Assessing Small Bowel Stricturing and Morphology in Crohn's Disease Using Semi-automated Image Analysis

The role of molecular imaging in detecting fibrosis in Crohn's disease

Fibrosis is a pathological process that occurs due to chronic inflammation, leading to the proliferation of fibroblasts and the excessive deposition of extracellular matrix (ECM). The process of long-term fibrosis initiates with tissue hypofunction and progressively culminates in the ultimate manifestation of organ failure. Intestinal fibrosis is a significant complication of Crohn's disease (CD) that can result in persistent luminal narrowing and strictures, which are difficult to reverse. In recent years, there have been significant advances in our understanding of the cellular and molecular mechanisms underlying intestinal fibrosis in inflammatory bowel disease (IBD). Significant progress has been achieved in the fields of pathogenesis, diagnosis, and management of intestinal fibrosis in the last few years. A significant amount of research has also been conducted in the field of biomarkers for the prediction or detection of intestinal fibrosis, including novel cross-sectional imaging modalities such as positron emission tomography (PET) and single photon emission computed tomography (SPECT). Molecular imaging represents a promising biomedical approach that enables the non-invasive visualization of cellular and subcellular processes. Molecular imaging has the potential to be employed for early detection, disease staging, and prognostication in addition to assessing disease activity and treatment response in IBD. Molecular imaging methods also have a potential role to enabling minimally invasive assessment of intestinal fibrosis. This review discusses the role of molecular imaging in combination of AI in detecting CD fibrosis.

Navigating the complexities of drug development for inflammatory bowel disease

Inflammatory bowel disease (IBD) - consisting of ulcerative colitis and Crohn's disease - is a complex, heterogeneous, immune-mediated inflammatory condition with a multifactorial aetiopathogenesis. Despite therapeutic advances in this arena, a ceiling effect has been reached with both single-agent monoclonal antibodies and advanced small molecules. Therefore, there is a need to identify novel targets, and the development of companion biomarkers to select responders is vital. In this Perspective, we examine how advances in machine learning and tissue engineering could be used at the preclinical stage where attrition rates are high. For novel agents reaching clinical trials, we explore factors decelerating progression, particularly the decline in IBD trial recruitment, and assess how innovative approaches such as reconfiguring trial designs, harmonizing end points and incorporating digital technologies into clinical trials can address this. Harnessing opportunities at each stage of the drug development process may allow for incremental gains towards more effective therapies.

Artificial Intelligence for Quantifying Cumulative Small Bowel Disease Severity on CT-Enterography in Crohn's Disease

INTRODUCTION

Assessing the cumulative degree of bowel injury in ileal Crohn's disease (CD) is difficult. We aimed to develop machine learning (ML) methodologies for automated estimation of cumulative ileal injury on computed tomography-enterography (CTE) to help predict future bowel surgery.

METHODS

Adults with ileal CD using biologic therapy at a tertiary care center underwent ML analysis of CTE scans. Two fellowship-trained radiologists graded bowel injury severity at granular spatial increments along the ileum (1 cm), called mini-segments. ML segmentation methods were trained on radiologist grading with predicted severity and then spatially mapped to the ileum. Cumulative injury was calculated as the sum (S-CIDSS) and mean of severity grades along the ileum. Multivariate models of future small bowel resection were compared with cumulative ileum injury metrics and traditional bowel measures, adjusting for laboratory values, medications, and prior surgery at the time of CTE.

RESULTS

In 229 CTE scans, 8,424 mini-segments underwent analysis. Agreement between ML and radiologists injury grading was strong (κ = 0.80, 95% confidence interval 0.79-0.81) and similar to inter-radiologist agreement (κ = 0.87, 95% confidence interval 0.85-0.88). S-CIDSS (46.6 vs 30.4, P = 0.0007) and mean cumulative injury grade scores (1.80 vs 1.42, P < 0.0001) were greater in CD biologic users that went to future surgery. Models using cumulative spatial metrics (area under the curve = 0.76) outperformed models using conventional bowel measures, laboratory values, and medical history (area under the curve = 0.62) for predicting future surgery in biologic users.

DISCUSSION

Automated cumulative ileal injury scores show promise for improving prediction of outcomes in small bowel CD. Beyond replicating expert judgment, spatial enterography analysis can augment the personalization of bowel assessment in CD.

Current Developments and Role of Intestinal Ultrasound including the Advent of AI

Intestinal ultrasound is a non-invasive, safe, and cost-effective technique to study the small and large intestines. In addition to conventional B-mode and color doppler imaging, new US tools have been developed in more recent years that provide auxiliary data on many GI conditions, improving the diagnosis and assessment of relevant outcomes. We have reviewed the more recent literature (from 2010 onwards) on auxiliary tools in bowel ultrasound such as elastography techniques, CEUS, SICUS, and the potential contribution by artificial intelligence (AI) to overcome current intestinal ultrasound limitations. For this scoping review, we performed an extensive literature search on PubMed and EMBASE to identify studies published until December 2023 and investigating the application of elastography techniques, CEUS, SICUS, and AI in the ultrasonographic assessment of the small and large intestines. Multiparametric intestinal ultrasound shows promising capabilities in Crohn's disease, while less is known about the role in ulcerative colitis. Despite some evidence, the CEUS role as a point-of-care examination tool for rare conditions such as intestinal GvHD and ischemic small bowel disease seems promising, possibly avoiding the need to perform further cross-sectional imaging. The use of AI in intestinal ultrasound is still anecdotical and limited to acute appendicitis.

Contemporary Imaging Assessment of Strictures and Fibrosis in Crohn Disease, With Focus on Quantitative Biomarkers: From the AJR Special Series on Imaging of Fibrosis

Patients with Crohn disease commonly have bowel strictures develop, which exhibit varying degrees of inflammation and fibrosis. Differentiation of the distinct inflammatory and fibrotic components of strictures is key for the optimization of therapeutic management and for the development of antifibrotic drugs. Cross-sectional imaging techniques, including ultrasound, CT, and MRI, allow evaluation of the full thickness of the bowel wall as well as extramural complications and associated mesenteric abnormalities. Although promising data have been reported for a range of novel imaging biomarkers for detection of fibrosis and quantification of the degree of fibrosis, these biomarkers lack sufficient validation and standardization for clinical use. Additional methods, including PET with emerging radiotracers, artificial intelligence, and radiomics, are also under investigation for stricture characterization. In this review, we highlight the clinical relevance of identifying fibrosis in Crohn disease, review the histopathologic aspects of strictures in Crohn disease, summarize the morphologic imaging findings of strictures, and explore contemporary developments in the use of cross-sectional imaging techniques for detecting and characterizing intestinal strictures, with attention given to emerging quantitative biomarkers.

Bowel Stiffness Assessed by Shear-Wave Ultrasound Elastography Predicts Disease Behavior Progression in Patients With Crohn's Disease

INTRODUCTION

There is a lack of reliable predictors of disease behavior progression in patients with Crohn's disease (CD). Real-time shear-wave elastography (SWE) is a novel method for evaluating tissue stiffness. However, its value for assessing CD has not yet been investigated. We aimed to explore the value of SWE and other ultrasound parameters at diagnosis in predicting CD behavior progression.

METHODS

We retrospectively collected data from patients with CD with the nonstenotic nonpenetrating disease (B1 phenotype based on the Montreal classification). All patients underwent intestinal ultrasound at baseline and were followed up. The end point was defined as disease behavior progression to stricturing (B2) or penetrating (B3) disease. Cox regression analysis was performed for the association between baseline characteristics and subsequent end points. In addition, a multivariate nomogram was established to predict the risk of disease behavior progression quantitatively.

RESULTS

A total of 130 patients with CD with B1 phenotype were enrolled. Twenty-seven patients (20.8%) developed B2 or B3 disease, with a median follow-up of 33 months. Multivariate analysis identified that SWE was the only independent predictor of disease behavior progression (hazard ratio 1.08, 95% confidence interval 1.03-1.12, P = 0.001). A reverse of the HR appeared at the cutoff 12.75 kPa. The nomogram incorporating SWE and other clinical characteristics showed a good prediction performance (area under the curve = 0.792).

DISCUSSION

Intestinal stiffness assessed using SWE is an independent predictor of disease behavior progression in patients with CD. Patients with CD with SWE >12.75 kPa at diagnosis are prone to progress toward stricturing or penetrating diseases.

AI-luminating Artificial Intelligence in Inflammatory Bowel Diseases: A Narrative Review on the Role of AI in Endoscopy, Histology, and Imaging for IBD

Endoscopy, histology, and cross-sectional imaging serve as fundamental pillars in the detection, monitoring, and prognostication of inflammatory bowel disease (IBD). However, interpretation of these studies often relies on subjective human judgment, which can lead to delays, intra- and interobserver variability, and potential diagnostic discrepancies. With the rising incidence of IBD globally coupled with the exponential digitization of these data, there is a growing demand for innovative approaches to streamline diagnosis and elevate clinical decision-making. In this context, artificial intelligence (AI) technologies emerge as a timely solution to address the evolving challenges in IBD. Early studies using deep learning and radiomics approaches for endoscopy, histology, and imaging in IBD have demonstrated promising results for using AI to detect, diagnose, characterize, phenotype, and prognosticate IBD. Nonetheless, the available literature has inherent limitations and knowledge gaps that need to be addressed before AI can transition into a mainstream clinical tool for IBD. To better understand the potential value of integrating AI in IBD, we review the available literature to summarize our current understanding and identify gaps in knowledge to inform future investigations.

A radiomics nomogram based on MSCT and clinical factors can stratify fibrosis in inflammatory bowel disease

Intestinal fibrosis is one of the major complications of inflammatory bowel disease (IBD) and a pathological process that significantly impacts patient prognosis and treatment selection. Although current imaging assessment and clinical markers are widely used for the diagnosis and stratification of fibrosis, these methods suffer from subjectivity and limitations. In this study, we aim to develop a radiomics diagnostic model based on multi-slice computed tomography (MSCT) and clinical factors. MSCT images and relevant clinical data were collected from 218 IBD patients, and a large number of quantitative image features were extracted. Using these features, we constructed a radiomics model and transformed it into a user-friendly diagnostic nomogram. A nomogram was developed to predict fibrosis in IBD by integrating multiple factors. The nomogram exhibited favorable discriminative ability, with an AUC of 0.865 in the validation sets, surpassing both the logistic regression (LR) model (AUC = 0.821) and the clinical model (AUC = 0.602) in the test set. In the train set, the LR model achieved an AUC of 0.975, while the clinical model had an AUC of 0.735. The nomogram demonstrated superior performance with an AUC of 0.971, suggesting its potential as a valuable tool for predicting fibrosis in IBD and improving clinical decision-making. The radiomics nomogram, incorporating MSCT and clinical factors, demonstrates promise in stratifying fibrosis in IBD. The nomogram outperforms traditional clinical models and offers personalized risk assessment. However, further validation and addressing identified limitations are necessary to enhance its applicability.

Integration and implementation of precision medicine in the multifaceted inflammatory bowel disease

Inflammatory bowel disease (IBD) is a complex disease with variability in genetic, environmental, and lifestyle factors affecting disease presentation and course. Precision medicine has the potential to play a crucial role in managing IBD by tailoring treatment plans based on the heterogeneity of clinical and temporal variability of patients. Precision medicine is a population-based approach to managing IBD by integrating environmental, genomic, epigenomic, transcriptomic, proteomic, and metabolomic factors. It is a recent and rapidly developing medicine. The widespread adoption of precision medicine worldwide has the potential to result in the early detection of diseases, optimal utilization of healthcare resources, enhanced patient outcomes, and, ultimately, improved quality of life for individuals with IBD. Though precision medicine is promising in terms of better quality of patient care, inadequacies exist in the ongoing research. There is discordance in study conduct, and data collection, utilization, interpretation, and analysis. This review aims to describe the current literature on precision medicine, its multiomics approach, and future directions for its application in IBD.

Radiomics could predict surgery at 10 years in Crohn's disease

BACKGROUND

Predicting clinical outcomes represents a major challenge in Crohn's disease (CD). Radiomics provides a method to extract quantitative features from medical images and may successfully predict clinical course.

AIMS

The aim of this pilot study is to evaluate the use of radiomics to predict 10-year surgery for CD patients.

METHODS

We selected a cohort of CD patients with CT scan enterographies and a 10-year follow up. The R library Moddicom was used to extract radiomic features from each lesion of CD, segmented in the CT scans. A logistic regression model based on selected radiomic features was developed to predict 10-year surgery. The model was evaluated by computing the area under the curve (AUC) of the receiver operating characteristic curve, sensitivity, specificity, positive and negative predictive values (PPV, NPV).

RESULTS

We enroled 30 patients, with 44 CT scans and 93 lesions. We extracted 217 radiomic features from each lesion. The developed model was based on two radiomic features and presented an AUC (95% CI) of 0.83 (0.73-0.91) in predicting 10-year surgery. Sensitivity, specificity, PPV, NPV of the radiomic model were equal to 0.72, 0.90, 0.79, 0.86, respectively.

CONCLUSION

Radiomics could be a helpful tool to identify patients with high risk for surgery and needing a stricter monitoring.

CB-HRNet: A Class-Balanced High-Resolution Network for the evaluation of endoscopic activity in patients with ulcerative colitis

Endoscopic evaluation is the key to the management of ulcerative colitis (UC). However, there is interobserver variability in interpreting endoscopic images among gastroenterologists. Furthermore, it is time-consuming. Convolutional neural networks (CNNs) can help overcome these obstacles and has yielded preliminary positive results. We aimed to develop a new CNN-based algorithm to improve the performance for evaluation tasks of endoscopic images in patients with UC. A total of 12,163 endoscopic images from 308 patients with UC were collected from January 2014 to December 2021. The training set and test set images were randomly divided into 37,515 and 3191 after excluding possible interference and data augmentation. Mayo Endoscopic Subscores (MES) were predicted by different CNN-based models with different loss functions. Their performances were evaluated by several metrics. After comparing the results of different CNN-based models with different loss functions, High-Resolution Network with Class-Balanced Loss achieved the best performances in all MES classification subtasks. It was especially great at determining endoscopic remission in UC, which achieved a high accuracy of 95.07% and good performances in other evaluation metrics with sensitivity 92.87%, specificity 95.41%, kappa coefficient 0.8836, positive predictive value 93.44%, negative predictive value 95.00% and area value under the receiver operating characteristic curve 0.9834, respectively. In conclusion, we proposed a new CNN-based algorithm, Class-Balanced High-Resolution Network (CB-HRNet), to evaluate endoscopic activity of UC with excellent performance. Besides, we made an open-source dataset and it can be a new benchmark in the task of MES classification.

Small bowel imaging in inflammatory bowel disease: updates for 2023

INTRODUCTION

Cross-sectional imaging techniques including MR and CT enterography and ultrasound are integral to Crohn's disease management, accurate, responsive, and well tolerated. They assess the full thickness of the bowel wall, perienteric environment, and distant complications. As we strive toward tighter disease control, imaging's role will expand further with transmural healing becoming an increasingly important therapeutic target.

AREAS COVERED

MEDLINE and Web of Science were searched from 2012 to 2023 inclusive. We review the evidence for cross-sectional imaging in assessing disease activity, phenotyping, and therapeutic response assessment. Emerging novel imaging applications such as quantifying enteric motility and fibrosis, prognostication, and potential utility of artificial intelligence will be covered. Recent international consensus statements highlight the need for standardized imaging reporting and definitions of transmural healing and remission. We will discuss how recent advances may be best integrated into patient care and highlight key outstanding research questions.

EXPERT OPINION

Cross-sectional imaging is established in Crohn's disease management. Research emphasis should be placed on optimal integration of imaging modalities in clinical care pathways, workforce training, definitions, and evidence for use of imaging based therapeutic targets such as transmural healing, better phenotyping of stricturing disease, and developing novel techniques, including integration of artificial intelligence.

How to Evaluate Fibrosis in IBD?

In this review, we will describe the importance of fibrosis in inflammatory bowel disease (IBD) by discussing its distinct impact on Crohn's disease (CD) and ulcerative colitis (UC) through their translation to histopathology. We will address the existing knowledge on the correlation between inflammation and fibrosis and the still not fully explained inflammation-independent fibrogenesis. Finally, we will compile and discuss the recent advances in the noninvasive assessment of intestinal fibrosis, including imaging and biomarkers. Based on the available data, none of the available cross-sectional imaging (CSI) techniques has proved to be capable of measuring CD fibrosis accurately, with MRE showing the most promising performance along with elastography. Very recent research with radiomics showed encouraging results, but further validation with reliable radiomic biomarkers is warranted. Despite the interesting results with micro-RNAs, further advances on the topic of fibrosis biomarkers depend on the development of robust clinical trials based on solid and validated endpoints. We conclude that it seems very likely that radiomics and AI will participate in the future non-invasive fibrosis assessment by CSI techniques in IBD. However, as of today, surgical pathology remains the gold standard for the diagnosis and quantification of intestinal fibrosis in IBD.

The Role of Radiomics in Fibrosis Crohn's Disease: A Review

Inflammatory bowel disease (IBD) is a global health concern that has been on the rise in recent years. In addition, imaging is the established method of care for detecting, diagnosing, planning treatment, and monitoring the progression of IBD. While conventional imaging techniques are limited in their ability to provide comprehensive information, cross-sectional imaging plays a crucial role in the clinical management of IBD. However, accurately characterizing, detecting, and monitoring fibrosis in Crohn's disease remains a challenging task for clinicians. Recent advances in artificial intelligence technology, machine learning, computational power, and radiomic emergence have enabled the automated evaluation of medical images to generate prognostic biomarkers and quantitative diagnostics. Radiomics analysis can be achieved via deep learning algorithms or by extracting handcrafted radiomics features. As radiomic features capture pathophysiological and biological data, these quantitative radiomic features have been shown to offer accurate and rapid non-invasive tools for IBD diagnostics, treatment response monitoring, and prognosis. For these reasons, the present review aims to provide a comprehensive review of the emerging radiomics methods in intestinal fibrosis research that are highlighted and discussed in terms of challenges and advantages.

Big Data in Gastroenterology Research

Studying individual data types in isolation provides only limited and incomplete answers to complex biological questions and particularly falls short in revealing sufficient mechanistic and kinetic details. In contrast, multi-omics approaches to studying health and disease permit the generation and integration of multiple data types on a much larger scale, offering a comprehensive picture of biological and disease processes. Gastroenterology and hepatobiliary research are particularly well-suited to such analyses, given the unique position of the luminal gastrointestinal (GI) tract at the nexus between the gut (mucosa and luminal contents), brain, immune and endocrine systems, and GI microbiome. The generation of 'big data' from multi-omic, multi-site studies can enhance investigations into the connections between these organ systems and organisms and more broadly and accurately appraise the effects of dietary, pharmacological, and other therapeutic interventions. In this review, we describe a variety of useful omics approaches and how they can be integrated to provide a holistic depiction of the human and microbial genetic and proteomic changes underlying physiological and pathophysiological phenomena. We highlight the potential pitfalls and alternatives to help avoid the common errors in study design, execution, and analysis. We focus on the application, integration, and analysis of big data in gastroenterology and hepatobiliary research.

Recent advances in clinical practice: advances in cross-sectional imaging in inflammatory bowel disease

Endoscopy remains the reference standard for the diagnosis and assessment of patients with inflammatory bowel disease (IBD), but it has several important limitations. Cross-sectional imaging techniques such as magnetic resonance enterography (MRE) and intestinal ultrasound (IUS) are better tolerated and safer. Moreover, they can examine the entire bowel, even in patients with stenoses and/or severe inflammation. A variety of cross-sectional imaging activity scores strongly correlate with endoscopic measures of mucosal inflammation in the colon and terminal ileum. Unlike endoscopy, cross-sectional techniques allow complete visualisation of the small-bowel and assess for extraintestinal disease, which occurs in nearly half of patients with IBD. Extramural findings may predict outcomes better than endoscopic mucosal assessment, so cross-sectional techniques might help identify more relevant therapeutic targets. Coupled with their high sensitivity, these advantages have made MRE and IUS the primary non-invasive options for diagnosing and monitoring Crohn's disease; they are appropriate first-line investigations, and have become viable alternatives to colonoscopy. This review discusses cross-sectional imaging in IBD in current clinical practice as well as research lines that will define the future role of these techniques.

Crohn's disease related strictures in cross-sectional imaging: More than meets the eye?

Strictures in Crohn's disease (CD) are a hallmark of long-standing intestinal damage, brought about by inflammatory and non-inflammatory pathways. Understanding the complex pathophysiology related to inflammatory infiltrates, extracellular matrix deposition, as well as muscular hyperplasia is crucial to produce high-quality scoring indices for assessing CD strictures. In addition, cross-sectional imaging modalities are the primary tool for diagnosis and follow-up of strictures, especially with the initiation of anti-fibrotic therapy clinical trials. This in turn requires such modalities to both diagnose strictures with high accuracy, as well as be able to delineate the impact of each histomorphologic component on the individual stricture. We discuss the current knowledge on cross-sectional imaging modalities used for stricturing CD, with an emphasis on histomorphologic correlates, novel imaging parameters which may improve segregation between inflammatory, muscular, and fibrotic stricture components, as well as a future outlook on the role of artificial intelligence in this field of gastroenterology.

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.

Assessing the inflammatory severity of the terminal ileum in Crohn disease using radiomics based on MRI

BACKGROUND

Evaluating inflammatory severity using imaging is essential for Crohn's disease, but it is limited by potential interobserver variation and subjectivity. We compared the efficiency of magnetic resonance index of activity (MaRIA) collected by radiologists and a radiomics model in assessing the inflammatory severity of terminal ileum (TI).

METHODS

121 patients were collected from two centers. Patients were divided into ulcerative group and mucosal remission group based on the TI Crohn's disease Endoscopic Severity Index. The consistency of bowel wall thickness (BWT), relative contrast enhancement (RCE), edema, ulcer, MaRIA and features of the region of interest between radiologists were described by weighted Kappa test and intraclass correlation coefficient (ICC), and developed receiver operating curve of MaRIA. The radiomics model was established using reproducible features of logistic regression based on arterial staging of T1WI sequences. Delong test was used to compare radiomics with MaRIA.

RESULTS

The consistency between radiologists were moderate in BWT (ICC = 0.638), fair in edema (κ = 0.541), RCE (ICC = 0.461), MaRIA (ICC = 0.579) and poor in ulcer (κ = 0.271). Radiomics model was developed by 6 reproducible features (ICC = 0.93-0.96) and equivalent to MaRIA which evaluated by the senior radiologist (0.872 vs 0.883 in training group, 0.824 vs 0.783 in validation group, P = 0.847, 0.471), both of which were significantly higher than MaRIA evaluated by junior radiologist (AUC: 0.621 in training group, 0.557 in validation group, all, P < 0.05).

CONCLUSION

The evaluation of inflammatory severity could be performed by radiomics objectively and reproducibly, and was comparable to MaRIA evaluated by the senior radiologist. Radiomics may be an important method to assist junior radiologists to assess the severity of inflammation objectively and accurately.

Imaging in inflammatory bowel disease: current and future perspectives

The use of cross-sectional imaging and ultrasonography has long complemented endoscopic assessment of inflammatory bowel disease (IBD). Clinical symptoms alone are often not enough to assess disease activity, so a reliance on non-invasive techniques is essential. In this paper, we aim to examine the current use of radiological modalities in aiding the management of patients with IBD. We focus on the various sections of the gastrointestinal tract and how different modalities can aid in assessing current disease state and response to treatments. We also have a look at how newer sequences in cross-sectional imaging and ultrasonography can allow for better differentiation of disease activity (ie, fibrotic vs inflammatory) as well improve evaluation of small bowel, colonic and perianal disease. Furthermore, we examine how advanced image processing has the potential to allow radiology to be a surrogate for biomarkers. An example of this is explored when reviewing the ability of MR sequences to quantify visceral fat, which potentially plays a role in determining disease activity in Crohn's disease. Lastly, we look into the expected role for artificial intelligence to be used as an adjunct to radiology to better improve IBD evaluation.

Artificial Intelligence Algorithm-Based Differential Diagnosis of Crohn's Disease and Ulcerative Colitis by CT Image

The aim of this study was to investigate the effect of low-dose CT enterography (CTE) based on modified guided image filtering (GIF) algorithm in the differential diagnosis of ulcerative colitis (UC) and Crohn's disease (CD). Methods. One hundred and twenty patients with suspected diagnosis of IBD were studied. They were randomly divided into control group (routine CT examination) and observation group (low-dose CTE examination based on improved GIF algorithm), with 60 cases in each group. Comprehensive diagnosis was used as the standard to assess the diagnostic effect. Results. (1) The peak signal-to-noise ratio (PSNR) (26.02 dB) and structural similarity (SSIM) (0.8921) of the algorithm were higher than those of GIF (17.22 dB/0.8491), weighted guided image filtering (WGIF) (23.78 dB/0.8489), and gradient domain guided image filtering (GGIF) (23.77 dB/0.7567) (P < 0.05); (2) the diagnostic sensitivity (91.49%), specificity (92.31%), accuracy (91.67%), positive predictive value (97.73%), and negative predictive value (75%) of the observation group were higher than those of the control group (P < 0.05); the sensitivity and specificity of CTE in the diagnosis of UD and CD were 96.77% and 81.25% and 98.33% and 93.33%, respectively (P < 0.05); there were significant differences in symmetrical intestinal wall thickening and smooth serosal surface between UD and CD (P < 0.05). Conclusion. (1) The improved GIF algorithm has a more effective application value in the denoising processing of low-dose CT images and can better improve the image quality; (2) the accuracy of CTE in the diagnosis of IBD is high, and CTE is of great value in the differential diagnosis of UD and CD.

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

Artificial intelligence (AI) has arrived and it will directly impact how we assess, monitor, and manage inflammatory bowel disease (IBD). Advances in the machine learning methodologies that power AI have produced astounding results for replicating expert judgment and predicting clinical outcomes, particularly in the analysis of imaging. This review will cover general concepts for AI in IBD, with descriptions of common machine learning methods, including decision trees and neural networks. Applications of AI in IBD will cover recent achievements in endoscopic image interpretation and scoring, new capabilities for cross-sectional image analysis, natural language processing for automated understanding of clinical text, and progress in AI-powered clinical decision support tools. In addition to detailing current evidence supporting the capabilities of AI for replicating expert clinical judgment, speculative commentary on how AI may advance concepts of disease activity assessment, care pathways, and pathophysiologic mechanisms of IBD will be addressed.

Cross-Sectional Imaging Instead of Colonoscopy in Inflammatory Bowel Diseases: Lights and Shadows

International guidelines recommend a treat-to-target strategy with a close monitoring of disease activity and therapeutic response in inflammatory bowel diseases (IBD). Colonoscopy (CS) represents the current first-line procedure for evaluating disease activity in IBD. However, as it is expensive, invasive and poorly accepted by patients, CS is not appropriate for frequent and repetitive reassessments of disease activity. Recently, cross-sectional imaging techniques have been increasingly shown as reliable tools for assessing IBD activity. While computed tomography (CT) is hampered by radiation risks, routine implementation of magnetic resonance enterography (MRE) for close monitoring is limited by its costs, low availability and long examination time. Novel magnetic resonance imaging (MRI)-based techniques, such as diffusion-weighted imaging (DWI), can overcome some of these weaknesses and have been shown as valuable options for IBD monitoring. Bowel ultrasound (BUS) is a noninvasive, highly available, cheap, and well accepted procedure that has been demonstrated to be as accurate as CS and MRE for assessing and monitoring disease activity in IBD. Furthermore, as BUS can be quickly performed at the point-of-care, it allows for real-time clinical decision making. This review summarizes the current evidence on the use of cross-sectional imaging techniques as cost-effective, noninvasive and reliable alternatives to CS for monitoring patients with IBD.

Charting a path forward for clinical research in artificial intelligence and gastroenterology

Gastroenterology has been an early leader in bridging the gap between artificial intelligence (AI) model development and clinical trial validation, and in recent years we have seen the publication of several randomized clinical trials examining the role of AI in gastroenterology. As AI applications for clinical medicine advance rapidly, there is a clear need for guidance surrounding AI-specific study design, evaluation, comparison, analysis and reporting of results. Several initiatives are in the publication or pre-publication phase including AI-specific amendments to minimum reporting guidelines for clinical trials, society task force initiatives aimed at priority use cases and research priorities, and minimum reporting guidelines that guide the reporting of clinical prediction models. In this paper, we examine applications of AI in clinical trials and discuss elements of newly published AI-specific extensions to the Consolidated Standards of Reporting Trials and Standard Protocol Items: Recommendations for Interventional Trials statements that guide clinical trial reporting and development. We then review AI applications at the pre-trial level in both endoscopy and other subfields of gastroenterology and explore areas where further guidance is needed to supplement the current guidance available at the pre-trial level.

Artificial intelligence and inflammatory bowel disease: practicalities and future prospects

Artificial intelligence (AI) is an emerging technology predicted to have significant applications in healthcare. This review highlights AI applications that impact the patient journey in inflammatory bowel disease (IBD), from genomics to endoscopic applications in disease classification, stratification and self-monitoring to risk stratification for personalised management. We discuss the practical AI applications currently in use while giving a balanced view of concerns and pitfalls and look to the future with the potential of where AI can provide significant value to the care of the patient with IBD.

Artificial intelligence in gastroenterology: A state-of-the-art review

The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.

Adoption of New Technologies: Artificial Intelligence

Over the past decade, artificial intelligence (AI) has been broadly applied to many aspects of human life, with recent groundbreaking successes in facial recognition, natural language processing, autonomous driving, and medical imaging. Gastroenterology has applied AI to a vast array of clinical problems, and some of the earliest prospective trials examining AI in medicine have been in computer vision applied to endoscopy. Evidence is mounting for 2 broad areas of AI as applied to gastroenterology: computer-aided detection and computer-aided diagnosis.

Application of Artificial Intelligence to Clinical Practice in Inflammatory Bowel Disease - What the Clinician Needs to Know

Artificial intelligence [AI] techniques are quickly spreading across medicine as an analytical method to tackle challenging clinical questions. What were previously thought of as highly complex data sources, such as images or free text, are now becoming manageable. Novel analytical methods merge the latest developments in information technology infrastructure with advances in computer science. Once primarily associated with Silicon Valley, AI techniques are now making their way into medicine, including in the field of inflammatory bowel diseases [IBD]. Understanding potential applications and limitations of these techniques can be difficult, in particular for busy clinicians. In this article, we explain the basic terminologies and provide a particular focus on the foundations behind state-of-the-art AI methodologies in both imaging and text. We explore the growing applications of AI in medicine, with a specific focus on IBD to inform the practising gastroenterologist and IBD specialist. Finally, we outline possible future uses of these technologies in daily clinical practice.

Implications of artificial intelligence in inflammatory bowel disease: Diagnosis, prognosis and treatment follow up

Driven by the tremendous availability of data, artificial intelligence (AI) using deep learning has emerged as a breakthrough computer technology in the last few decades and has recently been acknowledged by the Task Force on AI as a golden opportunity for research. With its ability to understand, learn from and build on non-linear relationships, AI aims to individualize medical care in an attempt to save time, cost, effort and improve patient’s safety. AI has been applied in multiple medical fields with substantial progress made in gastroenterology mainly to facilitate accurate detection of pathology in different disease processes, among which inflammatory bowel disease (IBD) seems to drag significant attention, specifically by interpreting imaging studies, endoscopic images and videos and -to a lesser extent- disease genomics. Moreover, models have been built to predict IBD occurrence, flare ups, persistence of histological inflammation, disease-related structural abnormalities as well as disease remission. In this article, we will review the applications of AI in IBD in the present medical literature at multiple points of IBD timeline, starting from disease prediction via genomic assessment, diagnostic phase via interpretation of radiological studies and AI-assisted endoscopy, and the role of AI in the evaluation of therapy response and prognosis of IBD patients.

Emerging Imaging Biomarkers in Crohn Disease

ABSTRACT

In this review article, we present the latest developments in quantitative imaging biomarkers based on magnetic resonance imaging (MRI), applied to the diagnosis, assessment of response to therapy, and assessment of prognosis of Crohn disease. We also discuss the biomarkers' limitations and future prospects. We performed a literature search of clinical and translational research in Crohn disease using diffusion-weighted MRI (DWI-MRI), dynamic contrast-enhanced MRI (DCE-MRI), motility MRI, and magnetization transfer MRI, as well as emerging topics such as T1 mapping, radiomics, and artificial intelligence. These techniques are integrated in and combined with qualitative image assessment of magnetic resonance enterography (MRE) examinations. Quantitative MRI biomarkers add value to MRE qualitative assessment, achieving substantial diagnostic performance (area under receiver-operating curve = 0.8-0.95). The studies reviewed show that the combination of multiple MRI sequences in a multiparametric quantitative fashion provides rich information that may help for better diagnosis, assessment of severity, prognostication, and assessment of response to biological treatment. However, the addition of quantitative sequences to MRE examinations has potential drawbacks, including increased scan time and the need for further validation before being used in therapeutic drug trials as well as the clinic.

Small bowel radiology

PURPOSE OF REVIEW

Recent advances in computed tomography (CT), ultrasound (US), magnetic resonance imaging (MRI), and nuclear radiology have improved the diagnosis and characterization of small bowel pathology. Our purpose is to highlight the current status and recent advances in multimodality noninvasive imaging of the small bowel.

RECENT FINDINGS

CT and MR enterography are established techniques for small bowel evaluation. Dual-energy CT is a novel technique that has shown promise for the mesenteric ischemia and small bowel bleeding. Advanced US techniques and MRI sequences are being investigated to improve assessment of bowel inflammation, treatment response assessment, motility, and mural fibrosis. Novel radiotracers and scanner technologies have made molecular imaging the new reference standard for small bowel neuroendocrine tumors. Computational image analysis and artificial intelligence (AI) have the potential to augment physician expertise, reduce errors and variability in assessment of the small bowel on imaging.

SUMMARY

Advances in translational imaging research coupled with progress in imaging technology have led to a wider adoption of cross-sectional imaging for the evaluation and management of small bowel entities. Ongoing developments in image acquisition and postprocessing techniques, molecular imaging and AI have the strongest potential to transform the care and outcomes of patients with small bowel diseases.

An MSCT-based radiomics nomogram combined with clinical factors can identify Crohn's disease and ulcerative colitis

Background

We established and evaluated a radiomics nomogram based on multislice computed tomography (MSCT) arterial phase contrast-enhanced images to distinguish between Crohn's disease (CD) and ulcerative colitis (UC) objectively, quantitatively, and reproducibly.

Methods

MSCT arterial phase-enhancement images of 165 lesions (99 CD, 66 UC) in 87 patients with inflammatory bowel disease (IBD) confirmed by endoscopy or surgical pathology were retrospectively analyzed. A total of 132 lesions (80%) were selected as the training cohort and 33 lesions (20%) as the test cohort. A total of 1648 radiomic features were extracted from each region of interest (ROI), and the Pearson correlation coefficient and tree-based method were used for feature selection. Five machine learning classifiers, including logistic regression (LR), support vector machine (SVM), random forest (RF), stochastic gradient descent (SGD), and linear discriminative analysis (LDA), were trained. The best classifier was evaluated and obtained, and the results were transformed into the Rscore. Three clinical factors were screened out from 8 factors by univariate analysis. The logistic regression method was used to synthesize the significant clinical factors and the Rscore to generate the nomogram, which was compared with the clinical model and LR model.

Results

Among all machine learning classifiers, LR performed the best (AUC =0.8077, accuracy =0.697, sensitivity =0.8, specificity =0.5385), SGD model had the second best performance (AUC =0.8, accuracy =0.6667, sensitivity =0.75, specificity =0.5385), and the DeLong test results showed that there was no significant difference between LR and SGD (P=0.465>0.05), while the other models performed poorly. Texture features had the greatest impact on classification results among all imaging features. The significant features of the LR model were used to calculate the Rscore. The 3 significant clinical factors were perienteric edema or inflammation, CT value of arterial phase-enhancement (AP-CT value), and lesion location. Finally, a nomogram was constructed based on the 3 significant clinical factors and the Rscore, whose AUC (0.8846) was much higher than that of the clinical model (0.6154) and the LR model (0.8077).

Conclusions

The nomogram is expected to provide a new auxiliary tool for radiologists to quickly identify CD and UC.

Clinical applications of artificial intelligence and machine learning-based methods in inflammatory bowel disease

Our objective was to review and exemplify how selected applications of artificial intelligence (AI) might facilitate and improve inflammatory bowel disease (IBD) care and to identify gaps for future work in this field. IBD is highly complex and associated with significant variation in care and outcomes. The application of AI to IBD has the potential to reduce variation in healthcare delivery and improve quality of care. AI refers to the ability of machines to mimic human intelligence. The range of AI's ability to perform tasks that would normally require human intelligence varies from prediction to complex decision-making that more closely resembles human thought. Clinical applications of AI have been applied to study pathogenesis, diagnosis, and patient prognosis in IBD. Despite these advancements, AI in IBD is in its early development and has tremendous potential to transform future care.

Association of Baseline Luminal Narrowing With Ileal Microbial Shifts and Gene Expression Programs and Subsequent Transmural Healing in Pediatric Crohn Disease

BACKGROUND

Transmural healing (TH) is associated with better long-term outcomes in Crohn disease (CD), whereas pretreatment ileal gene signatures encoding myeloid inflammatory responses and extracellular matrix production are associated with stricturing. We aimed to develop a predictive model for ileal TH and to identify ileal genes and microbes associated with baseline luminal narrowing (LN), a precursor to strictures.

MATERIALS AND METHODS

Baseline small bowel imaging obtained in the RISK pediatric CD cohort study was graded for LN. Ileal gene expression was determined by RNASeq, and the ileal microbial community composition was characterized using 16S rRNA amplicon sequencing. Clinical, demographic, radiologic, and genomic variables were tested for association with baseline LN and future TH.

RESULTS

After controlling for ileal location, baseline ileal LN (odds ratio [OR], 0.3; 95% confidence interval [CI], 0.1-0.8), increasing serum albumin (OR, 4; 95% CI, 1.3-12.3), and anti-Saccharomyces cerevisiae antibodies IgG serology (OR, 0.97; 95% CI, 0.95-1) were associated with subsequent TH. A multivariable regression model including these factors had excellent discriminant power for TH (area under the curve, 0.86; positive predictive value, 80%; negative predictive value, 87%). Patients with baseline LN exhibited increased Enterobacteriaceae and inflammatory and extracellular matrix gene signatures, coupled with reduced levels of butyrate-producing commensals and a respiratory electron transport gene signature. Taxa including Lachnospiraceae and the genus Roseburia were associated with increased respiratory and decreased inflammatory gene signatures, and Aggregatibacter and Blautia bacteria were associated with reduced extracellular matrix gene expression.

CONCLUSIONS

Pediatric patients with CD with LN at diagnosis are less likely to achieve TH. The association between specific microbiota, wound healing gene programs, and LN may suggest future therapeutic targets.

Emerging use of artificial intelligence in inflammatory bowel disease

Inflammatory bowel disease (IBD) is a complex, immune-mediated gastrointestinal disorder with ill-defined etiology, multifaceted diagnostic criteria, and unpredictable treatment response. Innovations in IBD diagnostics, including developments in genomic sequencing and molecular analytics, have generated tremendous interest in leveraging these large data platforms into clinically meaningful tools. Artificial intelligence, through machine learning facilitates the interpretation of large arrays of data, and may provide insight to improving IBD outcomes. While potential applications of machine learning models are vast, further research is needed to generate standardized models that can be adapted to target IBD populations. 

Big data in IBD: big progress for clinical practice

IBD is a complex multifactorial inflammatory disease of the gut driven by extrinsic and intrinsic factors, including host genetics, the immune system, environmental factors and the gut microbiome. Technological advancements such as next-generation sequencing, high-throughput omics data generation and molecular networks have catalysed IBD research. The advent of artificial intelligence, in particular, machine learning, and systems biology has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically translatable knowledge. In this narrative review, we discuss how big data integration and machine learning have been applied to translational IBD research. Approaches such as machine learning may enable patient stratification, prediction of disease progression and therapy responses for fine-tuning treatment options with positive impacts on cost, health and safety. We also outline the challenges and opportunities presented by machine learning and big data in clinical IBD research.

Artificial Intelligence for Understanding Imaging, Text, and Data in Gastroenterology

Artificial intelligence (AI) could change the practice of gastroenterology through its ability to both acquire and analyze information with speed, reproducibility, and, potentially, insight that may exceed that of human medical specialists. AI is powered by computational methods that allow machines to replicate clinical pattern recognition used by gastroenterology specialists to interpret endoscopic or cross-sectional images; understand the meaning and intent of medical documents; and merge different types of data to infer a diagnosis, prognosis, or expected outcome. Ongoing research is studying the use of AI for automated interpretation of text from colonoscopy and clinical documents for improved quality and patient phenotyping as well as enhanced detection and descriptions of polyps and other endoscopic lesions, and for predicting the probability of future therapeutic response early in a treatment course. This article introduces emerging technologies of natural language processing, machine vision, and machine learning for data analytics, and describes current and future applications in gastroenterology.

Surgical treatment of intestinal stricture in inflammatory bowel disease

Fibroblast infiltration and collagen deposition result in structural changes in the bowel wall, and lead to strictures in intestinal inflammatory disease. While strictures can also occur in other contexts, such as malignancy, this review focuses on the surgical treatment of stricture secondary to inflammatory bowel disease. Distinguishing between predominantly inflammation vs established fibrosis as the cause of a stricture can be challenging. While inflammatory strictures may be responsive to medication, predominantly fibrotic strictures usually need surgical intervention. Both endoluminal and extraluminal approaches are described in this review. Endoscopic dilatation of strictures is suitable for short-segment isolated small bowel strictures. Other options are to divide the stricture surgically but preserve the length, performing a strictureplasty or resecting the strictured segment. The mesentery is increasingly recognized as playing a role in stricture recurrence. In a relapsing-remitting disease such as Crohn's disease, the preservation of intestinal length is essential and balance is needed between this and a complete resection to reduce the risk of recurrence. Pre- and postoperative involvement of the multidisciplinary team is essential to improve outcomes in this challenging clinical scenario.