Endoscopic virtual ruler (EVR) based on image recognition technology: a novel tool for decision support in endoscopic treatment
摘要
Accurate preoperative assessment of lesion size is crucial for selecting the appropriate endoscopic resection technique. However, the current assessment of lesion size still mainly relies on visual estimation, lacking objective measurement methods. To develop and validate an Endoscopic Virtual Ruler (EVR) based on image detection technology for objective measurement of lesion size before endoscopic treatment.
MethodsUsing computer image recognition technology and laser spot imaging principle, EVR was formed to detect the size of lesions. In vitro animal and human experiments were carried out to verify the accuracy and safety of EVR by comparing its measurement results with the actual size and the visual inspection results of endoscopists.
ResultsIn 30 in vitro tests, the measurement error of EVR was 0.08 ± 0.17 cm (95% CI 0.01–0.14), and the relative accuracy of the measurement was 92.80% ± 5.50%( 95% CI 90.75–94.85%). In 58 clinical lesions, the mean error for visual estimation was 0.16 ± 0.66 cm (95% CI− 0.01 to 0.33), while EVR showed 0.12 ± 0.32 cm (95% CI 0.04–0.21). EVR was significantly more accurate (85.68% ± 15.25%) than visual estimation (67.08% ± 22.59%, p < 0.001). EVR was more effective [48 (82.8%) vs 31 (46.6%), p = 0.001]. In the multivariable model, EVR-assisted measurement was independently associated with achieving clinically acceptable accuracy (OR 4.38, 95% CI 1.84–10.43, p = 0.001). EVR also demonstrated higher consistency in lesion size classification (Kappa = 0.764 vs. 0.522, p < 0.001). For lesions < 1 cm, EVR misclassified only 12.5% as 1–2 cm, significantly less than the 50% misclassification rate with visual estimation (p = 0.034). There was no laser damage side effect.
ConclusionEVR offers an accurate, safe, and objective measurement tool, which is helpful for the formulation of appropriate treatment decisions.
Chinese clinical trial registryChiCTR2400085998.
Graphical abstract