Objective:To develop and validate artificial intelligence (AI) models based on deep learning for precise boundary identification of early gastric cancer (EGC) in narrow-band imaging (NBI) and near focus narrow-band imaging (NF-NBI) endoscopic images.Methods:Endoscopic submucosal dissection (ESD) images from 282 patients diagnosed as having EGC by postoperative pathology at the Department of Gastroenterology, the First Affiliated Hospital of Soochow University were retrospectively collected from February 2016 to June 2024. The images were randomly divided into the training set and the validation set at an approximate 8∶2 ratio. In the NBI modality, 980 images from 171 patients were used for training, 235 images from 61 patients were used for validation. In the NF-NBI modality, 1 273 images from 128 patients were used for training, and 373 images from 35 patients were used for validation. This study trained a total of six convolutional neural network (CNN) models: two independent CNN1 models, two independent CNN2 models, and two fused CNN3 models. Using expert-delineated EGC boundaries based on post-ESD pathological findings as the gold standard, the intersection over union (IOU) value of the CNN3 models was compared against junior (<5 years experience), mid-level (5-10 years), and senior (>10 years) endoscopists.Results:In NBI validation set, the IOU value of CNN3 model was significantly higher than that of junior (0.732 VS 0.489, Z=11.528, P<0.001) and mid-level endoscopists (0.732 VS 0.521, Z=11.184, P<0.001). However, no significant difference was observed between CNN3 model and senior endoscopists (0.732 VS 0.739, Z=0.593, P=0.554). Similarly, in NF-NBI validation set, CNN3 model outperformed junior (0.757 VS 0.537, Z=15.944, P<0.001) and mid-level endoscopists (0.757 VS 0.597, Z=9.722, P<0.001), while matching senior endoscopists (0.757 VS 0.769, Z=0.854, P=0.394). Conclusion:The fused CNN3 model achieves senior expert-level accuracy in delineating EGC boundaries in both NBI and NF-NBI images, demonstrating potential to assist less-experienced endoscopists in precise identification of EGC boundaries.

Objective:To develop and validate artificial intelligence (AI) models based on deep learning for precise boundary identification of early gastric cancer (EGC) in narrow-band imaging (NBI) and near focus narrow-band imaging (NF-NBI) endoscopic images.Methods:Endoscopic submucosal dissection (ESD) images from 282 patients diagnosed as having EGC by postoperative pathology at the Department of Gastroenterology, the First Affiliated Hospital of Soochow University were retrospectively collected from February 2016 to June 2024. The images were randomly divided into the training set and the validation set at an approximate 8∶2 ratio. In the NBI modality, 980 images from 171 patients were used for training, 235 images from 61 patients were used for validation. In the NF-NBI modality, 1 273 images from 128 patients were used for training, and 373 images from 35 patients were used for validation. This study trained a total of six convolutional neural network (CNN) models: two independent CNN1 models, two independent CNN2 models, and two fused CNN3 models. Using expert-delineated EGC boundaries based on post-ESD pathological findings as the gold standard, the intersection over union (IOU) value of the CNN3 models was compared against junior (<5 years experience), mid-level (5-10 years), and senior (>10 years) endoscopists.Results:In NBI validation set, the IOU value of CNN3 model was significantly higher than that of junior (0.732 VS 0.489, Z=11.528, P<0.001) and mid-level endoscopists (0.732 VS 0.521, Z=11.184, P<0.001). However, no significant difference was observed between CNN3 model and senior endoscopists (0.732 VS 0.739, Z=0.593, P=0.554). Similarly, in NF-NBI validation set, CNN3 model outperformed junior (0.757 VS 0.537, Z=15.944, P<0.001) and mid-level endoscopists (0.757 VS 0.597, Z=9.722, P<0.001), while matching senior endoscopists (0.757 VS 0.769, Z=0.854, P=0.394). Conclusion:The fused CNN3 model achieves senior expert-level accuracy in delineating EGC boundaries in both NBI and NF-NBI images, demonstrating potential to assist less-experienced endoscopists in precise identification of EGC boundaries.