Quantitative assessment of mucosal architecture using computer-based analysis of confocal laser endomicroscopy in inflammatory bowel diseases

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.

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

Artificial intelligence (AI) refers to computer-based methodologies that 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.

The development of artificial intelligence in the histological diagnosis of Inflammatory Bowel Disease (IBD-AI)

BACKGROUND

Inflammatory bowel disease (IBD) includes Crohn's Disease (CD) and Ulcerative Colitis (UC). Correct diagnosis requires the identification of precise morphological features such basal plasmacytosis. However, histopathological interpretation can be challenging, and it is subject to high variability.

AIM

The IBD-Artificial Intelligence (AI) project aims at the development of an AI-based evaluation system to support the diagnosis of IBD, semi-automatically quantifying basal plasmacytosis.

METHODS

A deep learning model was trained to detect and quantify plasma cells on a public dataset of 4981 annotated images. The model was then tested on an external validation cohort of 356 intestinal biopsies of CD, UC and healthy controls. AI diagnostic performance was calculated compared to human gold standard.

RESULTS

The system correctly found that CD and UC samples had a greater prevalence of basal plasma cells with mean number of PCs within ROIs of 38.22 (95 % CI: 31.73, 49.04) for CD, 55.16 (46.57, 65.93) for UC, and 17.25 (CI: 12.17, 27.05) for controls. Overall, OR=4.968 (CI: 1.835, 14.638) was found for IBD compared to normal mucosa (CD: +59 %; UC: +129 %). Additionally, as expected, UC samples were found to have more plasma cells in colon than CD cases.

CONCLUSION

Our model accurately replicated human assessment of basal plasmacytosis, underscoring the value of AI models as a potential aid IBD diagnosis.

Role of ultra-high definition endoscopy (endomicroscopy and endocytoscopy) and real-time histologic examination in inflammatory bowel disease: Scoping review

OBJECTIVES

Confocal laser endomicroscopy (CLE) and endocytoscopy (EC) are ultra-high definition (HD) imaging modalities that enable real-time histological assessment. Although existent for nearly two decades, their role in current clinical decision making in inflammatory bowel disease management is not well defined.

METHODS

We searched PubMed using keywords ("confocal" OR "CLE" OR "endocytoscopy") AND ("IBD" OR "inflammatory bowel" OR "Crohn*" OR "Crohn's" OR "colitis ulcerosa" OR "ulcerative colitis") between 2005 and March 2023. We identified 52 studies for detailed review.

RESULTS

Confocal laser endomicroscopy was useful in real-time assessment of histologic inflammation and dysplasia characterization in both ulcerative colitis (UC) and Crohn's disease. Although CLE was associated with higher per-biopsy yield for UC-associated neoplasia (UCAN), the benefit was offset by higher procedure time, frequent equipment failure, and conflicting results on incremental yield over chromoendoscopy. Assessment of barrier dysfunction by CLE did not correlate with disease/endoscopic activity but could predict major adverse outcomes. The implications of residual CLE abnormalities in endoscopic remission remain uncertain. Ex vivo binding of labeled biologics can help in predicting biologic response in UC. EC can discriminate mucosal inflammatory cells by morphology and allows assessment of histologic activity. EC combined with pit pattern was better than pit pattern alone for UCAN. Artificial intelligence-assisted EC in UCAN needs further study.

CONCLUSION

Ultra-HD imaging in inflammatory bowel disease can be useful in assessment of UCAN, barrier dysfunction, predicting histologic remission, and biologic response. Future controlled studies are warranted to define the role of these novel technologies in clinical decision making.

Artificial intelligence in endoscopy related to inflammatory bowel disease: A systematic review

BACKGROUND AND OBJECTIVES

In spite of rapid growth of artificial intelligence (AI) in digestive endoscopy in lesion detection and characterization, the role of AI in inflammatory bowel disease (IBD) endoscopy is not clearly defined. We aimed at systematically reviewing the role of AI in IBD endoscopy and identifying future research areas.

METHODS

We searched the PubMed and Embase database using keywords ("artificial intelligence" OR "machine learning" OR "computer-aided" OR "convolutional neural network") AND ("inflammatory bowel disease" OR "ulcerative colitis" OR "Crohn's") AND ("endoscopy" or "colonoscopy" or "capsule endoscopy" or "device assisted enteroscopy") between 1975 and September 2023 and identified 62 original articles for detailed review. Review articles, consensus guidelines, case reports/series, editorials, letter to the editor, non-peer-reviewed pre-prints and conference abstracts were excluded. The quality of the included studies was assessed using the MI-CLAIM checklist.

RESULTS

The accuracy of AI models (25 studies) to assess ulcerative colitis (UC) endoscopic activity ranged between 86.54% and 94.5%. AI-assisted capsule endoscopy reading (12 studies) substantially reduced analyzable images and reading time with excellent accuracy (90.5% to 99.9%). AI-assisted analysis of colonoscopic images can help differentiate IBD from non-IBD, UC from non-UC and UC from Crohn's disease (CD) (three studies) with 72.1%, 98.3% and > 90% accuracy, respectively. AI models based on non-invasive clinical and radiologic parameters could predict endoscopic activity (three studies). AI-assisted virtual chromoendoscopy (four studies) could predict histologic remission and long-term outcomes. Computer-assisted detection (CADe) of dysplasia (two studies) is feasible along with AI-based differentiation of high from low-grade IBD neoplasia (79% accuracy). AI is effective in linking electronic medical record data (two studies) with colonoscopic videos to facilitate widespread machine learning.

CONCLUSION

AI-assisted IBD endoscopy has the potential to impact clinical management by automated detection and characterization of endoscopic lesions. Large, multi-center, prospective studies and commercially available IBD-specific endoscopic AI algorithms are warranted.

Machine learning application in autoimmune diseases: State of art and future prospectives

Autoimmune diseases are a group of disorders resulting from an alteration of immune tolerance, characterized by the formation of autoantibodies and the consequent development of heterogeneous clinical manifestations. Diagnosing autoimmune diseases is often complicated, and the available prognostic tools are limited. Machine learning allows us to analyze large amounts of data and carry out complex calculations quickly and with minimal effort. In this work, we examine the literature focusing on the use of machine learning in the field of the main systemic (systemic lupus erythematosus and rheumatoid arthritis) and organ-specific autoimmune diseases (type 1 diabetes mellitus, autoimmune thyroid, gastrointestinal, and skin diseases). From our analysis, interesting applications of machine learning emerged for developing algorithms useful in the early diagnosis of disease or prognostic models (risk of complications, therapeutic response). Subsequent studies and the creation of increasingly rich databases to be supplied to the algorithms will eventually guide the clinician in the diagnosis, allowing intervention when the pathology is still in an early stage and immediately directing towards a correct therapeutic approach.

Utility of four machine learning approaches for identifying ulcerative colitis and Crohn's disease

Objective

Peripheral blood routine parameters (PBRPs) are simple and easily acquired markers to identify ulcerative colitis (UC) and Crohn's disease (CD) and reveal the severity, whereas the diagnostic performance of individual PBRP is limited. We, therefore used four machine learning (ML) models to evaluate the diagnostic and predictive values of PBRPs for UC and CD.

Methods

A retrospective study was conducted by collecting the PBRPs of 414 inflammatory bowel disease (IBD) patients, 423 healthy controls (HCs), and 344 non-IBD intestinal diseases (non-IBD) patients. We used approximately 70 % of the PBRPs data from both patients and HCs for training, 30 % for testing, and another group for external verification. The area under the receiver operating characteristic curve (AUC) was used to evaluate the diagnosis and prediction performance of these four ML models.

Results

Multi-layer perceptron artificial neural network model (MLP-ANN) yielded the highest diagnostic performance than the other three models in six subgroups in the training set, which is helpful for discriminating IBD and HCs, UC and CD, active CD and remissive CD, active UC and remissive UC, non-IBD and HCs, and IBD and non-IBD with the AUC of 1.00, 0.988, 0.942, 1.00, 0.986, and 0.97 in the testing set, as well as the AUC of 1.00, 1.00, 0.773, 0.904, 1.00 and 0.992 in the external validation set.

Conclusion

PBRPs-based MLP-ANN model exhibited good performance in discriminating between UC and CD and revealing the disease activity; however, a larger sample size and more models need to be considered for further research.

Machine Learning-Based Prediction of Responsiveness to Neoadjuvant Chemoradiotheapy in Locally Advanced Rectal Cancer Patients from Endomicroscopy

The protocol for treating locally advanced rectal cancer consists of the application of chemoradiotherapy (neoCRT) followed by surgical intervention. One issue for clinical oncologists is predicting the efficacy of neoCRT in order to adjust the dosage and avoid treatment toxicity in cases when surgery should be conducted promptly. Biomarkers may be used for this purpose along with in vivo cell-level images of the colorectal mucosa obtained by probe-based confocal laser endomicroscopy (pCLE) during colonoscopy. The aim of this article is to report our experience with Motiro, a computational framework that we developed for machine learning (ML) based analysis of pCLE videos for predicting neoCRT response in locally advanced rectal cancer patients. pCLE videos were collected from 47 patients who were diagnosed with locally advanced rectal cancer (T3/T4, or N+). The patients received neoCRT. Response to treatment by all patients was assessed by endoscopy along with biopsy and magnetic resonance imaging (MRI). Thirty-seven patients were classified as non-responsive to neoCRT because they presented a visible macroscopic neoplastic lesion, as confirmed by pCLE examination. Ten remaining patients were considered responsive to neoCRT because they presented lesions as a scar or small ulcer with negative biopsy, at post-treatment follow-up. Motiro was used for batch mode analysis of pCLE videos. It automatically characterized the tumoral region and its surroundings. That enabled classifying a patient as responsive or non-responsive to neoCRT based on pre-neoCRT pCLE videos. Motiro classified patients as responsive or non-responsive to neoCRT with an accuracy of ~ 0.62 when using images of the tumor. When using images of regions surrounding the tumor, it reached an accuracy of ~ 0.70. Feature analysis showed that spatial heterogeneity in fluorescence distribution within regions surrounding the tumor was the main contributor to predicting response to neoCRT. We developed a computational framework to predict response to neoCRT by locally advanced rectal cancer patients based on pCLE images acquired pre-neoCRT. We demonstrate that the analysis of the mucosa of the region surrounding the tumor provides stronger predictive power.

Different levels of healing in inflammatory bowel diseases: mucosal, histological, transmural, barrier and complete healing

Mucosal healing on endoscopy has emerged as a key prognostic parameter in the management of patients with IBD (Crohn's disease, ulcerative colitis/UC) and can predict sustained clinical remission and resection-free survival. The structural basis for this type of mucosal healing is a progressive resolution of intestinal inflammation with associated healing of ulcers and improved epithelial barrier function. However, in some cases with mucosal healing on endoscopy, evidence of histological activity in mucosal biopsies has been observed. Subsequently, in UC, a second, deeper type of mucosal healing, denoted histological healing, was defined which requires the absence of active inflammation in mucosal biopsies. Both levels of mucosal healing should be considered as initial events in the resolution of gut inflammation in IBD rather than as indicators of complete transmural healing. In this review, the effects of anti-inflammatory, biological or immunosuppressive agents as well as small molecules on mucosal healing in clinical studies are highlighted. In addition, we focus on the implications of mucosal healing for clinical management of patients with IBD. Moreover, emerging techniques for the analysis of mucosal healing as well as potentially deeper levels of mucosal healing such as transmural healing and functional barrier healing of the mucosa are discussed. Although none of these new levels of healing indicate a definitive cure of the diseases, they make an important contribution to the assessment of patients' prognosis. The ultimate level of healing in IBD would be a resolution of all aspects of intestinal and extraintestinal inflammation (complete healing).

Computer-Aided Imaging Analysis of Probe-Based Confocal Laser Endomicroscopy With Molecular Labeling and Gene Expression Identifies Markers of Response to Biological Therapy in IBD Patients: The Endo-Omics Study

BACKGROUND

We aimed to predict response to biologics in inflammatory bowel disease (IBD) using computerized image analysis of probe confocal laser endomicroscopy (pCLE) in vivo and assess the binding of fluorescent-labeled biologics ex vivo. Additionally, we investigated genes predictive of anti-tumor necrosis factor (TNF) response.

METHODS

Twenty-nine patients (15 with Crohn's disease [CD], 14 with ulcerative colitis [UC]) underwent colonoscopy with pCLE before and 12 to 14 weeks after starting anti-TNF or anti-integrin α4β7 therapy. Biopsies were taken for fluorescein isothiocyanate-labeled infliximab and vedolizumab staining and gene expression analysis. Computer-aided quantitative image analysis of pCLE was performed. Differentially expressed genes predictive of response were determined and validated in a public cohort.

RESULTS

In vivo, vessel tortuosity, crypt morphology, and fluorescein leakage predicted response in UC (area under the receiver-operating characteristic curve [AUROC], 0.93; accuracy 85%, positive predictive value [PPV] 89%; negative predictive value [NPV] 75%) and CD (AUROC, 0.79; accuracy 80%; PPV 75%; NPV 83%) patients. Ex vivo, increased binding of labeled biologic at baseline predicted response in UC (UC) (AUROC, 83%; accuracy 77%; PPV 89%; NPV 50%) but not in Crohn's disease (AUROC 58%). A total of 325 differentially expressed genes distinguished responders from nonresponders, 86 of which fell within the most enriched pathways. A panel including ACTN1, CXCL6, LAMA4, EMILIN1, CRIP2, CXCL13, and MAPKAPK2 showed good prediction of anti-TNF response (AUROC >0.7).

CONCLUSIONS

Higher mucosal binding of the drug target is associated with response to therapy in UC. In vivo, mucosal and microvascular changes detected by pCLE are associated with response to biologics in inflammatory bowel disease. Anti-TNF-responsive UC patients have a less inflamed and fibrotic state pretreatment. Chemotactic pathways involving CXCL6 or CXCL13 may be novel targets for therapy in nonresponders.

New Technologies in Digestive Endoscopy for Ulcerative Colitis Patients

Ulcerative colitis (UC) is a chronic inflammatory bowel disease primarily affecting the colon and rectum. Endoscopy plays a crucial role in the diagnosis and management of UC. Recent advancements in endoscopic technology, including chromoendoscopy, confocal laser endomicroscopy, endocytoscopy and the use of artificial intelligence, have revolutionized the assessment and treatment of UC patients. These innovative techniques enable early detection of dysplasia and cancer, more precise characterization of disease extent and severity and more targeted biopsies, leading to improved diagnosis and disease monitoring. Furthermore, these advancements have significant implications for therapeutic decision making, empowering clinicians to carefully consider a range of treatment options, including pharmacological therapies, endoscopic interventions and surgical approaches. In this review, we provide an overview of the latest endoscopic technologies and their applications for diagnosing and monitoring UC. We also discuss their impact on treatment decision making, highlighting the potential benefits and limitations of each technique.

The Role of Artificial Intelligence in Monitoring Inflammatory Bowel Disease-The Future Is Now

Crohn's disease and ulcerative colitis remain debilitating disorders, characterized by progressive bowel damage and possible lethal complications. The growing number of applications for artificial intelligence in gastrointestinal endoscopy has already shown great potential, especially in the field of neoplastic and pre-neoplastic lesion detection and characterization, and is currently under evaluation in the field of inflammatory bowel disease management. The application of artificial intelligence in inflammatory bowel diseases can range from genomic dataset analysis and risk prediction model construction to the disease grading severity and assessment of the response to treatment using machine learning. We aimed to assess the current and future role of artificial intelligence in assessing the key outcomes in inflammatory bowel disease patients: endoscopic activity, mucosal healing, response to treatment, and neoplasia surveillance.

Intestinal Barrier Healing Is Superior to Endoscopic and Histologic Remission for Predicting Major Adverse Outcomes in Inflammatory Bowel Disease: The Prospective ERIca Trial

BACKGROUND & AIMS

Endoscopic and histologic remission have emerged as key therapeutic goals in the management of inflammatory bowel diseases (IBD) that are associated with favorable long-term disease outcomes. Here, we prospectively compared the predictive value of barrier healing with endoscopic and histologic remission for predicting long-term disease behavior in a large cohort of patients with IBD in clinical remission.

METHODS

At baseline, patients with IBD in clinical remission underwent ileocolonoscopy with assessment of intestinal barrier function by confocal endomicroscopy. Endoscopic and histologic disease activity, as well as barrier healing, was prospectively assessed along established scores. During subsequent follow-up, patients were closely monitored for clinical disease activity and the occurrence of major adverse outcomes (MAOs): disease flares, IBD-related hospitalization or surgery, and initiation or dose escalation of systemic steroids, immunosuppressants, small molecules, or biological therapy.

RESULTS

The final analysis included 181 patients, 100 with Crohn's disease [CD] and 81 with ulcerative colitis (UC). During a mean follow-up of 35 (CD) and 25 (UC) months, 73% of patients with CD and 69% of patients with UC experienced at least 1 MAO. The probability of MAO-free survival was significantly higher in patients with IBD with endoscopic remission compared with endoscopically active disease. In addition, histologic remission predicted MAO-free survival in patients with UC but not CD. Barrier healing on endomicroscopy was superior to endoscopic and histologic remission for predicting MAO-free survival in both UC and CD.

CONCLUSIONS

Barrier healing is associated with decreased risk of disease progression in patients with clinically remittent IBD, with superior predictive performance compared with endoscopic and histologic remission. Analysis of barrier function might be considered as a future treatment target in clinical trials.

CLINICALTRIALS

gov number, NCT05157750.

Artificial intelligence and inflammatory bowel disease: Where are we going?

Inflammatory bowel diseases, namely ulcerative colitis and Crohn’s disease, are chronic and relapsing conditions that pose a growing burden on healthcare systems worldwide. Because of their complex and partly unknown etiology and pathogenesis, the management of ulcerative colitis and Crohn’s disease can prove challenging not only from a clinical point of view but also for resource optimization. Artificial intelligence, an umbrella term that encompasses any cognitive function developed by machines for learning or problem solving, and its subsets machine learning and deep learning are becoming ever more essential tools with a plethora of applications in most medical specialties. In this regard gastroenterology is no exception, and due to the importance of endoscopy and imaging numerous clinical studies have been gradually highlighting the relevant role that artificial intelligence has in inflammatory bowel diseases as well. The aim of this review was to summarize the most recent evidence on the use of artificial intelligence in inflammatory bowel diseases in various contexts such as diagnosis, follow-up, treatment, prognosis, cancer surveillance, data collection, and analysis. Moreover, insights into the potential further developments in this field and their effects on future clinical practice were discussed.

Could the Microbiota Be a Predictive Factor for the Clinical Response to Probiotic Supplementation in IBS-D? A Cohort Study

BACKGROUND

Increasing evidence suggests the beneficial effects of probiotics in irritable bowel syndrome (IBS), but little is known about how they can impact the gut microbiota. Our objective was to evaluate the effects of a multistrain probiotic on IBS symptoms, gut permeability and gut microbiota in patients with diarrhoea-predominant IBS (IBS-D).

METHODS

Adults with IBS-D were enrolled in an open-label trial to receive a multistrain probiotic for 4 weeks. Abdominal pain, stool frequency, quality of life, gut permeability, and the luminal and adherent microbiota from colonic biopsies were evaluated before and after supplementation.

RESULTS

Probiotics significantly improved symptoms and quality of life, despite having no impact on permeability in the global population. In the population stratified by the response, the diarrhoea responders displayed reduced colonic permeability after supplementation. The luminal and adherent microbiota were specifically altered depending on the patients' clinical responses regarding pain and diarrhoea. Interestingly, we identified a microbial signature in IBS-D patients that could predict a response or lack of response to supplementation.

CONCLUSIONS

The multistrain probiotic improved the symptoms of IBS-D patients and induced distinct effects on the gut microbiota according to the patient's clinical response and initial microbiota composition. Our study further supports the need to develop individualised probiotic-based approaches regarding IBS.

Future of Endoscopy in Inflammatory Bowel Diseases (IBDs)

Gastrointestinal (GI) endoscopy has transformed over the years in scope, safety, accuracy, acceptability, and cost effectiveness of the clinical practice. There has been a reduction in the superiority of the endoscopic devices as innovations have taken place and increased the diagnostic values with certain limitations. There are particular difficulties in striking a balance between the development of new technology and the device's acceptance. The wide use of endoscopy for investigating GI lesions and diagnosis has led to an increase in more advanced methods and their broad application. It can simultaneously diagnose pre-malignant and malignant lesions, and newer interventions have made the biopsy specimen uptake possible. In this review article, we focus on the more recent roles, indications, applications, and usage of the innovative methods of endoscopy.

Clinical application and diagnostic accuracy of artificial intelligence in colonoscopy for inflammatory bowel disease: systematic review

Background and aims  Artificial intelligence (AI) technology is being evaluated for its potential to improve colonoscopic assessment of inflammatory bowel disease (IBD), particularly with computer-aided image classifiers. This review evaluates the clinical application and diagnostic test accuracy (DTA) of AI algorithms in colonoscopy for IBD. Methods  A systematic review was performed on studies evaluating AI in colonoscopy of adult patients with IBD. MEDLINE, Embase, Emcare, PsycINFO, CINAHL, Cochrane Library and Clinicaltrials.gov databases were searched on 28 ^th April 2021 for English language articles published between January 1, 2000 and April 28, 2021. Risk of bias and applicability were assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Diagnostic accuracy was presented as median (interquartile range). Results  Of 1029 records screened, nine studies with 7813 patients were included for review. AI was used to predict endoscopic and histologic disease activity in ulcerative colitis, and differentiation of Crohn's disease from Behcet's disease and intestinal tuberculosis. DTA of AI algorithms ranged between 52-91 %. The sensitivity and specificity for AI algorithms predicting endoscopic severity of disease were 78 % (range 72-83, interquartile range 5.5) and 91 % (range 86-96, interquartile range 5), respectively. Conclusions  AI has been primarily used to assess disease activity in ulcerative colitis. The diagnostic performance is promising and suggests potential for other clinical application of AI in IBD colonoscopy such as dysplasia detection. However, current evidence is limited by retrospective data and models trained on still images only. Future prospective multicenter studies with full-motion videos are needed to replicate the real-world clinical setting.

Monitoring of intestinal inflammation and prediction of recurrence in ulcerative colitis

Background and objectives: Ulcerative colitis is a chronic recurrent intestinal inflammatory disease, and its recurrence is difficult to predict. In this review, we summarized the objective indicators that can be used to evaluate intestinal inflammation, the purpose is to better predict the clinical recurrence of UC, formulate individualized treatment plan during remission of UC, and improve the level of diagnosis and treatment of UC.Methods: Based on the search results in the PUBMED database, we explored the accuracy and value of these methods in predicting the clinical recurrence of UC from the following three aspects: endoscopic and histological scores, serum biomarkers and fecal biomarkers.Results: Colonoscopy with biopsy is the gold standard for assessing intestinal inflammation, but it is invasive, inconvenient and expensive. At present, there is no highly sensitive and specific endoscopic or histological score to predict the clinical recurrence of UC. Compared with serum biomarkers, fecal biomarkers have higher sensitivity and specificity because they are in direct contact with the intestine and are closer to the site of intestinal inflammation. Fecal calprotectin is currently the most studied and meaningful fecal biomarker. Lactoferrin and S100A12, as novel biomarkers, have no better performance than FC in predicting the recurrence of UC.Conclusions: FC is currently the most promising predictive marker, but it lacks an accurate cut-off value. Combining patient symptoms, incorporating multiple indicators to construct a UC recurrence prediction model, and formulating individualized treatment plans for high recurrence risk patients will be the focus of UC remission management.

Artificial Intelligence in Endoscopy

Artificial intelligence (AI) is rapidly developing in various medical fields, and there is an increase in research performed in the field of gastrointestinal (GI) endoscopy. In particular, the advent of convolutional neural network, which is a class of deep learning method, has the potential to revolutionize the field of GI endoscopy, including esophagogastroduodenoscopy (EGD), capsule endoscopy (CE), and colonoscopy. A total of 149 original articles pertaining to AI (27 articles in esophagus, 30 articles in stomach, 29 articles in CE, and 63 articles in colon) were identified in this review. The main focuses of AI in EGD are cancer detection, identifying the depth of cancer invasion, prediction of pathological diagnosis, and prediction of Helicobacter pylori infection. In the field of CE, automated detection of bleeding sites, ulcers, tumors, and various small bowel diseases is being investigated. AI in colonoscopy has advanced with several patient-based prospective studies being conducted on the automated detection and classification of colon polyps. Furthermore, research on inflammatory bowel disease has also been recently reported. Most studies of AI in the field of GI endoscopy are still in the preclinical stages because of the retrospective design using still images. Video-based prospective studies are needed to advance the field. However, AI will continue to develop and be used in daily clinical practice in the near future. In this review, we have highlighted the published literature along with providing current status and insights into the future of AI in GI endoscopy.

Artificial Endoscopy and Inflammatory Bowel Disease: Welcome to the Future

Artificial intelligence (AI) is assuming an increasingly important and central role in several medical fields. Its application in endoscopy provides a powerful tool supporting human experiences in the detection, characterization, and classification of gastrointestinal lesions. Lately, the potential of AI technology has been emerging in the field of inflammatory bowel disease (IBD), where the current cornerstone is the treat-to-target strategy. A sensible and specific tool able to overcome human limitations, such as AI, could represent a great ally and guide precision medicine decisions. Here we reviewed the available literature on the endoscopic applications of AI in order to properly describe the current state-of-the-art and identify the research gaps in IBD at the dawn of 2022.

Classification of the Confocal Microscopy Images of Colorectal Tumor and Inflammatory Colitis Mucosa Tissue Using Deep Learning

Confocal microscopy image analysis is a useful method for neoplasm diagnosis. Many ambiguous cases are difficult to distinguish with the naked eye, thus leading to high inter-observer variability and significant time investments for learning this method. We aimed to develop a deep learning-based neoplasm classification model that classifies confocal microscopy images of 10× magnified colon tissues into three classes: neoplasm, inflammation, and normal tissue. ResNet50 with data augmentation and transfer learning approaches was used to efficiently train the model with limited training data. A class activation map was generated by using global average pooling to confirm which areas had a major effect on the classification. The proposed method achieved an accuracy of 81%, which was 14.05% more accurate than three machine learning-based methods and 22.6% better than the predictions made by four endoscopists. ResNet50 with data augmentation and transfer learning can be utilized to effectively identify neoplasm, inflammation, and normal tissue in confocal microscopy images. The proposed method outperformed three machine learning-based methods and identified the area that had a major influence on the results. Inter-observer variability and the time required for learning can be reduced if the proposed model is used with confocal microscopy image analysis for diagnosis.

Deep neural network for video colonoscopy of ulcerative colitis: a cross-sectional study

BACKGROUND

A combination of endoscopic and histological evaluation is important in the management of patients with ulcerative colitis. We aimed to adapt our previous deep neural network system (deep neural ulcerative colitis [DNUC]) to full video colonoscopy and evaluate its validity in the real-time detection of histological mucosal inflammation.

METHODS

In this multicentre, cross-sectional study, we prospectively enrolled consecutive patients (≥15 years) with ulcerative colitis who had an indication for colonoscopy at five hospitals in Japan. Patients in clinical remission were randomly assigned (1:2) to study 1 and study 2. Those with clinically active disease were assigned to study 2 only. Study 1 assessed the validity of real-time histological assessment using DNUC and study 2 validated the consistency of endoscopic scoring between DNUC and experts. The primary endpoint for study 1 was comparison of the results judged by DNUC (healing or active) with biopsy specimens evaluated by pathologists. In study 2, the primary endpoint was the ability of DNUC to determine the Ulcerative Colitis Endoscopic Index of Severity score compared with centrally evaluated scoring by inflammatory bowel disease endoscopy experts.

FINDINGS

From April 1, 2020, to March 31, 2021, 770 patients (180 in study 1 and 590 in study 2) were enrolled. Using real-time histological evaluation, DNUC was able to evaluate the presence or absence of histological inflammation in 729 (81%) of 900 biopsy specimens. For predicting histological remission, the DNUC had a sensitivity of 97·9% (95% CI 97·0-98·5) and a specificity of 94·6% (91·1-96·9). Moreover, its positive predictive value was 98·6% (97·7-99·2) and negative predictive value was 92·1% (88·7-94·3). The intraclass correlation coefficient between DNUC and experts for endoscopic scoring was 0·927 (95% CI 0·915-0·938).

INTERPRETATION

DNUC provided consistently accurate endoscopic scoring and showed potential for reducing the number of biopsies required. This system is an objective and consistent application for video colonoscopy that has potential for use in various medical situations.

FUNDING

Tokyo Medical and Dental University and Sony.

The endoscopic diagnosis of mucosal healing and deep remission in inflammatory bowel disease

The therapeutic goal in inflammatory bowel disease (IBD) patients has shifted from controlling the clinical activity alone to managing other associated problems. The concept of mucosal healing (MH) and deep remission (DR) are advocated and regarded as new therapeutic goals in IBD. However, the definition of MH still remains controversial. It is unclear whether or not the histological structures or functional factors should be included in the definition of DR in addition to clinical remission and MH. The classifications of white-light imaging (e.g. Mayo endoscopic subscore, UCEIS, CD Endoscopic Index of Severity, simple Endoscopic Score-CD) have been proposed and are now widely used to assess the severity as well as the MH of inflammation in IBD. In ulcerative colitis, magnifying chromoendoscopy has been shown to be useful to assess the MH of inflammation while other types of image-enhanced endoscopy, such as narrow-band imaging, have not. Endocytoscopy and confocal laser endomicroscopy (CLE) are also applied to assess the activity in IBD. These endoscopic procedures can estimate MH with more precision through observing the details of superficial structures, such as crypt openings. In addition, CLE can partially assess the mucosal function by detecting fluorescence leakage. Molecular imaging can possibly detect the molecules associated with inflammation, intestinal regeneration and differentiation, and various functions including the intestinal barrier and mucus secretion. These novel procedures may improve the diagnosis strategy of DR through the assessment of DR-associated factors such as the histological structures and functional factors in the near future.

The Role of Intestinal Permeability in Gastrointestinal Disorders and Current Methods of Evaluation

An increased intestinal permeability has been described in various gastrointestinal and non-gastrointestinal disorders. Nevertheless, the concept and definition of intestinal permeability is relatively broad and includes not only an altered paracellular route, regulated by tight junction proteins, but also the transcellular route involving membrane transporters and channels, and endocytic mechanisms. Paracellular intestinal permeability can be assessed in vivo by using different molecules (e.g., sugars, polyethylene glycols, ⁵¹Cr-EDTA) and ex vivo in Ussing chambers combining electrophysiology and probes of different molecular sizes. The latter is still the gold standard technique for assessing the epithelial barrier function, whereas in vivo techniques, including putative blood biomarkers such as intestinal fatty acid-binding protein and zonulin, are broadly used despite limitations. In the second part of the review, the current evidence of the role of impaired barrier function in the pathophysiology of selected gastrointestinal and liver diseases is discussed. Celiac disease is one of the conditions with the best evidence for impaired barrier function playing a crucial role with zonulin as its proposed regulator. Increased permeability is clearly present in inflammatory bowel disease, but the question of whether this is a primary event or a consequence of inflammation remains unsolved. The gut-liver axis with a crucial role in impaired intestinal barrier function is increasingly recognized in chronic alcoholic and metabolic liver disease. Finally, the current evidence does not support an important role for increased permeability in bile acid diarrhea.

Early remodeling of the colonic mucosa after allogeneic hematopoietic stem cells transplantation: An open-label controlled pilot study on 19 patients

Background

Graft‐versus‐host disease (GVHD), particularly acute digestive GVHD (aDGVHD), is a severe complication of allogeneic hematopoietic stem cell transplantation (allo‐HSCT). It is necessary to identify predictive factors of GVHD to adapt prophylactic treatment.

Objective

In this context, our pilot study aimed (i) to determine whether an early remodeling of the colonic mucosa occurred after allo‐HSCT and (ii) to identify potential predictive mucosal markers of aDGVHD after allo‐HSCT.

Methods

Between day 21 and day 28 after the allo‐HSCT, 19 allo‐HSCT patients were included and had a rectosigmoidoscopy with probe‐based confocal laser endomicroscopy (pCLE) recording and biopsies. Sixteen patients were included in the control group. Morphological (pCLE), functional (intestinal permeability), and inflammatory parameters (cytokine multiplex immunoassay) were assessed.

Results

Among allo‐HSCT patients, 11 patients developed GVHD, and 6 of them developed aDGVHD. Morphological and functional changes of the colonic mucosa occurred after allo‐HSCT. Indeed, the perimeter of colonic crypts was significantly increased in allo‐HSCT patients compared to controls as well as crypt lumen fluorescein leakage (53% vs. 9%), whereas crypts sphericity, roundness, Feret diameter, and mean vessel area were significantly decreased in allo‐HSCT patients compared to the control group. In addition, interleukin‐6 (IL‐6), IL‐33, and IL‐15 levels in the supernatants of 24 h explant cultures of colonic biopsies were significantly increased in allo‐HSCT patients compared to controls. Finally, there was no difference in pCLE parameters, intestinal permeability, and inflammatory cytokines between patients who developed aDGVHD and those who did not.

Conclusion

This pilot study identified early colonic mucosa remodeling after allo‐HSCT conditioning therapy, that is morphological and functional mucosal alterations as well as mucosal inflammation. As to whether these changes are first steps in GVHD initiation and could be considered as predictive biomarkers of aDGVHD need to be determined in a larger cohort of patients.

Artificial intelligence in gastrointestinal endoscopy for inflammatory bowel disease: a systematic review and new horizons

INTRODUCTION

Since the advent of artificial intelligence (AI) in clinical studies, luminal gastrointestinal endoscopy has made great progress, especially in the detection and characterization of neoplastic and preneoplastic lesions. Several studies have recently shown the potential of AI-driven endoscopy for the investigation of inflammatory bowel disease (IBD). This systematic review provides an overview of the current position and future potential of AI in IBD endoscopy.

METHODS

A systematic search was carried out in PubMed and Scopus up to 2 December 2020 using the following search terms: artificial intelligence, machine learning, computer-aided, inflammatory bowel disease, ulcerative colitis (UC), Crohn's disease (CD). All studies on human digestive endoscopy were included. A qualitative analysis and a narrative description were performed for each selected record according to the Joanna Briggs Institute methodologies and the PRISMA statement.

RESULTS

Of 398 identified records, 18 were ultimately included. Two-thirds of these (12/18) were published in 2020 and most were cross-sectional studies (15/18). No relevant bias at the study level was reported, although the risk of publication bias across studies cannot be ruled out at this early stage. Eleven records dealt with UC, five with CD and two with both. Most of the AI systems involved convolutional neural network, random forest and deep neural network architecture. Most studies focused on capsule endoscopy readings in CD (n = 5) and on the AI-assisted assessment of mucosal activity in UC (n = 10) for automated endoscopic scoring or real-time prediction of histological disease.

DISCUSSION

AI-assisted endoscopy in IBD is a rapidly evolving research field with promising technical results and additional benefits when tested in an experimental clinical scenario. External validation studies being conducted in large and prospective cohorts in real-life clinical scenarios will help confirm the added value of AI in assessing UC mucosal activity and in CD capsule reading.

PLAIN LANGUAGE SUMMARY

Artificial intelligence for inflammatory bowel disease endoscopy Artificial intelligence (AI) is a promising technology in many areas of medicine. In recent years, AI-assisted endoscopy has been introduced into several research fields, including inflammatory bowel disease (IBD) endoscopy, with promising applications that have the potential to revolutionize clinical practice and gastrointestinal endoscopy.We have performed the first systematic review of AI and its application in the field of IBD and endoscopy.A formal process of paper selection and analysis resulted in the assessment of 18 records. Most of these (12/18) were published in 2020 and were cross-sectional studies (15/18). No relevant biases were reported. All studies showed positive results concerning the novel technology evaluated, so the risk of publication bias cannot be ruled out at this early stage.Eleven records dealt with UC, five with CD and two with both. Most studies focused on capsule endoscopy reading in CD patients (n = 5) and on AI-assisted assessment of mucosal activity in UC patients (n = 10) for automated endoscopic scoring and real-time prediction of histological disease.We found that AI-assisted endoscopy in IBD is a rapidly growing research field. All studies indicated promising technical results. When tested in an experimental clinical scenario, AI-assisted endoscopy showed it could potentially improve the management of patients with IBD.Confirmatory evidence from real-life clinical scenarios should be obtained to verify the added value of AI-assisted IBD endoscopy in assessing UC mucosal activity and in CD capsule reading.

Deep Neural Network for Differentiation of Brain Tumor Tissue Displayed by Confocal Laser Endomicroscopy

Background

Reliable on site classification of resected tumor specimens remains a challenge. Implementation of high-resolution confocal laser endoscopic techniques (CLEs) during fluorescence-guided brain tumor surgery is a new tool for intraoperative tumor tissue visualization. To overcome observer dependent errors, we aimed to predict tumor type by applying a deep learning model to image data obtained by CLE.

Methods

Human brain tumor specimens from 25 patients with brain metastasis, glioblastoma, and meningioma were evaluated within this study. In addition to routine histopathological analysis, tissue samples were stained with fluorescein ex vivo and analyzed with CLE. We trained two convolutional neural networks and built a predictive level for the outputs.

Results

Multiple CLE images were obtained from each specimen with a total number of 13,972 fluorescein based images. Test accuracy of 90.9% was achieved after applying a two-class prediction for glioblastomas and brain metastases with an area under the curve (AUC) value of 0.92. For three class predictions, our model achieved a ratio of correct predicted label of 85.8% in the test set, which was confirmed with five-fold cross validation, without definition of confidence. Applying a confidence rate of 0.999 increased the prediction accuracy to 98.6% when images with substantial artifacts were excluded before the analysis. 36.3% of total images met the output criteria.

Conclusions

We trained a residual network model that allows automated, on site analysis of resected tumor specimens based on CLE image datasets. Further in vivo studies are required to assess the clinical benefit CLE can have.

Variability in the Distribution of Histological Disease Activity in the Colon of Patients with Ulcerative Colitis

BACKGROUND AND AIMS

Histological activity scores have been developed and validated. However, data on the distribution of histological inflammation within one segment in patients with ulcerative colitis [UC] are lacking. This impacts on the reliability of histological activity scores. The aim of this study was to assess the variability in histological activity within one endoscopic segment in patients with UC.

METHODS

Biopsies were taken in sequential patients with UC in three adjacent contiguous regions within a macroscopically homogeneous colonic segment. Biopsies were scored for Geboes score [GS], Robarts histological index [RHI] and Nancy histological index [NHI]. Variability was assessed by Kappa statistics for categorical outcomes and intraclass correlation coefficient [ICC] for continuous outcomes.

RESULTS

A total of 161 biopsy sets from 55 endoscopic segments of 21 patients were analysed. Endoscopically active disease was present in 45% of segments. The continuous histological scores showed excellent agreement between the different regions. The ICC for RHI in all segments was 0.974 (95% confidence interval [CI] 0.958-0.984; p < 0.0001) and 0.98 [95% CI: 0.968-0.988; p < 0.0001] for the numerically converted GS. The categorical NHI showed higher variability: κ = 0.574 [95% CI: 0.571-0.577; p < 0.0001]. In all segments the highest variability was seen in samples with NHI = 2. When dichotomizing based on histological remission, substantial agreement was seen for all scores, with κ > 0.734 for all cut-offs. The homogeneity in the distribution of histological disease activity was comparable between colonic segments.

CONCLUSION

The distribution of histological disease activity in UC follows a homogeneous pattern in different locations of one segment.

Artificial intelligence: Thinking outside the box

Artificial intelligence (AI) for luminal gastrointestinal endoscopy is rapidly evolving. To date, most applications have focused on colon polyp detection and characterization. However, the potential of AI to revolutionize our current practice in endoscopy is much more broadly positioned. In this review article, the Authors provide new ideas on how AI might help endoscopists in the future to rediscover endoscopy practice.

Development and Validation of a Deep Neural Network for Accurate Evaluation of Endoscopic Images From Patients With Ulcerative Colitis

BACKGROUND & AIMS

There are intra- and interobserver variations in endoscopic assessment of ulcerative colitis (UC) and biopsies are often collected for histologic evaluation. We sought to develop a deep neural network system for consistent, objective, and real-time analysis of endoscopic images from patients with UC.

METHODS

We constructed the deep neural network for evaluation of UC (DNUC) algorithm using 40,758 images of colonoscopies and 6885 biopsy results from 2012 patients with UC who underwent colonoscopy from January 2014 through March 2018 at a single center in Japan (the training set). We validated the accuracy of the DNUC algorithm in a prospective study of 875 patients with UC who underwent colonoscopy from April 2018 through April 2019, with 4187 endoscopic images and 4104 biopsy specimens. Endoscopic remission was defined as a UC endoscopic index of severity score of 0; histologic remission was defined as a Geboes score of 3 points or less.

RESULTS

In the prospective study, the DNUC identified patients with endoscopic remission with 90.1% accuracy (95% confidence interval [CI] 89.2%-90.9%) and a kappa coefficient of 0.798 (95% CI 0.780-0.814), using findings reported by endoscopists as the reference standard. The intraclass correlation coefficient between the DNUC and the endoscopists for UC endoscopic index of severity scoring was 0.917 (95% CI 0.911-0.921). The DNUC identified patients in histologic remission with 92.9% accuracy (95% CI 92.1%-93.7%); the kappa coefficient between the DNUC and the biopsy result was 0.859 (95% CI 0.841-0.875).

CONCLUSIONS

We developed a deep neural network for evaluation of endoscopic images from patients with UC that identified those in endoscopic remission with 90.1% accuracy and histologic remission with 92.9% accuracy. The DNUC can therefore identify patients in remission without the need for mucosal biopsy collection and analysis. Trial number: UMIN000031430.

Many Patients With Irritable Bowel Syndrome Have Atypical Food Allergies Not Associated With Immunoglobulin E

BACKGROUND & AIMS

Confocal laser endomicroscopy (CLE) is a technique that permits real-time detection and quantification of changes in intestinal tissues and cells, including increases in intraepithelial lymphocytes and fluid extravasation through epithelial leaks. Using CLE analysis of patients with irritable bowel syndrome (IBS), we found that more than half have responses to specific food components. Exclusion of the defined food led to long-term symptom relief. We used the results of CLE to detect reactions to food in a larger patient population and analyzed duodenal biopsy samples and fluid from patients to investigate mechanisms of these reactions.

METHODS

In a prospective study, 155 patients with IBS received 4 challenges with each of 4 common food components via the endoscope, followed by CLE, at a tertiary medical center. Classical food allergies were excluded by negative results from immunoglobulin E serology analysis and skin tests for common food antigens. Duodenal biopsy samples and fluid were collected 2 weeks before and immediately after CLE and were analyzed by histology, immunohistochemistry, reverse transcription polymerase chain reaction, and immunoblots. Results from patients who had a response to food during CLE (CLE+) were compared with results from patients who did not have a reaction during CLE (CLE-) or healthy individuals (controls).

RESULTS

Of the 108 patients who completed the study, 76 were CLE+ (70%), and 46 of these (61%) reacted to wheat. CLE+ patients had a 4-fold increase in prevalence of atopic disorders compared with controls (P = .001). Numbers of intraepithelial lymphocytes were significantly higher in duodenal biopsy samples from CLE+ vs CLE- patients or controls (P = .001). Expression of claudin-2 increased from crypt to villus tip (P < .001) and was up-regulated in CLE+ patients compared with CLE- patients or controls (P = .023). Levels of occludin were lower in duodenal biopsy samples from CLE+ patients vs controls (P = .022) and were lowest in villus tips (P < .001). Levels of messenger RNAs encoding inflammatory cytokines were unchanged in duodenal tissues after CLE challenge, but eosinophil degranulation increased, and levels of eosinophilic cationic protein were higher in duodenal fluid from CLE+ patients than controls (P = .03).

CONCLUSIONS

In a CLE analysis of patients with IBS, we found that more than 50% of patients could have nonclassical food allergy, with immediate disruption of the intestinal barrier upon exposure to food antigens. Duodenal tissues from patients with responses to food components during CLE had immediate increases in expression of claudin-2 and decreases in occludin. CLE+ patients also had increased eosinophil degranulation, indicating an atypical food allergy characterized by eosinophil activation.

Imbalanced mucosal microcirculation in the remission stage of ulcerative colitis using probe-based confocal laser endomicroscopy

Background

Microcirculatory disturbance is an important factor in the pathogenesis of Inflammatory Bowel Disease (IBD) but there have been few studies in this field. Confocal Laser Endomicroscopy (CLE) has been used over the last 10 years and has made it possible to explore the changes in microcirculation of the colonic mucosa.

Methods

We retrospectively selected patients who underwent probe-based Confocal Laser Endomicroscopy (pCLE) between 2014 and 2016. There were 7 patients with ulcerative colitis (UC) in clinical remission and 7 healthy subjects included in this study; all the UC patients’ medical data were reviewed. For each patient, three segments of the colon were examined using pCLE including the ascending, transverse/descending and sigmoid colon. In each segment, the representative pCLE images of the three sites were selected for analysis. Four indicators, including Mean Vessel Diameter (MVD), Diameter Standard Deviation (DSD), Functional Capillary Density-long (FCDL) and Functional Capillary Density-area (FCDA), were measured with a specially designed detection software algorithm. The four indicators were compared between UC patients and healthy subjects. According to the different blood flow patterns, three types of distribution were established: the Around (A), Cobweb (C) and Deficiency (D) type. The relationships between the recurrence and blood flow patterns of UC patients were analyzed.

Results

MVD, DSD, FCDL and FCDA were 10.62 ± 0.56 μm, 2.23 ± 0.26, 0.030 ± 0.019 μm and 0.289 ± 0.030 for the healthy subjects and 11.06 ± 1.10 μm, 2.68 ± 0.29, 0.026 ± 0.005 μm and 0.272 ± 0.034 for the UC patients, respectively. Compared with healthy subjects, DSD was significantly increased and FCDA was significantly decreased (P < 0.01 for both). There was no difference in MVD and FCDL between UC patients and healthy subjects. The type A and type C blood flows were observed in healthy subjects (66.67 and 33.33%, respectively) while type C appears more in UC patients (71.3%) and type D blood flow could only be found in UC patients (14.29%) P < 0.01. UC patients who showed Type D blood flow had a shorter recurrence interval.

Conclusions

Some local mucosal capillary density in UC patients was decreased, particularly in the inflammation-affected segment. The three mucosal blood flow patterns can be used as an indicator of mucosal healing.