Introduction

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common chronic liver disease, with potential progression to metabolic dysfunction-associated steatohepatitis (MASH) and fibrosis. Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have revolutionized obesity treatment, but their role in MASH with fibrosis remains under investigation.

Aims & Methods

This study evaluates the efficacy and safety of GLP-1 RAs in MASH with fibrosis. We performed a systematic review and meta-analysis of randomized controlled trials (RCTs) from PubMed, Embase, and Cochrane. Primary outcomes included histologic improvement in fibrosis without worsening MASH, MASH resolution without worsening fibrosis, and improvement in disease activity score. Secondary outcomes included adverse events (AEs), laboratory, and anthropometric data.

Results

Six RCTs (1,000 patients, mean follow-up: 47 weeks) were included. GLP-1 RAs did not significantly improve fibrosis without worsening MASH (RR 1.35; 95% CI 0.91–2.01; p=0.141; I²=53.5%) or disease activity score (RR 1.33; 95% CI 0.93–1.89; p=0.114; I²=64%). However, they significantly increased MASH resolution without worsening fibrosis (RR 3.50; 95% CI 2.44–5.02; p<0.001; I²=0%). No significant differences were observed in serious AEs. GLP-1 RAs improved laboratory, imaging, glycemic, lipidic, blood pressure, and anthropometric parameters.

Conclusion

GLP-1 RAs appear effective and safe in MASH with fibrosis, particularly in resolving MASH without worsening fibrosis.

Introduction

Several Randomized Controlled Trials (RCTs) have evaluated the role of artificial intelligence (AI) Computer-Aided Detection (CADe) systems to enhance polyp detection. However, these studies have focused exclusively on locally deployed hardware-based AI systems, whereas cloud-based systems may improve the accessibility and upgrading of CADe technology. ‘CADDIE’ (Odin Vision, an Olympus company) is a cloud-based CADe system that receives the live endoscopy image stream via the hospital’s internet network, where the AI algorithm processes the images, and overlays real-time bounding boxes on the endoscopy screen to highlight suspected polyps.

Aims & Methods

This prospective RCT aimed to evaluate the efficacy and safety of the novel cloud-based CADe-system in improving polyp detection.

This international, multi-centre, parallel-group RCT was conducted across eight centres in four European countries. Participating endoscopists had a baseline of >1000 colonoscopy procedures and an adenoma detection rate (ADR) of ≥25%. Patients aged ≥40 years and at average risk for colorectal cancer undergoing screening or surveillance indications for colonoscopy were recruited. Patients were randomised in a 1:1 ratio to standard colonoscopy (SoC) or CADDIE-assisted (CADe-arm) colonoscopy. The co-primary endpoints were superiority in adenomas per colonoscopy (APC) as the efficacy endpoint, and non-inferiority for the Positive Percent Agreement (PPA), defined as the percentage of resections that are histologically confirmed adenomas, SSLs and large (>10mm) hyperplastic polyps of the proximal colon, as the safety endpoint.

Results

Overall, 22 endoscopists and 841 participants were included in the analysis. The CADe-arm significantly increased APC by 33% (0.62 vs 0.82, Ratio 1.33 [95%CI 1.06-1.67], p=0.01) and ADR (35.9% vs 43.2%, difference of 7.3% [95%CI 0.7%-13.9%], p=0.03) compared to the SoC-arm. The mean detection of sessile serrated lesions (SSLs) (0.03 vs 0.08, Ratio 3.30 [95%CI 1.41-7.57], p<0.01), large adenomas (0.04 vs 0.09; Ratio 1.93 [95%CI 1.03-3.62], p=0.04), and large polyps (0.05 vs 0.12; Ratio 2.36 [95%CI 1.33-4.17], p<0.01) also significantly improved. A higher proportion of large polyps (27.3% vs. 37.0%) and large adenomas (15.8% vs 24.2%) were non-polypoid ‘flat’ morphology in the CADe-arm compared to the SoC-arm. Other detection metrics, including neoplastic polyps per colonoscopy (0.66 vs 0.91; Ratio 1.39 [95%CI 1.12-1.73], p<0.01) and polyps per colonoscopy (1.06 vs 1.41 (Ratio 1.35 [95%CI 1.13-1.61], p<0.01), also significantly increased.

Non-inferiority in PPA was achieved (53.4% vs 53.9%; 0.5% (-5.0%,∞)), with no increase in unnecessary resections.

Conclusion

This trial demonstrates the safety and efficacy of a cloud-based CADe-system to increase endoscopists' polyp detection across multiple metrics, notably for large adenomas, large polyps, and SSLs.

Disclosure

AR, CS, LBL, RK have received medical consultancy fees from Odin Medical Ltd

Introduction

The Mayo Endoscopic Subscore (MES) is the most widely used endoscopic index for assessing disease activity in ulcerative colitis (UC). Although artificial-intelligence　(AI)–assisted colonoscopy is expected to reduce inter-observer variability, the MES 4-point scale (0–3) lacks sufficient sensitivity to detect subtle changes in mucosal activity, limiting its ability to capture incremental improvement or deterioration. Moreover, because MES focuses on qualitative features—such as bleeding and erosions—without considering the extent or surface area of inflammation, it provides an incomplete estimate of a patient’s overall inflammatory burden.

Aims & Methods

We sought to develop and validate a quantitative, AI–driven colonoscopic score—Quantitative Ulcerative Colitis Assessment using Deep-learning (QUAD)—and to determine whether it predicts clinical relapse in patients with UC. The QUAD system was trained on 84 743 colonoscopic images from 998 UC patients at three centers and comprises three main functions: i) Image filtering: Automatic exclusion of low-quality images that are unsuitable for analysis owing to light saturation (halation), inadequate insufflation, excessive proximity to the mucosa, chromoendoscopy, or virtual chromoendoscopy. ii) Frame-level scoring: Each frame is divided into four quadrants; inflammation in each quadrant is graded from 0 to 3, and the quadrant scores are summed to yield a QUAD score of 0–12. iii) Video-level aggregation: Full-length withdrawal videos are processed to generate (i) an Entire QUAD score (0–60), representing pan-colonic inflammation, and (ii) a Distal QUAD score (0–12) for the rectosigmoid segment (final 20 % of the video).
The QUAD system contains mainly three function: i) automatically exclude low quality images that is ineligible for analysis in cases of halation, inadequate air de­livery, proximity to mucosa, chromoendoscopy, and virtual chromoendoscopy; ii) fully auto­mated twelve-class score output based on frame analysis: Each frame was divided into four quadrants; inflammation in each quadrant was graded 0–3 and summed to yield a QUAD score of 0–12; and iii) full-length withdrawal videos were processed to generate (I) an Entire QUAD (0–60) for considering whole-colon inflammation status and (II) a Distal QUAD (0–12) for the distal rectum–sigmoid segment (final 20 % of the video). An open-label cohort of adults with quiescent UC (partial Mayo score ≤ 2 for ≥6 months) was prospectively enrolled at a single center (August 2022–February 2023), and followed for 24 months after baseline colonoscopy; clinical relapse was defined as partial Mayo score > 2. The predictive value of QUAD scores for relapse was evaluated.

Results

Frame-level QUAD scores correlated strongly with expert-assigned MES values (Spearman r = 0.68, p < 0.01). Among 156 participants (median age, 51 years; 49 % male), 22 experienced relapse during follow-up. Relapse occurred in 19.4 % (19/98) of patients with a QUAD score > 3 versus 5.2 % (3/58) with a score ≤ 3 (log-rank p = 0.01).
In video analysis, an Entire QUAD > 6 did not significantly stratify relapse risk (16.8 % [17/101] vs 9.1 % [5/55]; log-rank p = 0.17). In contrast, a Distal QUAD > 3 was strongly associated with relapse (43.8 % [7/16] vs 10.7 % [15/140]; log-rank p < 0.01).

Conclusion

AI-generated QUAD provides a sensitive, fully quantitative measure of mucosal inflammation and stratifies relapse risk in clinically quiescent UC. In particular, the automatically derived Distal QUAD—focused on the distal colon and independent of operator image selection—offers a practical, objective tool for longitudinal monitoring and treatment tailoring in UC.

Introduction

The treatment of esophageal motor disorders (EMD) has historically been focused on decreasing pressure at the gastro-esophageal junction (GEJ) by disrupting muscular fibers at this level. The classic management through dilatation, botulinum toxin injection, and Heller's myotomy (HM) underwent a revolution with the advent of third-space endoscopy, offering safe access to the submucosal space and enabling peroral endoscopic myotomy (POEM).
In this prospective national multicenter study, the results of the implementation period of this technique in Spain from 2016 to the present are analyzed, examining its short, medium, and long-term outcomes. Furthermore, outcomes based on EMD, previous treatments, and other multiple variables are also investigated.

Aims & Methods

The primary aim of this study was to ascertain the clinical efficacy of POEM.
Secondary objectives included the identification of predictors of clinical success and the measurement of the incidence of post-POEM GERD and significant complications, as well as the determination of the risk factors for complications
This is a prospective multicenter study conducted across 13 hospitals in Spain. Symptomatic patients undergoing POEM for any EMD were recruited between June 2016 and February 2023. Baseline characteristics, procedural details and outcomes related to efficacy and safety during short and long-term follow-up were analyzed.

Results

A total of 441 patients were enrolled, with type II achalasia representing the most common diagnosis (62.1%). POEM was the primary treatment in 277 patients (62.8%). Clinical success data were available for 396 patients, with a success rate of 94.4% at the last available follow-up (2 years in 117 patients, 1 year in 140, <1 year in 139). A higher baseline Eckardt score was an independent risk factor for clinical failure in the long-term follow-up (p = 0.024). Symptomatic reflux occurred in 4.7% of the patients, with a significant esophagitis rate of 10.1%. Complication incidence was 9.3% (41/441), including 14 (3.2%) severe events. Older age (p = 0.025) and total procedure duration (p=0.036) were independent risk factors for complications, while mucosal injury was found to be a predictor of severe complications (p = 0.027).

Conclusion

These findings confirm the clinical efficacy and safety during the implementation phase of POEM for all EMD in Spain.

Introduction

Artificial Intelligence (AI) assisted reading in Small Bowel Capsule Endoscopy (SBCE) has recently been shown to achieve comparable and potentially superior accuracy compared to standard clinician reading. In Colon Capsule Endoscopy (CCE), AI algorithms have also demonstrated promising results. However, the extent of AI-assisted reading's advantage remains unclear, particularly regarding its performance across different polyp sizes, morphologies, locations, and other factors. A deeper understanding of these variables is crucial for optimising AI performance and facilitating its clinical integration.

Aims & Methods

This CESCAIL sub-analysis evaluates the per-polyp diagnostic accuracy of AI-assisted versus standard clinician assessment (readings) and identifies key factors influencing AI-assisted accuracy using AiSPEED^TM, the Computer-Aided Detection (CADe) system.
The CESCAIL study evaluated the diagnostic accuracy of AI-assisted CCE readings using AiSPEED™ compared to the standard readings. Standard readings were manually performed by clinicians, while AI-assisted readings incorporated an AI pre-review followed by clinician validation. A total of 1,803 polyps from 673 patients were analysed at the per-polyp level, focusing on sensitivity and positive predictive value. Factors influencing the improved accuracy of AI-assisted readings over standard clinician readings were also assessed, including polyp size, morphology, location, patient demographics (age and sex), bowel preparation quality, capsule excretion rates, comorbidities, medications, reading time, and video duration.

Results

AI-assisted reading demonstrated significantly higher sensitivity with clear superiority for smaller polyps (<10 mm) compared to larger ones (≥10 mm) (OR 2.27 vs 0.88, p<0.001) (see Table 1). While there was no observed difference in diagnostic accuracy between AI-assisted and standard clinician assessment for polyps ≥10 mm, non-inferiority was established. AI accuracy remained consistent between 6–9 mm and ≤5 mm polyps (p=0.64). The most notable improvement was observed with hyperplastic polyps (HPs) (OR 5.4, p<0.001). No significant differences were identified for pedunculated, sub-pedunculated, Lateral Spreading Tumours, or Sessile Serrated Lesions. Furthermore, AI-assisted readings were significantly more accurate for left-sided polyps than right-sided ones (OR 2.36 vs 1.66, p<0.0001), although AI-assisted reads outperformed standard reads in both locations.

Conclusion

This study highlights the performance and consistency of AI-assisted reading, particularly for detecting smaller adenomas and HPs, with notably improved accuracy in the left colon. Next-generation AI should focus on distinguishing significant from diminutive polyps and enhancing polyp characterisation, especially for right-sided lesions.

References

1. Spada C, Piccirelli S, Hassan C, Ferrari C, Toth E, Gonzalez-Suarez B, et al. AI-assisted capsule endoscopy reading in suspected small bowel bleeding: a multicentre prospective study. Lancet Digit Health. 2024;6(5):e345-e53.

Disclosure

Hagen Wenzek serves as the CEO, Elizabeth White is the Project Manager, and Pablo Laiz is the AI software engineer at Corporate Health International, which owns the intellectual properties of AiSPEED^TM.