LAUSR

BACKGROUND AND AIMS White-light endoscopy (WLE) is the most pivotal tool to detect gastric cancer in the early stage. However, the skill among endoscopists varies greatly. Here, we aim to develop a deep learning-based system named ENDOANGEL-LD (lesion detection) to assist in detecting all focal gastric lesions and predicting neoplasms by WLE. METHODS Endoscopic images were retrospectively obtained from Renmin Hospital of Wuhan University (RHWU) for the development, validation and internal test of the system. Additional external tests were conducted in 5 other hospitals to evaluate the robustness. Stored videos from RHWU were used for assessing and comparing the performance of ENDOANGEL-LD with that of experts. Prospective consecutive patients undergoing upper endoscopy were enrolled from May 6, 2021 to Aug 2 2021 in RHWU to assess clinical practice applicability. RESULTS Over 10 thousand patients undergoing upper endoscopy were enrolled in this study. The sensitivities were 96.9% and 95.6% for detecting gastric lesions, 92.9% and 91.7% for diagnosing neoplasm in internal and external patients, respectively. In 100 videos, ENDOANGEL-LD achieved superior sensitivity and negative predictive value for detecting gastric neoplasm than that of experts (100% vs 85.5%±3.4%, p=0.003; 100% vs 86.4%±2.8%, p=0.002). In 2010 prospective consecutive patients, ENDOANGEL-LD achieved a sensitivity of 92.8% for detecting gastric lesions with 3.04±3.04 false positives per gastroscopy, and a sensitivity of 91.8% and specificity of 92.4% for diagnosing neoplasm. CONCLUSIONS The results show that ENDOANGEL-LD has great potential for assisting endoscopists in screening gastric lesions and suspicious neoplasms in clinical work.