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 AiSPEEDTM, 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.
| Table 1: Comparison of diagnostic yields of standard and AI-assisted readings at per-Polyp analysis (non-inferiority and superiority analysis) |
| Superiority Analysis (Sensitivity and PPV) |
Polyp size
cut-off (mm) | All size | ≤5mm | 6-9mm | ≥10mm |
| Sensitivity difference (AI-ST) | 0.174 | 0.222 | 0.161 | 0.027 |
| P-value | <0.001*** | <0.001*** | <0.001*** | 0.340 |
| Conclusion | AI-assisted arm is superior | AI-assisted arm is superior | AI-assisted arm is superior | No superiority |
| PPV difference (AI-ST) | -0.0002 | 0 | -0.002 | 0 |
| P-value | 0.936 | N/A | 0.317 | N/A |
| Conclusion | No superiority | No superiority | No superiority | No superiority |
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 AiSPEEDTM.