Deep Learning-Based Automatic Detection of Rectal Polyps Using Abdominal CT Images Guided by Cold Snare Polypectomy

Abstract

The study drew attention to the therapeutic effects of cold snare polypectomy guided by a deep convolutional neural network- (CNN-) based abdominal CT and hot snare polypectomy (HSP) on colonic and rectal polyps. Specifically, 90 patients were enrolled into a blank group, a control (Ctrl) group, and an experimental group. The blank group accepted HSP, the Ctrl accepted cold snare polypectomy, and the experimental group accepted cold snare polypectomy guided by deep CNN-based CT images. It was found that the experimental group had the lowest false-positive rate (9.2%) in polyp detection in contrast with the Ctrl (21.4%) and the blank group (52.3%) $\left(P<0.05\right)$ . The complete resection rate of large polyps in the experimental group was the highest $\left(P<0.05\right)$ , and its operation time (2.91 ± 0.75 min) was obviously shorter versus the blank group (6.18 ± 1.19 min) $\left(P<0.05\right)$ . In conclusion, the cold snare polypectomy under the guidance of deep CNN-based CT has a relatively high complete resection rate and detection accuracy of polyps with a low complication rate, which can be adopted clinically.