Colonoscopy is an effective screening procedure in colorectal cancer prevention programs; however, colonoscopy practice can vary in terms of lesion detection, classification, and removal. Artificial intelligence (AI)-assisted decision support systems for endoscopy is an area of rapid research and development. The systems promise improved detection, classification, screening, and surveillance for colorectal polyps and cancer. Several recently developed applications for AI-assisted colonoscopy have shown promising results for the detection and classification of colorectal polyps and adenomas. However, their value for real-time application in clinical practice has yet to be determined owing to limitations in the design, validation, and testing of AI models under real-life clinical conditions. Despite these current limitations, ambitious attempts to expand the technology further by developing more complex systems capable of assisting and supporting the endoscopist throughout the entire colonoscopy examination, including polypectomy procedures, are at the concept stage. However, further work is required to address the barriers and challenges of AI integration into broader colonoscopy practice, to navigate the approval process from regulatory organizations and societies, and to support physicians and patients on their journey to accepting the technology by providing strong evidence of its accuracy and safety. This article takes a closer look at the current state of AI integration into the field of colonoscopy and offers suggestions for future research.
Background and Aims: Accurate polyp size measurement is important for guideline conforming choice of polypectomy techniques and subsequent surveillance interval assignments. Some endoscopic tools (biopsy forceps [BF] or endoscopic rulers [ER]) exist to help with visual size estimation. A virtual scale endoscope (VSE) has been developed that allows superimposing a virtual measurement scale during live endoscopies. Our aim was to evaluate the performance of VSE when compared to ER and BF-based measurement.
Methods: We conducted a preclinical randomized trial to evaluate the relative accuracy of size measurement of simulated colorectal polyps when using: VSE, ER, and BF. Six endoscopists performed 60 measurements randomized at a 1:1:1 ratio using each method. Primary outcome was relative accuracy in polyp size measurement. Secondary outcomes included misclassification of sizes at the 5, 10, and 20mm thresholds.
Results: A total of 360 measurements were performed. The relative accuracy of BF, ER, and VSE was 78.9% (95%CI=76.2-81.5), 78.4% (95%CI=76.0-80.8), and 82.7% (95%CI=80.8-84.8). VSE had significantly higher accuracy compared to BF (p=0.02) and ER (p=0.006). VSE misclassified a lower percentage of polyps >5mm as ≤5mm (9.4%) compared to BF (15.7%) and ER (20.9%). VSE misclassified a lower percentage of ≥20mm polyps as <20mm (8.3%) compared with BF (66.7%) and ER (75.0%). 25.6%, 25.5%, and 22.5% of polyps ≥10mm were misclassified as <10mm with ER, BF, and VSE, respectively.
Conclusions: VSE had significantly higher relative accuracy in measuring polyps compared to ER or biopsy forceps assisted measurement. VSE improves correct classification of polyps at clinically important size thresholds.
Objectives
The virtual scale endoscope (VSE) allows projection of a virtual scale onto colorectal polyps allowing real‐time size measurements. We studied the relative accuracy of VSE compared to visual assessment (VA) for the measuring simulated polyps of different size and morphology groups.
Methods
We conducted a blinded randomized controlled trial using simulated polyps within a colon model. Sixty simulated polyps were evenly distributed across four size groups (1–5, >5–9.9, 10–19.9, and ≥20 mm) and three Paris morphology groups (flat, sessile, and pedunculated). Six endoscopists performed polyp size measurements using random allocation of either VA or VSE.
Results
A total of 359 measurements were completed. The relative accuracy of VSE was significantly higher when compared to VA for all size groups >5 mm (P = 0.004, P < 0.001, P < 0.001). For polyps ≤5 mm, the relative accuracy of VSE compared to VA was not significantly higher (P = 0.186). The relative accuracy of VSE was significantly higher when compared to VA for all morphology groups. VSE misclassified a lower percentage of >5 mm polyps as ≤5 mm (2.9%), ≥10 mm polyps as <10 mm (5.5%), and ≥20 mm polyps as <20 mm (21.7%) compared to VA (11.2%, 24.7%, and 52.3% respectively; P = 0.008, P < 0.001, and P = 0.003).
Conclusion
Virtual scale endoscope had significantly higher relative accuracies for every polyp size group or morphology type aside from diminutive. VSE enables the endoscopist to better classify polyps into correct size categories at clinically relevant size thresholds of 5, 10, and 20 mm.
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