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

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

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.

Automated Neutrophil Quantification and Histological Score Estimation in Ulcerative Colitis.

BACKGROUND AND AIMS: In the management of ulcerative colitis (UC), histological remission is increasingly recognized as the ultimate goal. The absence of neutrophil infiltration is crucial for assessing remission. This study aimed to develop an artificial intelligence (AI) system capable of accurately quantifying and localizing neutrophils in UC biopsy specimens to facilitate histological assessment. METHODS: Our AI system, which incorporates semantic segmentation and object detection models, was developed to identify neutrophils in hematoxylin-eosin-stained whole slide images. The system assessed the presence and location of neutrophils within either the epithelium or lamina propria and predicted components of the Nancy Histological Index (NHI) and the PICaSSO Histologic Remission Index (PHRI). We evaluated the system's performance against that of experienced pathologists and validated its ability to predict future clinical relapse risk in patients with clinically remitted UC. The primary outcome measure was the clinical relapse rate, defined as a partial Mayo score of ≥3. RESULTS: The model accurately identified neutrophils, achieving a performance of 0.77, 0.81, and 0.79 for precision, recall, and F-score, respectively. The system's histological score predictions showed a positive correlation with the pathologists' diagnoses (Spearman's ρ = 0.68-0.80, P < .05). Among patients who relapsed, the mean number of neutrophils in the rectum was higher than in those who did not relapse. Furthermore, the study highlighted that higher AI-based PHRI and NHI scores were associated with hazard ratios increasing from 3.2 to 5.0 for evaluating the risk of UC relapse. CONCLUSION: The AI system's precise localization and quantification of neutrophils proved valuable for histological assessment and clinical prognosis stratification.

Artificial Intelligence Enabled Histological Prediction of Remission or Activity and Clinical Outcomes in Ulcerative Colitis.

BACKGROUND & AIMS: Microscopic inflammation has significant prognostic value in ulcerative colitis (UC); however, its assessment is complex with high interobserver variability. We aimed to develop and validate an artificial intelligence (AI) computer-aided diagnosis system to evaluate UC biopsies and predict prognosis. METHODS: A total of 535 digitalized biopsies (273 patients) were graded according to the PICaSSO Histologic Remission Index (PHRI), Robarts, and Nancy Histological Index. A convolutional neural network classifier was trained to distinguish remission from activity on a subset of 118 biopsies, calibrated on 42 and tested on 375. The model was additionally tested to predict the corresponding endoscopic assessment and occurrence of flares at 12 months. The system output was compared with human assessment. Diagnostic performance was reported as sensitivity, specificity, prognostic prediction through Kaplan-Meier, and hazard ratios of flares between active and remission groups. We externally validated the model in 154 biopsies (58 patients) with similar characteristics but more histologically active patients. RESULTS: The system distinguished histological activity/remission with sensitivity and specificity of 89% and 85% (PHRI), 94% and 76% (Robarts Histological Index), and 89% and 79% (Nancy Histological Index). The model predicted the corresponding endoscopic remission/activity with 79% and 82% accuracy for UC endoscopic index of severity and Paddington International virtual ChromoendoScopy ScOre, respectively. The hazard ratio for disease flare-up between histological activity/remission groups according to pathologist-assessed PHRI was 3.56, and 4.64 for AI-assessed PHRI. Both histology and outcome prediction were confirmed in the external validation cohort. CONCLUSION: We developed and validated an AI model that distinguishes histologic remission/activity in biopsies of UC and predicts flare-ups. This can expedite, standardize, and enhance histologic assessment in practice and trials.

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

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

Present and future of endoscopy precision for inflammatory bowel disease.

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

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

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

A virtual chromoendoscopy artificial intelligence system to detect endoscopic and histologic activity/remission and predict clinical outcomes in ulcerative colitis.

BACKGROUND: Endoscopic and histological remission (ER, HR) are therapeutic targets in ulcerative colitis (UC). Virtual chromoendoscopy (VCE) improves endoscopic assessment and the prediction of histology; however, interobserver variability limits standardized endoscopic assessment. We aimed to develop an artificial intelligence (AI) tool to distinguish ER/activity, and predict histology and risk of flare from white-light endoscopy (WLE) and VCE videos. METHODS: 1090 endoscopic videos (67 280 frames) from 283 patients were used to develop a convolutional neural network (CNN). UC endoscopic activity was graded by experts using the Ulcerative Colitis Endoscopic Index of Severity (UCEIS) and Paddington International virtual ChromoendoScopy ScOre (PICaSSO). The CNN was trained to distinguish ER/activity on endoscopy videos, and retrained to predict HR/activity, defined according to multiple indices, and predict outcome; CNN and human agreement was measured. RESULTS: The AI system detected ER (UCEIS ≤ 1) in WLE videos with 72 % sensitivity, 87 % specificity, and an area under the receiver operating characteristic curve (AUROC) of 0.85; for detection of ER in VCE videos (PICaSSO ≤ 3), the sensitivity was 79 %, specificity 95 %, and the AUROC 0.94. The prediction of HR was similar between WLE and VCE videos (accuracies ranging from 80 % to 85 %). The model's stratification of risk of flare was similar to that of physician-assessed endoscopy scores. CONCLUSIONS: Our system accurately distinguished ER/activity and predicted HR and clinical outcome from colonoscopy videos. This is the first computer model developed to detect inflammation/healing on VCE using the PICaSSO and the first computer tool to provide endoscopic, histologic, and clinical assessment.

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

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

Validation of red dichromatic imaging score (RDI score) to evaluate the severity of ulcerative colitis.

BACKGROUND: Red dichromatic imaging (RDI) is a new imaging technology that has been closely correlated with the activity index of ulcerative colitis (UC). Although the RDI score has been developed no study has validated a correlation between the RDI score and the activity index of UC. Therefore, this study aims to validate the RDI score prospectively. METHODS: A total of 34 patients with UC in whom colonoscopy was scheduled between May 2019 and October 2021 at our hospital were enrolled prospectively. MES, UCEIS, RDI scores, and Nancy index were evaluated in a blinded fashion. We evaluated the correlation between RDI and WLI scores using still images with histology. RESULTS: We analyzed 191 sites of colorectum. RDI score showed the positive correlation with UCEIS (r = 0.74 P < 0.0001) and MES (r = 0.78 P < 0.0001). RDI score also showed the positive correlation with Nancy index (r = 0.63 P < 0.0001). RDI score was more strongly correlated with Nancy index than UCEIS (r = 0.51) and MES (r = 0.48). CONCLUSIONS: When comparing still images of RDI and WLI scores, we showed RDI had a higher correlation to histology than WLI. CLINICAL TRIAL ID: The University Hospital Medical Information Network (UMIN000041750).

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

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

PICaSSO Histologic Remission Index (PHRI) in ulcerative colitis: development of a novel simplified histological score for monitoring mucosal healing and predicting clinical outcomes and its applicability in an artificial intelligence system.

Histological remission is evolving as an important treatment target in UC. We aimed to develop a simple histological index, aligned to endoscopy, correlated with clinical outcomes, and suited to apply to an artificial intelligence (AI) system to evaluate inflammatory activity. METHODS: Using a set of 614 biopsies from 307 patients with UC enrolled into a prospective multicentre study, we developed the Paddington International virtual ChromoendoScopy ScOre (PICaSSO) Histologic Remission Index (PHRI). Agreement with multiple other histological indices and validation for inter-reader reproducibility were assessed. Finally, to implement PHRI into a computer-aided diagnosis system, we trained and tested a novel deep learning strategy based on a CNN architecture to detect neutrophils, calculate PHRI and identify active from quiescent UC using a subset of 138 biopsies. RESULTS: PHRI is strongly correlated with endoscopic scores (Mayo Endoscopic Score and UC Endoscopic Index of Severity and PICaSSO) and with clinical outcomes (hospitalisation, colectomy and initiation or changes in medical therapy due to UC flare-up). A PHRI score of 1 could accurately stratify patients' risk of adverse outcomes (hospitalisation, colectomy and treatment optimisation due to flare-up) within 12 months. Our inter-reader agreement was high (intraclass correlation 0.84). Our preliminary AI algorithm differentiated active from quiescent UC with 78% sensitivity, 91.7% specificity and 86% accuracy. CONCLUSIONS: PHRI is a simple histological index in UC, and it exhibits the highest correlation with endoscopic activity and clinical outcomes. A PHRI-based AI system was accurate in predicting histological remission.

Development and Validation of a Novel Computed-Tomography Enterography Radiomic Approach for Characterization of Intestinal Fibrosis in Crohn's Disease.

BACKGROUND & AIMS: No reliable method for evaluating intestinal fibrosis in Crohn's disease (CD) exists; therefore, we developed a computed-tomography enterography (CTE)-based radiomic model (RM) for characterizing intestinal fibrosis in CD. METHODS: This retrospective multicenter study included 167 CD patients with 212 bowel lesions (training, 98 lesions; test, 114 lesions) who underwent preoperative CTE and bowel resection at 1 of the 3 tertiary referral centers from January 2014 through June 2020. Bowel fibrosis was histologically classified as none-mild or moderate-severe. In the training cohort, 1454 radiomic features were extracted from venous-phase CTE and a machine learning-based RM was developed based on the reproducible features using logistic regression. The RM was validated in an independent external test cohort recruited from 3 centers. The diagnostic performance of RM was compared with 2 radiologists' visual interpretation of CTE using receiver operating characteristic (ROC) curve analysis. RESULTS: In the training cohort, the area under the ROC curve (AUC) of RM for distinguishing moderate-severe from none-mild intestinal fibrosis was 0.888 (95% confidence interval [CI], 0.818-0.957). In the test cohort, the RM showed robust performance across 3 centers with an AUC of 0.816 (95% CI, 0.706-0.926), 0.724 (95% CI, 0.526-0.923), and 0.750 (95% CI, 0.560-0.940), respectively. Moreover, the RM was more accurate than visual interpretations by either radiologist (radiologist 1, AUC = 0.554; radiologist 2, AUC = 0.598; both, P < .001) in the test cohort. Decision curve analysis showed that the RM provided a better net benefit to predicting intestinal fibrosis than the radiologists. CONCLUSIONS: A CTE-based RM allows for accurate characterization of intestinal fibrosis in CD.

An International Multicenter Real-Life Prospective Study of Electronic Chromoendoscopy Score PICaSSO in Ulcerative Colitis.

BACKGROUND & AIMS: Endoscopic and histologic remission are important goals in the treatment of ulcerative colitis (UC). We investigated the correlation of the recently developed Paddington International Virtual ChromoendoScopy ScOre (PICaSSO) and other established endoscopic scores against multiple histological indices and prospectively assessed outcomes. METHODS: In this prospective multicenter international study, inflammatory activity was assessed with high-definition and virtual chromoendoscopy in the rectum and sigmoid using the Mayo Endoscopic Score (MES), UC Endoscopic Index of Severity (UCEIS), and PICaSSO. Targeted biopsies were taken for assessment using Robarts Histological Index (RHI), Nancy Histological index (NHI), ECAP (Extent, Chronicity, Activity, Plus score), Geboes, and Villanacci. Follow-up data were obtained at 6 and 12 months after colonoscopy. RESULTS: A total of 307 patients were recruited. There was strong correlation between PICaSSO and histology scores, significantly superior to correlation coefficients of MES and UCEIS with histology scores. A PICaSSO score of ≤3 detected histologic remission by RHI (≤3 + absence of neutrophils) with area under the receiver operating characteristic curve (AUROC) 0.90 (95% confidence interval [CI] 0.86-0.94) and NHI (≤1) AUROC 0.82 (95% CI 0.77-0.87). The interobserver agreement for PICaSSO was 0.88 (95% CI 0.83-0.92). At 6- and 12-months follow-up, PICaSSO score ≤3 predicted better outcomes than PICaSSO >3 (hazard ratio [HR] 0.19 [0.11-0.33] and 0.22 [0.13-0.34], respectively),} as well as PICaSSO 4-8 (HR 0.25 [0.12-0.53] and 0.22 (0.12-0.39), respectively) and similar to histologic remission. CONCLUSION: In this first real-life multicenter study, the PICaSSO score correlated strongly with multiple histological indices. Furthermore, PICaSSO score predicted specified clinical outcomes at 6 and 12 months, similar to histology. Thus, PICaSSO can be a useful endoscopic tool in the therapeutic management of UC.

Evaluation in real-time use of artificial intelligence during colonoscopy to predict relapse of ulcerative colitis: a prospective study.

BACKGROUND AND AIMS: The use of artificial intelligence (AI) during colonoscopy is attracting attention as an endoscopist-independent tool to predict histologic disease activity of ulcerative colitis (UC). However, no study has evaluated the real-time use of AI to directly predict clinical relapse of UC. Hence, it is unclear whether the real-time use of AI during colonoscopy helps clinicians make real-time decisions regarding treatment interventions for patients with UC. This study aimed to establish the role of real-time AI in stratifying the relapse risk of patients with UC in clinical remission. METHODS: This open-label, prospective, cohort study was conducted in a referral center. The cohort comprised 145 consecutive patients with UC in clinical remission who underwent AI-assisted colonoscopy with a contact-microscopy function. We classified patients into either the Healing group or Active group based on the AI outputs during colonoscopy. The primary outcome measure was clinical relapse of UC (defined as a partial Mayo score >2) during 12 months of follow-up after colonoscopy. RESULTS: Overall, 135 patients completed the 12-month follow-up after AI-assisted colonoscopy. AI-assisted colonoscopy classified 61 patients as the Healing group and 74 as the Active group. The relapse rate was significantly higher in the AI-Active group (28.4% [21/74]; 95% confidence interval, 18.5%-40.1%) than in the AI-Healing group (4.9% [3/61]; 95% confidence interval, 1.0%-13.7%; P < .001). CONCLUSIONS: Real-time use of AI predicts the risk of clinical relapse in patients with UC in clinical remission, which helps clinicians make real-time decisions regarding treatment interventions. (Clinical trial registration number: UMIN000036650.).

Reproducibility of the electronic chromoendoscopy PICaSSO score (Paddington International Virtual ChromoendoScopy ScOre) in ulcerative colitis using multiple endoscopic platforms: a prospective multicenter international study (with video).

BACKGROUND AND AIMS: Endoscopic and histologic remission (HR) are key therapeutic targets in the management of ulcerative colitis (UC). The aim of this study was to evaluate the reproducibility of the Paddington International virtual ChromoendoScopy ScOre (PICaSSO), a virtual chromoendoscopy score originally validated by use of the iSCAN platform, with the narrow-band imaging (NBI), linked-color imaging (LCI), and blue-laser imaging (BLI) platforms. METHODS: We evaluated endoscopic activity using the Mayo Endoscopic Score (MES), the Ulcerative Colitis Endoscopic Index of Severity (UCEIS), and PICaSSO in 159 UC patients (78 NBI and 81 BLI/LCI) who underwent colonoscopy in 2 tertiary referral centers. HR was defined by the Robarts Histopathology Index (RHI) and the Nancy Histologic Index (NHI). Receiver operating characteristic curves were plotted to evaluate endoscopic scores for the prediction of HR. Intraclass correlation coefficients (ICC) between endoscopists were evaluated. RESULTS: PICaSSO had an ICC of 0.825 when the NBI and BLI/LCI cohorts were combined, higher than MES and UCEIS. The correlation between PICaSSO and RHI and NHI was 0.83 and 0.79 in the NBI cohort and between 0.63 and 0.65 in LCI/BLI. In the NBI cohort, the accuracy of MES, UCEIS, and PICaSSO was 0.936, 0.897, and 0.808 for HR measured by RHI and 0.897, 0.885, and 0.821 by NHI, respectively. In the BLI/LCI cohort, the accuracy of MES, UCEIS, LCI PICaSSO and BLI PICaSSO was 0.765, 0.778, 0.827, and 0.79 to predict HR with RHI and NHI, respectively. CONCLUSIONS: The PICaSSO score can be consistently and accurately reproduced with NBI and LCI/BLI and therefore can be applied to all virtual electronic chromoendoscopy platforms.

Performance measures for colonoscopy in inflammatory bowel disease patients: European Society of Gastrointestinal Endoscopy (ESGE) Quality Improvement Initiative.

The European Society of Gastrointestinal Endoscopy (ESGE) presents a short list of performance measures for colonoscopy in inflammatory bowel disease (IBD) patients. Current performance measures for colonoscopy mainly focus on detecting (pre)malignant lesions. However, these performance measures are not relevant for all colonoscopy indications in IBD patients. Therefore, our aim was to provide endoscopy services across Europe and other interested countries with a tool for quality monitoring and improvement in IBD colonoscopy. Eight key performance measures and one minor performance measure were recommended for measurement and evaluation in daily endoscopy practice.

Definition of competence standards for optical diagnosis of diminutive colorectal polyps: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement.

BACKGROUND : The European Society of Gastrointestinal Endoscopy (ESGE) has developed a core curriculum for high quality optical diagnosis training for practice across Europe. The development of easy-to-measure competence standards for optical diagnosis can optimize clinical decision-making in endoscopy. This manuscript represents an official Position Statement of the ESGE aiming to define simple, safe, and easy-to-measure competence standards for endoscopists and artificial intelligence systems performing optical diagnosis of diminutive colorectal polyps (1 - 5 mm). METHODS : A panel of European experts in optical diagnosis participated in a modified Delphi process to reach consensus on Simple Optical Diagnosis Accuracy (SODA) competence standards for implementation of the optical diagnosis strategy for diminutive colorectal polyps. In order to assess the clinical benefits and harms of implementing optical diagnosis with different competence standards, a systematic literature search was performed. This was complemented with the results from a recently performed simulation study that provides guidance for setting alternative competence standards for optical diagnosis. Proposed competence standards were based on literature search and simulation study results. Competence standards were accepted if at least 80 % agreement was reached after a maximum of three voting rounds. RECOMMENDATION 1: In order to implement the leave-in-situ strategy for diminutive colorectal lesions (1-5 mm), it is clinically acceptable if, during real-time colonoscopy, at least 90 % sensitivity and 80 % specificity is achieved for high confidence endoscopic characterization of colorectal neoplasia of 1-5 mm in the rectosigmoid. Histopathology is used as the gold standard.Level of agreement 95 %. RECOMMENDATION 2: In order to implement the resect-and-discard strategy for diminutive colorectal lesions (1-5 mm), it is clinically acceptable if, during real-time colonoscopy, at least 80 % sensitivity and 80 % specificity is achieved for high confidence endoscopic characterization of colorectal neoplasia of 1-5 mm. Histopathology is used as the gold standard.Level of agreement 100 %. CONCLUSION : The developed SODA competence standards define diagnostic performance thresholds in relation to clinical consequences, for training and for use when auditing the optical diagnosis of diminutive colorectal polyps.

Image-Enhanced Endoscopy Surveillance of Colon and Pouch Dysplasia in IBD.

BACKGROUND: Patients with longstanding ulcerative colitis and Crohn's colitis are at risk for developing colorectal cancer and need regular endoscopic surveillance to detect and remove precursor lesions. To do so, different technologies are available. DATA SOURCES: The sources are observational and controlled studies, meta-analysis, and expert consensus articles available on PubMed. STUDY SELECTION: The selected materials include articles reporting outcomes of and recommendations on endoscopic surveillance and resection of dysplasia in the gastrointestinal tract, including the ileoanal pouch and the anal transition zone, in patients with inflammatory bowel disease. MAIN OUTCOME MEASURES: Incidence and detection rate of dysplasia and cancer with different endoscopic techniques in patients with inflammatory bowel disease. RESULTS: Risk of cancer is proportional to the duration and extent of the disease, and surveillance interval should be tailored on the individual risk in a range of 1 to 5 years. High-definition imaging and virtual chromoendoscopy have improved the detection of dysplasia and are now comparable with conventional dye spray chromoendoscopy. After restorative proctocolectomy with ileoanal pouch, the risk of cancer is modest, but its high mortality warrants endoscopic surveillance. The evidence to guide pouch surveillance is limited, and recently, the first expert consensus provided a framework of recommendations, which include an initial assessment 1 year after surgery and follow-up depending on individual risk factors. LIMITATIONS: The limitation includes scarcity of data on ileoanal pouch surveillance. CONCLUSIONS: Virtual chromoendoscopy and high-definition imaging have improved endoscopic surveillance, and more progress is expected with the implementation of artificial intelligence systems.

A Multicenter Study of Patient Acceptability of the IBD Disk Tool and Patient-Reported Disabilities.

BACKGROUND: IBD, both Crohn's disease and ulcerative colitis, is associated with significant functional disability. Gastrointestinal symptoms alone are not the sole purpose of the interaction between patients and providers. In order to ascertain patients' disabilities, we utilized the recently developed IBD Disk to help determine their functional concerns and initiate relevant conversation. We aimed to ascertain patient acceptability and their major disabilities. PATIENTS AND METHODS: In this multicenter study, IBD patients at their outpatient visit were given the paper version of the IBD Disk. Patients were asked to score their level of disability for each item of the IBD Disk. The completed scores were then shared with their healthcare provider to act as a focus of discussion during the consultation. Patients and clinicians were also asked to provide informal qualitative feedback as to the benefits of the IBD Disk and areas for improvement. RESULTS: A total of 377 (female 60%) patients completed the questionnaires over the study period. Patient acceptability scored on a 0-10 Likert scale was excellent. All patients scored all domains of disability. Sleep, energy, and joint pain were the highest scoring domains of the IBD Disk, scoring higher than digestive symptoms. Clinicians and patients agreed that the IBD Disk allowed for ease of communication about disability symptoms and relevance to their day-to-day functioning. CONCLUSION: The IBD Disk is a novel easy-to-use tool to assess the functional disability of patients. We next plan to utilize it in the form of an electronic app internationally and in relation to treatment commencement and escalation.