Type of publication:

Conference abstract

Author(s):

Lei I.I.; Parisi I.; Bhandare A.; Perez F.P.; Lee T.; Shehkar C.; McStay M.; Anderson S.; Watson A.; Conlin A.; Badreldin R.; Malik K.; Jacob J.; Dixon A.; *Butterworth J.; Parson N.; Koulaouzidis A.; Robertson A.; Treceno P.; Arasaradnam R.

Citation:

Gut. Conference: BSG Annual Meeting, BSG LIVE 2025. Glasgow United Kingdom. 74(Supplement 1) (pp A9-A10), 2025. Date of Publication: 01 Jun 2025.

Abstract:

Background Colon Capsule Endoscopy (CCE) provides a noninvasive alternative to colonoscopy for evaluating the lower gastrointestinal (LGI) tract. However, its widespread use has been limited by prolonged reading times and variability in diagnostic accuracy, often affected by factors such as bowel preparation quality and completion rates. In recent years, artificial intelligence (AI) has demonstrated potential in overcoming these limitations, particularly in small bowel CE, by enabling clinicians to achieve high diagnostic accuracy with significantly reduced reading times. The CESCAIL multi-centre study aims to evaluate a Computer-Aided Detection (CADe) system (AiSPEEDTM) to enhance polyp detection efficiency in CCE. Objective The primary aim is to assess AI-assisted CCE readings' diagnostic accuracy and non-inferiority in detecting polypoid lesions compared to standard readings using a per-patient analysis. The secondary objective focuses on mean reading time to evaluate the efficiency of each approach. Methods Patients aged 18 years or older, referred under urgent cancer or post-polypectomy surveillance pathway to one of the 14 CESCAIL participating centres across the UK, were prospectively enrolled in the study. Participants underwent CCE examinations, which were analysed using the AiSPEEDTM system, a convolutional neural network designed for automated polyp detection. Clinicians conducted initial manual readings, followed by AI-assisted readings, which involved an AI-automated first read, a review and annotation by a pre-reader, and a clinician assessment of selected images to create a report. Results Between February 2022 and September 2024, 673 patients were included in the final analysis. The overall completion rate was 77.1%, with adequate bowel preparation achieved in 78.1% of the standard pathway and 74.9% of the AI-assisted pathway (McNemar p=0.1). In the standard pathway, 403 patients (59.9%) required further investigation, including 243 (36.1%) colonoscopies and 138 (20.5%) flexible sigmoidoscopies. In the per-patient analysis, the diagnostic yield for polyp detection leading to a follow-up colonoscopy was 71.9% (484/673) for AI-assisted reading and 63.6% (428/ 673) for standard reading, confirming non-inferiority (p<0.0001). The diagnostic accuracy was 0.96 (95% CI: 0.95-0.98) for AI-assisted reading and 0.91 (95% CI: 0.89- 0.93) for standard reading (McNemar p<0.0001). The mean clinician reading time per video was 8.7 (SD=11.3) minutes for AI-assisted reading, compared to 47.3 (SD=24) minutes for standard reading, with a 5-fold reduction. Conclusion AI-assisted reading using AiSPEEDTM demonstrated significantly higher detection yield with improved diagnostic accuracy coupled with reduced reading time for polyp detection in CCE compared to standard clinician readings. These findings emphasise AI's potential to enhance efficiency and scalability in CCE, supporting its broader adoption for LGI investigations in clinical practice.

DOI: 10.1136/gutjnl-2025-BSG.14

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Type of publication:

Oral presentation

Author(s):

Lei I.I.; Parisi I.; Bhandare A.; Perez F.P.; Lee T.; Shekhar C.; McStay M.; Anderson S.; Watson A.; Conlin A.; Badreldin R.; Malik K.; Jacob J.; Dixon A.; *Butterworth J.; Parsons N.; Robertson A.; Koulaouzidis A.; Arasaradnam R.

Citation:

Gut. Conference: BSG Annual Meeting, BSG LIVE 2025. Glasgow United Kingdom. 74(Supplement 1) (pp A48-A49), 2025. Date of Publication: 01 Jun 2025.

Abstract:

Background 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 some promising results.1 However, the extent of AI-assisted reading's advantage remains unclear, particularly regarding its performance across different polyp sizes, morphologies, locations, and non-polyp-related factors. Understanding this is essential for optimising AI performance and clinical integration. Objective(s) This CESCAIL sub-analysis evaluates the per-polyp diagnostic accuracy of AI-assisted versus standard clinician reads (pathways) and identifies key factors influencing AIassisted accuracy using AiSPEEDTM. Methods A total of 1,803 polyps from 673 patients were analysed at the per-polyp level to assess diagnostic accuracy in terms of sensitivity and PPV, as well as the factors influencing the improved accuracy of AI-assisted readings compared to standard clinician readings. Factors examined included polyp size, morphology, location, patient demographics (age and sex), bowel preparation quality, capsule excretion rates, comorbidities, medications, reading time, and video duration. Statistical methods included, McNemar's test, superiority and noninferiority analyses, Generalised Estimating Equations, and generalized linear models with interaction terms, were employed to identify key predictors of enhanced diagnostic accuracy in both AI-assisted and standard readings. 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<0.0001). While there was no observed difference in diagnostic accuracy between pathways for polyps >=10 mm, noninferiority was established. AI accuracy remained consistent between polyps measuring 6-9 mm and <=5 mm (p=0.64). The most notable improvement was observed with hyperplastic polyps (OR 5.4, p<0.0001), particularly in the rectal region (OR 5.7, p<0.0001). No significant differences were identified for pedunculated, subpedunculated, LST, or SSL polyps. Furthermore, AI-assisted readings were significantly more accurate for left-sided polyps compared to 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 strengths of AI-assisted reading, particularly for detecting smaller adenomas and hyperplastic polyps, with notable 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.

DOI: 10.1136/gutjnl-2025-BSG.72

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Type of publication:

Poster presentation

Author(s):

Lei I.I.; Parisi I.; Bhandare A.; Perez F.; Lee T.; Shehkar C.; McStay M.; Anderson S.; Watson A.; Conlin A.; Badreldin R.; Malik K.; Jacob J.; Dixon A.; *Butterworth J.; Parsons N.; Koulaouzidis A.; Arasaradnam R.

Citation:

Gut. Conference: BSG Annual Meeting, BSG LIVE 2025. Glasgow United Kingdom. 74(Supplement 1) (pp A269-A270), 2025. Date of Publication: 01 Jun 2025.

Abstract:

Background Colon capsule endoscopy (CCE) has emerged as a non-invasive alternative to traditional colonoscopy for low-risk patients. However, its adoption is limited by low completion rates and the inability to perform biopsies or polyp removal, often resulting in CCE-to-conventional colonoscopy conversion (CCC). This conversion carries financial implications, contributes to patient dissatisfaction due to repeated procedures, and imposes a potential environmental burden from increased hospital visits. Objective(s) The aim is to identify the factors that predict issues with bowel cleansing, capsule excretion rates, pathology detection, and the need for CCC. Methods In this prospective analysis of the CESCAIL study (November 2021-June 2024), 603 patients who underwent CCE were included. Predictive factors-including patient demographics, comorbidities, medications, and laboratory results-were analysed across symptomatic and surveillance groups. Statistical techniques such as LASSO regression, linear regression, and logistic regression were applied. Results Among the 603 participants analyzed, elevated f-Hb levels (OR=1.48, 95% CI: 1.18-1.86, p=0.0002) and smoking (OR=1.44, 95% CI: 1.01-2.11, p=0.047) were significantly associated with CCE-to-conventional colonoscopy conversion (CCC). However, an AUC of 0.62 after adjusting for confounders suggests f-Hb is a weak predictor of CCC. Diabetes was linked to poor bowel preparation (OR=0.40, 95% CI: 0.18-0.87, p=0.022). Alcohol use (p=0.004), smoking (p=0.003), and psychological conditions (p=0.001) were significantly associated with an increased polyp count, while haemoglobin levels (p=0.046) showed a marginal negative association with polyp numbers. Additionally, antidepressants (p=0.003) were associated with larger polyps, whereas betablockers (p=0.001) were linked to smaller polyps. Conclusion Non-smokers with lower f-Hb levels are less likely to require CCC. Effective patient selection criteria are essential for minimising conversion rates and improving the efficiency of CCE services. These findings highlight the need for validation across diver

DOI: 10.1136/gutjnl-2025-BSG.428

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