Utility of a deep learning model and a clinical model for predicting bleeding after endoscopic submucosal dissection in patients with early gastric cancer
Ji Eun Na, Yeong Chan Lee, Tae Jun Kim, Hyuk Lee, Hong‐Hee Won, Yang Won Min, Byung‐Hoon Min, Jun Haeng Lee, Poong‐Lyul Rhee, Jae J. Kim
Abstract
BACKGROUND: Bleeding is one of the major complications after endoscopic submucosal dissection (ESD) in early gastric cancer (EGC) patients. There are limited studies on estimating the bleeding risk after ESD using an artificial intelligence system. AIM: To derivate and verify the performance of the deep learning model and the clinical model for predicting bleeding risk after ESD in EGC patients. METHODS: Patients with EGC who underwent ESD between January 2010 and June 2020 at the Samsung Medical Center were enrolled, and post-ESD bleeding (PEB) was investigated retrospectively. We split the entire cohort into a development set (80%) and a validation set (20%). The deep learning and clinical model were built on the development set and tested in the validation set. The performance of the deep learning model and the clinical model were compared using the area under the curve and the stratification of bleeding risk after ESD. RESULTS: = 0.730). The patients expected to the low- (< 5%), intermediate- (≥ 5%, < 9%), and high-risk (≥ 9%) categories were observed with actual bleeding rate of 2.2%, 3.9%, and 11.6%, respectively, in the deep learning model; 4.0%, 8.8%, and 18.2%, respectively, in the clinical model. CONCLUSION: A deep learning model can predict and stratify the bleeding risk after ESD in patients with EGC.