR at ≥6 months compared with conventional anastomosis. Our findings highlight the need to implement the use of Kono-S anastomosis, particularly for difficult to treat patients. However, results from larger randomized controlled trials are needed to confirm these data.

This meta-analysis provides estimates of endoscopic postoperative recurrence based on different types of anastomosis. The results reveal a trend towards the superiority of Kono-S over conventional anastomosis, including side-to-side, endo-to-end and end-to-side, for decreasing the risk of endoscopic postoperative recurrence.

Validation of a new optical diagnosis training module to improve dysplasia characterization in inflammatory bowel disease: a multicenter international study.

BACKGROUND AND AIMS: Inflammatory bowel disease (IBD) increases risk of dysplasia and colorectal cancer. Advanced endoscopic techniques allow for the detection and characterization of IBD dysplastic lesions, but specialized training is not widely available. We aimed to develop and validate an online training platform to improve the detection and characterization of colonic lesions in IBD: OPtical diagnosis Training to Improve dysplasia Characterization in Inflammatory Bowel Disease (OPTIC-IBD). METHODS: We designed a web-based learning module that includes surveillance principles, optical diagnostic methods, approach to characterization, and classifications of colonic lesions using still images and videos. We invited gastroenterologists from Canada, Italy, and the United Kingdom with a wide range of experience. Participants reviewed 24 educational videos of IBD colonic lesions, predicted histology, and rated their confidence. The primary endpoint was to improve accuracy in detecting dysplastic lesions after training on the platform. Furthermore, participants were randomized 1:1 to get additional training or not, with a final assessment occurring after 60 days. Diagnostic performance for dysplasia and rater confidence were measured. RESULTS: A total of 117 participants completed the study and were assessed for the primary endpoint. Diagnostic accuracy improved from 70.8% to 75.0% (P = .002) after training, with the greatest improvements seen in less experienced endoscopists. Improvements in both accuracy and confidence were sustained after 2 months of assessment, although the group randomized to receive additional training did not improve further. Similarly, participants' confidence in characterizing lesions significantly improved between before and after the course (P < .001), and it was sustained after 2 months of assessment. CONCLUSIONS: The OPTIC-IBD training module demonstrated that an online platform could improve participants' accuracy and confidence in the optical diagnosis of dysplasia in patients with IBD. The training platform can be widely available and improve endoscopic care for people with IBD. (Clinical trial registration number: NCT04924543.).

Precision endoscopy in colorectal polyps' characterization and planning of endoscopic therapy.

Precision endoscopy in the management of colorectal polyps and early colorectal cancer has emerged as the standard of care. It includes optical characterization of polyps and estimation of submucosal invasion depth of large nonpedunculated colorectal polyps to select the appropriate endoscopic resection modality. Over time, several imaging modalities have been implemented in endoscopic practice to improve optical performance. Among these, image-enhanced endoscopy systems and magnification endoscopy represent now well-established tools. New advanced technologies, such as endocytoscopy and confocal laser endomicroscopy, have recently shown promising results in predicting the histology of colorectal polyps. In recent years, artificial intelligence has continued to enhance endoscopic performance in the characterization of colorectal polyps, overcoming the limitations of other imaging modes. In this review we retrace the path of precision endoscopy, analyzing the yield of various endoscopic imaging techniques in personalizing management of colorectal polyps and early colorectal cancer.

How to recognise a Behçet's ulcer from other types of oral ulceration? Defining Behçet's ulceration by an International Delphi Consultation.

OBJECTIVES: To define the clinical characteristics of oral ulceration (OU) in Behçet's disease (BD), to allow differentiation from other causes of OU, including aphthous ulcers, by an International Delphi consultation. To develop a clinical guideline on how to recognise BD ulcers. METHODS: Round 1. 40 clinical images of OU in BD, recurrent aphthous stomatitis (RAS), inflammatory bowel disease (IBD) and mucous membrane pemphigoid (MMP) were shown. Participants answered, independently, which images would be consistent with a BD ulcer. Round 2. The results from marking independently were shown. The panel remarked the questions through iteration process. The images not agreed to be a possible BD ulcer were discarded. Round 3. 10 clinical descriptors that may define BD ulcers were suggested. Participants ranked the level of importance for each descriptor on each image presented. Round 4. Participants re-ranked their level of agreement for each descriptor through iteration process. Whether the clinical pictures would be different from RAS was also explored. A final agreement was reached. RESULTS: This study has shown clear differentiation between BD, IBD and MMP ulcers when defining them by phenotype through clinical images only. On the other hand, no differentiation between RAS and BD ulcers was found. The most important clinical descriptors that define BD ulcers have been agreed. CONCLUSIONS: New clinical guidance for Health Care Professionals (HCP) on how to recognise a BD ulcer has been proposed. This should elucidate an earlier diagnosis, quicker access to treatment and control of the disease enhancing patient's quality of life.