Using a systematic literature search of original articles published during 2024 in Gastrointestinal Endoscopy (GIE) and other high-impact medical and gastroenterology journals, the GIE Editorial Board of the American Society for Gastrointestinal Endoscopy compiled a list of the top 10 most significant topic areas in general and advanced GI endoscopy during the year. Each GIE Editorial Board member was directed to consider 3 criteria in generating candidate topics: significance, novelty, and impact on clinical practice. Subject matter consensus was facilitated by the Chair through electronic voting of the entire GIE Editorial Board. The top 10 identified topics collectively represent advances in the following endoscopic areas: glucagon-like peptide-1 receptor agonists and endoscopy, advances in AI in endoscopy, ergonomics in endoscopy, peroral endoscopic myotomy, bariatric and metabolic endoscopy, endoscopic resection in the colon, gastric intestinal metaplasia and endoscopy, inflammatory bowel disease and endoscopy, GI bleeding risk stratification and endoscopic therapies, and therapeutic EUS. Board members were assigned a topic area and summarized relevant and important articles, thereby generating this overview of the "top 10" endoscopic advances of 2024.

Inflammatory bowel diseases (IBDs), comprising Crohn's disease (CD) and ulcerative colitis (UC), are chronic immune-mediated inflammatory diseases of the gastrointestinal (GI) tract with still-elusive etiopathogeneses and an increasing prevalence worldwide. Despite the growing availability of more advanced therapies in the last two decades, there are still a number of unmet needs. For example, the achievement of mucosal healing has been widely demonstrated as a prognostic marker for better outcomes and a reduced risk of dysplasia and cancer; however, the accuracy of endoscopy is crucial for both this aim and the precise and reproducible evaluation of endoscopic activity and the detection of dysplasia. Artificial intelligence (AI) has drastically altered the field of GI studies and is being extensively applied to medical imaging. The utilization of deep learning and pattern recognition can help the operator optimize image classification and lesion segmentation, detect early mucosal abnormalities, and eventually reveal and uncover novel biomarkers with biologic and prognostic value. The role of AI in endoscopy-and potentially also in histology and imaging in the context of IBD-is still at its initial stages but shows promising characteristics that could lead to a better understanding of the complexity and heterogeneity of IBDs, with potential improvements in patient care and outcomes. The initial experience with AI in IBDs has shown its potential value in the differentiation of UC and CD when there is no ileal involvement, reducing the significant amount of time it takes to review videos of capsule endoscopy and improving the inter- and intra-observer variability in endoscopy reports and scoring. In addition, these initial experiences revealed the ability to predict the histologic score index and the presence of dysplasia. Thus, the purpose of this review was to summarize recent advances regarding the application of AI in IBD endoscopy as there is, indeed, increasing evidence suggesting that the integration of AI-based clinical tools will play a crucial role in paving the road to precision medicine in IBDs.

Endoscopic scoring indices for ulcerative colitis and Crohn's disease are subject to inter-endoscopist variability. There is increasing interest in the development of deep learning models to standardize endoscopic assessment of intestinal diseases. Here, we summarize and critically appraise the literature on artificial intelligence-assisted endoscopic characterization of inflammatory bowel disease severity.

A systematic search of Ovid MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and IEEE Xplore was performed to identify reports of AI systems used for endoscopic severity classification of IBD. Selected studies were critically appraised for methodological and reporting quality using APPRAISE-AI.

Thirty-one studies published between 2019 and 2024 were included. Of 31 studies, 28 studies examined endoscopic classification of ulcerative colitis and 3 examined Crohn's disease. Researchers sought to accomplish a wide range of classification tasks, including binary and multilevel classification, based on still images or full-length colonoscopy videos. Overall scores for study quality ranged from 41 (moderate quality) to 64 (high quality) out of 100, with 28 out of 31 studies within the moderate quality range. The highest-scoring domains were clinical relevance and reporting quality, while the lowest-scoring domains were robustness of results and reproducibility.

Multiple AI models have demonstrated the potential for clinical translation for ulcerative colitis. Research concerning the endoscopic severity assessment of Crohn's disease is limited and should be further explored. More rigorous external validation of AI models and increased transparency of data and codes are needed to improve the quality of AI studies.

The Mayo Endoscopic Subscore (MES) is widely utilized for assessing mucosal activity in ulcerative colitis (UC). Artificial intelligence has emerged as a promising tool for enhancing diagnostic precision and addressing interobserver variability. This study evaluated the diagnostic accuracy of ChatGPT-4, a multimodal large language model, in identifying and grading endoscopic images of UC patients using the MES.

Real-world endoscopic images of UC patients were reviewed by an expert consensus board. Each image was graded based on the MES. Only images that were uniformly graded were subsequently provided to three inflammatory bowel disease (IBD) specialists and ChatGPT-4. Severity gradings of the IBD specialists and ChatGPT-4 were compared with assessments made by the expert consensus board.

Thirty of 50 images were graded with complete agreement among the experts. Compared with the consensus board, ChatGPT-4 gradings had a mean accuracy rate of 78.9% whereas the mean accuracy rate for the IBD specialists was 81.1%. Between the two groups, there was no statistically significant difference in mean accuracy rates ( P = 0.71) and a high degree of reliability was found.

ChatGPT-4 has the potential to assess mucosal inflammation severity from endoscopic images of UC patients, without prior configuration or fine-tuning. Performance rates were comparable to those of IBD specialists.

Inflammatory bowel disease is marked by significant clinical heterogeneity, posing challenges for accurate diagnosis and personalized treatment strategies. Conventional approaches, such as endoscopy and histology, often fail to adequately and accurately predict medium- and long-term outcomes, leading to suboptimal patient management. Artificial intelligence is emerging as a transformative force enabling standardized, accurate, and timely disease assessment and outcome prediction, including therapeutic response. Artificial intelligence-driven intestinal barrier healing assessment provides novel insights into deep healing, facilitating the discovery of novel therapeutic targets. In addition, the automated integration of multi-omics data can enhance patient profiling and personalized management strategies. The future of inflammatory bowel disease care lies in the artificial intelligence-enabled "endo-histo-omics" integrative real-time approach, harmoniously fusing endoscopic, histologic, and molecular data. Despite challenges in its adoption, this paradigm shift has the potential to refine risk stratification, improve therapeutic precision, and enable personalized interventions, ultimately advancing the implementation of precision medicine in routine clinical practice.

Validated scoring methods such as the Mayo Clinic Endoscopic Subscore (MCES) evaluate ulcerative colitis (UC) severity at the worst colon segment, without considering disease extent. We present the Ulcerative Colitis Severity Classification and Localized Extent (UC-SCALE) algorithm, which provides a comprehensive and automated evaluation of endoscopic severity and disease extent in UC.

Ulcerative Colitis Severity Classification and Localized Extent consists of 3 main elements: (1) a quality filter selecting readable images (frames) from colonoscopy videos, (2) a scoring system assigning an MCES to each readable frame, and (3) a camera localization algorithm assigning each frame to a location within the colon. Ulcerative Colitis Severity Classification and Localized Extent was trained and tested using 4326 sigmoidoscopy videos from phase III Etrolizumab clinical trials.

The high agreement between UC-SCALE and central reading at the level of the colon section (𝜅 = 0.80), and the agreement between central and local reading (𝜅 = 0.84), suggested a similar inter-rater agreement between UC-SCALE and experienced readers. Furthermore, UC-SCALE correlated with disease activity markers such calprotectin, C-reactive protein and patient-reported outcomes, Physician Global Assessment and Geboes Histologic scores (rs 0.40-0.55, ps < 0.0001). Finally, the value of using UC-SCALE was demonstrated by assessing individual endoscopic severity between baseline and induction.

Our fully automated scoring system enables accurate, objective, and localized assessment of endoscopic severity in UC patients. In addition, we provide a topological representation of the score as a marker of disease severity that correlates highly with clinical metrics. Ulcerative Colitis Severity Classification and Localized Extent reproduces central reading and holds promise to enhance disease severity evaluation in both clinical trials and everyday practice.

The Mayo endoscopic subscore [MES] is the most popular endoscopic disease activity measure of ulcerative colitis [UC]. Artificial intelligence [AI]-assisted colonoscopy is expected to reduce diagnostic variability among endoscopists. However, no study has been conducted to ascertain whether AI-based MES assignments can help predict clinical relapse, nor has AI been verified to improve the diagnostic performance of non-specialists.

This open-label, prospective cohort study enrolled 110 patients with UC in clinical remission. The AI algorithm was developed using 74 713 images from 898 patients who underwent colonoscopy at three centres. Patients were followed up after colonoscopy for 12 months, and clinical relapse was defined as a partial Mayo score > 2. A multi-video, multi-reader analysis involving 124 videos was conducted to determine whether the AI system reduced the diagnostic variability among six non-specialists.

The clinical relapse rate for patients with AI-based MES = 1 (24.5% [12/49]) was significantly higher [log-rank test, p = 0.01] than that for patients with AI-based MES = 0 (3.2% [1/31]). Relapse occurred during the 12-month follow-up period in 16.2% [13/80] of patients with AI-based MES = 0 or 1 and 50.0% [10/20] of those with AI-based MES = 2 or 3 [log-rank test, p = 0.03]. Using AI resulted in better inter- and intra-observer reproducibility than endoscopists alone.

Colonoscopy using the AI-based MES system can stratify the risk of clinical relapse in patients with UC and improve the diagnostic performance of non-specialists.

The gap between endoscopy and histology is getting closer with the introduction of sophisticated endoscopic technologies. Furthermore, unprecedented advances in artificial intelligence (AI) have enabled objective assessment of endoscopy and digital pathology, providing accurate, consistent, and reproducible evaluations of endoscopic appearance and histologic activity. These advancements result in improved disease management by predicting treatment response and long-term outcomes. AI will also support endoscopy in raising the standard of clinical trial study design by facilitating patient recruitment and improving the validity of endoscopic readings and endoscopy quality, thus overcoming the subjective variability in scoring. Accordingly, AI will be an ideal adjunct tool for enhancing, complementing, and improving our understanding of ulcerative colitis course. This review explores promising AI applications enabled by endoscopy and histology techniques. We further discuss future directions, envisioning a bright future where AI technology extends the frontiers beyond human limits and boundaries.

Ulcerative colitis (UC), characterized by its recurrent nature, imposes a significant disease burden and compromises the quality of life. Emerging evidence suggests that achieving clinical remission is not sufficient for long-term remission. In pursuit of a favorable prognosis, mucosal healing (MH) has been defined as the target of therapies in UC. This paradigm shift has given rise to the formulation of diverse endoscopic and histological scoring systems, providing distinct definitions for MH. Endoscopic remission (ER) has been widely employed in clinical practice, but it is susceptible to subjective factors related to endoscopists. And there's growing evidence that histological remission (HR) might be associated with a lower risk of disease flares, but the incorporation of HR as a routine therapeutic endpoint remains a debate. The integration of advanced technology has further enriched the definition of deep MH. Up to now, a universal standardized definition for deep MH in clinical practice is currently lacking. This review will focus on the definition of deep MH, from different dimensions, and analyze strengths and limitations, respectively. Subsequent multiple large-scale trials are needed to validate the concept of deep MH, offering valuable insights into potential benefits for UC patients.

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.

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.

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.

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.

Endoscopic evaluation in diagnosing and managing ulcerative colitis (UC) is becoming increasingly important. Several endoscopic scoring systems have been established, including the Ulcerative Colitis Endoscopic Index of Severity (UCEIS) score and Mayo Endoscopic Subscore (MES). Furthermore, the Toronto Inflammatory Bowel Disease Global Endoscopic Reporting (TIGER) score for UC has recently been proposed; however, its clinical value remains unclear.

To investigate the clinical value of the TIGER score in UC by comparing it with the UCEIS score and MES.

This retrospective study included 166 patients with UC who underwent total colonoscopy between January 2017 and March 2023 at the Affiliated Hospital of Qingdao University (Qingdao, China). We retrospectively analysed endoscopic scores, laboratory and clinical data, treatment, and readmissions within 1 year. Spearman's rank correlation coefficient, receiver operating characteristic curve, and univariate and multivariable logistic regression analyses were performed using IBM SPSS Statistics for Windows, version 26.0 (IBM Corp., Armonk, NY, United States) and GraphPad Prism version 9.0.0 for Windows (GraphPad Software, Boston, Massachusetts, United States).

The TIGER score significantly correlated with the UCEIS score and MES (r = 0.721, 0.626, both P < 0.001), showed good differentiating values for clinical severity among mild, moderate, and severe UC [8 (4-112.75) vs 210 (109-219) vs 328 (219-426), all P < 0.001], and exhibited predictive value in diagnosing patients with severe UC [area under the curve (AUC) = 0.897, P < 0.001]. Additionally, the TIGER (r = 0.639, 0,551, 0.488, 0.376, all P < 0.001) and UCEIS scores (r = 0.622, 0,540, 0.494, and 0.375, all P < 0.001) showed stronger correlations with laboratory and clinical parameters, including C-reactive protein, erythrocyte sedimentation rate, length of hospitalisation, and hospitalisation costs, than MES (r = 0.509, 0,351, 0.339, and 0.270, all P < 0.001). The TIGER score showed the best predictability for patients' recent advanced treatment, including systemic corticosteroids, biologics, or immunomodulators (AUC = 0.848, P < 0.001) and 1-year readmission (AUC = 0.700, P < 0.001) compared with the UCEIS score (AUC = 0.762, P < 0.001; 0.627, P < 0.05) and MES (AUC = 0.684, P < 0.001; 0.578, P = 0.132). Furthermore, a TIGER score of ≥ 317 was identified as an independent risk factor for advanced UC treatment (P = 0.011).

The TIGER score may be superior to the UCIES score and MES in improving the accuracy of clinical disease severity assessment, guiding therapeutic decision-making, and predicting short-term prognosis.